A randomized trial of Adapted versus Standard versions the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC) implemented via facilitation and delivered by community mental health providers using train-the-trainer

A randomized trial of Adapted versus Standard versions the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC) implemented via facilitation and delivered by community mental health providers using train-the-trainer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A randomized trial of Adapted versus Standard versions the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC) implemented via facilitation and delivered by community mental health providers using train-the-trainer Allison Harvey, Emma R. Agnew, Rafael Esteva Hache, Catherine A. Callaway, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6414484/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Nov, 2025 Read the published version in Implementation Science → Version 1 posted 5 You are reading this latest preprint version Abstract Background. Grounded in the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, we investigated the Train-the-Trainer (TTT) to expand access to evidence-based psychological treatments (EBPTs) in community mental health centers (CMHCs), focusing on the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC). Methods. Eight Californian counties were cluster-randomized to Standard TSC or an adapted version designed to improve the “fit” of TSC to CMHCs. University-based trainers trained CMHC providers ("Generation 1 providers") in either Adapted or Standard TSC. These trained providers were then trained to become local CMHC trainers (“Generation 1 trainers”), who then trained a new cohort of providers (“Generation 2 providers”) in TSC. Within each county, patients diagnosed with serious mental illness (SMI) were randomized to receive either immediate TSC or usual care and delayed treatment with TSC (UC-DT) from the Generation 2 providers (“Generation 2 patients”). This study focused on 53 Generation 2 providers (Adapted TSC = 47; Standard TSC = 6), and 143 Generation 2 patients (Adapted TSC = 127; Standard TSC = 16) (the larger Adapted sample was driven by recruitment, perhaps reflecting preference for the “fitted” approach). Patient assessments were conducted pre-treatment, post-treatment, and six-month follow-up (6FU). Provider assessments occurred after completing TSC training and post-treatment for each patient treated. Results. Combining Adapted and Standard, TSC was associated with improvements for Generation 2 patients from pre- to post-treatment in sleep disturbance ( p < 0.001, d = -0.90), sleep-related impairment ( p = 0.001, d = -0.69), psychiatric symptoms ( p = 0.002, d = -0.48), and functional impairment ( p = 0.002, d = -0.54), relative to UC-DT. The effects of sleep disturbance and impairment on the relationship between treatment condition (TSC vs. UC-DT) and psychiatric symptoms and functional impairment were significant. Higher provider perception of TSC fit predicted improvements in selected patient outcomes. Conclusion. TSC can be delivered by CMHC providers trained by local CMHC trainers with strong outcomes. These data contribute to the dearth of evidence for TTT collected from locally trained providers and from patients treated by local CMHC trainers. Trial registration: Clinicaltrials.gov identifier: NCT05805657. Registered on March 10, 2023. https://clinicaltrials.gov/ct2/show/NCT05805657 community mental health train-the-trainer facilitation adaptation i-PARIHS mental illness sleep circadian insomnia transdiagnostic psychosis depression anxiety disorder bipolar disorder Figures Figure 1 Figure 2 Contributions to the Literature Train-the-Trainer holds promise for expanding access to evidence-based treatments Using external facilitation grounded in the i-PARIHS framework, external experts trained local CMHC trainers to train other CMHC providers CMHC providers trained by the local CMHC trainers effectively delivered a transdiagnostic sleep treatment The sleep treatment improved outcomes for CMHC patients with serious mental illness Providers rated the sleep treatment as a strong fit for the CMHC context. Higher provider ratings of fit were associated with better patient outcomes Background According to the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework ( 1 ), the successful implementation of an evidence-based psychological treatment (EBPT) into practice is a function of the quality of evidence for the innovation, the recipients of the innovation, the characteristics of the context into which the innovation will be implemented, and the approach by which the innovation is integrated or facilitated into the context. The present study – focused on the implementation of the Transdiagnostic Intervention for Sleep and Circadian dysfunction (TSC) via Train-the-Trainer (TTT) – will be introduced through the lens of the i-PARIHS framework (also see Table 1 ). Innovations and Recipients EBPTs typically require specialized training for providers. Prior research has established that an effective approach to training providers in EBPTs includes a training workshop utilizing active learning strategies, a provider manual, and ongoing clinical supervision (e.g., 2, 3–6). However, barriers to the use of these multicomponent training initiatives in routine practice settings include insufficient time and funding, shortage of trainers and supervisors, and staff turnover (e.g., 7, 8). As a potential solution, the first “innovation” tested in the present study is the Train-the-Trainer (TTT) which involved the external university-based “expert trainers” training an initial cohort of providers in an EBPT. These providers are referred to as “Generation 1.” These providers were offered additional training on how to train others in the EBPT and became “local” trainers. These local CMHC trainers then trained the next cohort of providers within their organization, referred to as “Generation 2.” Although relatively few studies have been conducted on TTT for EBPTs, the existing research has been encouraging ( 7 , 9 , 10 ). At the provider-level, prior studies show no difference between generations on select outcomes, such as training effectiveness ( 11 ), provider competence ( 12 – 15 ) and fidelity ( 16 ). However, there is also evidence of poorer outcomes in Generation 2 relative to expert-led trainings in the domains of provider skill acquisition ( 12 , 17 ), quality case materials ( 13 ), provider knowledge about the EBT ( 7 ) and provider satisfaction with the training ( 7 ). Less research has measured TTT patient-level outcomes and relatively few clinical populations and contexts have been investigated ( 13 , 18 , 19 ). Furthermore, the existing research is qualified by small samples and methodological limitations ( 3 , 4 ). The present study aims to help fill these gaps by focusing on two groups of “recipients” (per i-PARIHS). The main focus is on the patient recipients who received the EBPT from Generation 2 providers. This group will be referred to as “Generation 2 patients.” An additional focus is on the Generation 2 providers. The second “innovation” tested was the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC) 1 (20). TSC is an EBPT that aims to improve six dimensions of sleep health ( 21 ) and targets sleep and circadian dysfunction, which is a common transdiagnostic contributor to serious mental illness (SMI) ( 22 ). In the present study, the patient recipients were SMI patients who has sleep and/or circadian problems. They were randomized to either Standard or Adapted TSC. Standard TSC was developed within a university setting. As described in the protocol papers ( 23 , 24 ), Adapted TSC was developed to address the concern that Standard TSC may not “fit” a routine practice setting. Thus, Adapted TSC was customized to fit the context for this study using theory, data, end-user input, consideration of TSC’s theoretical underpinnings and mechanisms of action and treatment strategies that addressed the key mechanisms. Context The context for this study (as per i-PARIHS) was community mental health centers (CMHCs) which, in the United States, are a large provider of affordable mental health services for people who are low-income and diverse with respect to demographic and clinical presentations. CMHC providers often have insufficient time and resources, carry a heavy caseload, and the patients they serve experience high rates of comorbidity and complexity ( 25 , 26 ). It can be difficult for CMHC providers to receive training and supervision in EBPTs ( 27 ), because of the cost associated with training and supervision. Before broadly recommending TTT to CMHCs and similar services, TTT must first be tested to assess its effectiveness for training providers in EBPTs within CMHCs. Facilitation Facilitation was chosen as the implementation strategy to support TTT due to its strong evidence base (e.g., 28, 29, 30). In this study, each CMHC received direct support from the lead facilitator, a licensed clinical social worker (ERA) with expertise in community mental health and sleep treatment, along with a team of trained facilitators employed by the research team. The facilitation team was overseen by the Principal Investigator (PI; AGH) and received periodic guidance from a Replicating Effective Programs and facilitation expert (AMK). Facilitation activities were also informed by the Veterans Affairs facilitation manual ( 31 ) and Harvey and Kitson’s ( 32 ) Facilitation Guide. Additionally, the lead facilitator (ERA) and postdoctoral scholar (LDS) completed the Behavioral Health Veterans Affairs Quality Enhancement Research Initiative Implementation (BH QUERI) Facilitation Training and ERA regularly attended BH QUERI’S monthly drop-in consultation group. The Present Study This paper describes Phase 2 of a three-part hybrid type 2 effectiveness-implementation study. As described in the protocol paper ( 23 ), this study builds upon the infrastructure of Phase 1, the Implementation Phase ( 24 , 33 ). During the Implementation Phase, sites were cluster-randomized by county to Adapted or Standard TSC with 1:1 allocation. External expert trainers trained an initial cohort of providers (i.e., Generation 1 providers) in TSC. Then, within each county, patients were randomized to receive immediate TSC or usual care and delayed treatment with TSC (UC-DT) from Generation 1 providers. In Phase 1, TSC (combining Adapted and Standard) was associated with improvement from pre- to post-treatment relative to UC-DT. However, Adapted versus Standard TSC did not differ on provider ratings of fit and better fit did not mediate the relation between TSC condition and patient outcome ( 33 ). The first aim of Phase 2 was to assess the effectiveness of TSC, compared to UC-DT, for Generation 2 patients who were treated by Generation 2 providers. We hypothesized that compared to UC-DT, TSC (combining Adapted and Standard) would be associated with larger reductions in the primary patient outcome of sleep disturbance, and the secondary patient outcomes of sleep-related impairment, sleep health, functional impairment, and psychiatric symptoms. To assess sleep and circadian problems as a mediator of the effects of TSC on patient outcomes, we further hypothesized that TSC’s benefits for functional impairment and psychiatric symptoms would be mediated by improvements in sleep and circadian problems. The second aim was to assess the effects of TSC treatment condition (Adapted vs. Standard) on primary and secondary outcomes in Generation 2. We hypothesized that Adapted TSC, relative to Standard TSC, would be associated with greater improvements from pre- to post-treatment. The third aim was to examine whether provider ratings of the perceived fit of TSC at post-treatment predicts change in patient outcomes at post-treatment. We hypothesized that greater provider perceived fit of TSC at post-treatment would be associated with improvements in patient outcomes at post-treatment, adjusting for perceived fit at post-training and outcome at pre-treatment. Exploratory analyses focused on ( 1 ) comparing Adapted and Standard TSC on patient perceptions of credibility/improvement and select PhenX Toolkit outcomes measuring suicidal ideation/behaviors and substance use; and ( 2 ) determining whether treatment effects of TSC versus UC-DT are moderated by risk factors. Aim 1 and the exploratory analyses were pre-specified ( 23 ). However, due to a small sample size for provider variables in the Standard condition, pre-specified Aims 2 and 3 could not yield reliable estimates. These results are reported in Additional File 1, see Supplement Tables 1 and 2 and have been replaced with revised Aims 2 and 3 that reflect the original intent and align with sample size constraints. The pre-treatment intent-to-treat sample sizes for the original pre-specified Aims and those reported in the final analyses are presented in Supplement Table 3 , Additional File 1. Exploratory Aim 1 from the protocol paper is not included here, as it will be part of a forthcoming report comparing Generations 1 and 2 on patient outcomes. Method Participants This study focused on 53 Generation 2 providers (Adapted TSC = 47; Standard TSC = 6), and 143 Generation 2 patients (Adapted TSC = 127; Standard TSC = 16). Participants were recruited from CMHCs and consisted of Generation 2 providers who had been trained by local CMHC trainers and Generation 2 patients. Participants were masked to condition (Adapted vs. Standard TSC), but not patient treatment allocation (immediate vs. delayed). All CMHC sites from the Implementation Phase were invited to participate in the TTT Phase. The inclusion criteria for selecting the CMHC sites for the Implementation Phase were: 1) provision of publicly funded adult mental health outpatient services and 2) support from CMHC leadership. CMHC sites in the following eight counties in California, USA participated: Alameda, Contra Costa, Kings, Monterey, Placer, Santa Cruz, Solano, and Santa Clara. There were 29 CMHC trainers (Adapted TSC = 20; Standard TSC = 9), 53 CMHC providers (Adapted TSC = 47; Standard TSC = 6) and 143 CMHC patients. Of the patients, 78 were randomized to receive TSC immediately (Adapted TSC = 70; Standard TSC = 8) and 65 were randomized to UC-DT (Adapted TSC = 57; Standard TSC = 8). The larger number of providers and patients in Adapted TSC was driven by stronger recruitment in the counties cluster randomized to this condition and perhaps also provider and patient preference for the shorter, “fitted” approach, as compared to Standard TSC. The inclusion criteria for local CMHC trainers were: 1) employed in participating CMHCs; 2) completed a Generation 1 TSC training (i.e., led by UC Berkeley expert trainers); and 3) volunteered to participate and formally consent to participate. CMHCs determined eligibility for Generation 2 providers (e.g., case managers, nurses, psychiatrists, training department staff), because this mirrors their real-world practice of determining who acquires additional training. For some CMHCs, this involved mandating TSC training for all untrained staff, whereas in others, leadership advertised the opportunity and allowed anyone who was interested to register. The other inclusion criteria for Generation 2 providers were: 1) employed or able to deliver patient-facing services to patients within the CMHC; 2) interested in learning and delivering TSC; and 3) voluntarily consented to participate. The inclusion criteria for patients were: 1) aged 18 years and older; 2) met criteria for an SMI per self-report and confirmed by referring provider or administration of the Mini International Neuropsychiatric Interview (DSM-5, Version 7.0.0) by a licensed clinical social worker on the research team; 3) exhibited a sleep or circadian disturbance as determined by endorsing 4 (quite a bit) or 5 (very much), or the equivalent for reverse scored items, on one or more items on the PROMIS-Sleep Disturbance ( 34 , 35 ); 4) guaranteed place to sleep for at least two months that is not a shelter; 5) receiving the standard of care for the SMI and consented to regular communications between the research team and provider; and 6) consented to access their medical record and to participate in the study. Patients were excluded if they met any of the following criteria: 1) presence of an active and progressive physical illness or neurological degenerative disease that was directly related to the onset and course of the sleep and circadian problems, or that made participation in the study unfeasible, as assessed by the Checklist of Medical Conditions and Symptoms on the Duke Structured Interview for Sleep Disorders ( 36 ) and clinical interview; 2) presence of substance abuse/dependence only if it made participation in the study unfeasible; 3) current active intent or plan to commit suicide (those with suicidal ideation are eligible) only if it made participation in the study unfeasible, or homicide risk; 4) night shift work for more than two nights per week in the past three months (i.e., regularly scheduled work from 12 a.m. – 6 a.m.); or 5) pregnant or breastfeeding. Interventions Two variations of TSC were tested: Adapted TSC and Standard TSC. Both were delivered alongside the usual care offered by each CMHC. The control condition was usual care followed by delayed treatment (UC-DT). See Additional File 2 for more detailed description of all conditions. Standard TSC CMHC providers were trained to deliver Standard TSC across eight 50-minute, weekly sessions ( 20 ). It was comprised of 4 cross-cutting modules featured in every session, 4 core modules, and 7 optional modules, used based on clinical presentation, treatment goals, and provider case conceptualization. Training for the Standard TSC condition consisted of a 1-day workshop (i.e., 6–8 hours) or two, 3-hour training blocks, based on CMHC preference. Adapted TSC We grounded the process for adapting TSC in theory, data, and end-user input. Adapted TSC was delivered by CMHC providers across four, 20-minute, weekly sessions (see Additional File 2 for description). Treatment consisted of the same four cross-cutting modules and three of the four core modules as Standard TSC along with one optional module focused on reducing sleep-related worry. Training for the Adapted TSC condition consisted of four, 1-hour workshops or two, 2-hour workshops, based on CMHC preferences. Usual Care and Delayed Treatment with TSC (UC-DT) In UC-DT, patients began with usual care for four or eight weeks, depending on whether their CMHC was randomized to Adapted TSC or Standard TSC, respectively. After the delay, they received Adapted or Standard TSC, similarly based on the condition to which their CMHC had been randomized. Usual care in CMHCs involves working with a service provider—such as a psychologist, case manager, occupational therapist, psychiatrist, or nurse practitioner—who delivers mental health support within their professional scope. Measures In addition to the measures below, a sociodemographics form was completed by providers and patients. Only measures analyzed for the aims of this paper are briefly reported below. See Additional File 3 for further details on each measure. Generation 2 Patients Sleep Disturbance. The 8-item PROMIS-Sleep Disturbance (PROMIS-SD) assessed disruption to sleep (e.g., trouble staying asleep) over the past seven days and has demonstrated acceptable reliability and validity ( 34 , 35 ). This was the primary outcome for the patient-level analyses. Sleep-Related Impairment. The 8-item PROMIS-Sleep Related Impairment (PROMIS-SRI) assessed daytime impairment related to sleep problems using the same scale as the PROMIS-SD. Functional Impairment. Functional impairment was assessed via the Sheehan Disability Scale (SDS) ( 37 ) which has demonstrated good reliability and validity ( 37 ). Overall Sleep Health . The Sleep Health Composite measured overall sleep health for the complexity of sleep and circadian problems experienced by people diagnosed with SMI and that are covered by TSC ( 38 ). The initial validity of this measure has been established ( 38 ). Psychiatric Symptoms . The DSM-5 Cross-Cutting Measure assessed psychiatric symptoms across 13 mental health domains (e.g., depression, anger, mania, psychosis, substance use). This measure has demonstrated good test-retest reliability and clinical utility ( 39 , 40 ). PhenX Toolkit. ( 41 ). Two subscales from the screening version of the Columbia-Suicide Severity Rating Scale—Severity of Suicidal Ideation and Suicidal Behavior—were administered. The PhenX ‘Alcohol – 30-Day Quantity and Frequency’, ‘Tobacco – 30 Day Quantity and Frequency’, ‘Substances – 30-Day Frequency’, and ‘Supplemental Beverage Questionnaire’ were used to assess alcohol, tobacco, psychoactive substance, and caffeine consumption over the past 30 days. Credibility and Perceived Improvement . At the post-treatment assessment, perceptions of TSC’s credibility and symptom improvement were assessed by four questions adapted from the Credibility/Expectancy Questionnaire (CEQ) (Devilly & Borkovec, 2000). Generation 2 Providers Acceptability. Providers rated the acceptability of TSC via the Acceptability of Intervention Measure (AIM) (Weiner et al., 2017) which has satisfactory validity, internal reliability, test-retest reliability, and sensitivity to change ( 42 ). This was the primary outcome for the provider-level analyses. Appropriateness and Feasibility . Providers rated the appropriateness and feasibility of TSC via the Feasibility of Intervention Measure (FIM) and Intervention Appropriateness Measure (IAM) ( 42 ). Number of TSC Sessions. The number of sessions delivered to each enrolled patient by each provider was counted. Occupation . Providers were asked to report their current position, professional degree, and work history, including their caseload, theoretical orientation, licensure status, and previous training in sleep treatment. Procedure CMHCs and patients were randomized through a computerized randomization sequence. We did not stratify during randomization at the CMHC level. When randomizing patients, we stratified for the presence of psychosis or not (current), presence of substance use or not (current) and age (≥ 50 or not), as there is evidence these variables can impact sleep or treatment outcome ( 43 – 45 ). Only the facilitators, assessors, and research team (i.e., not CMHCs, local trainers, providers, or patients) were privy to which CMHCs and patients were allocated to which TSC treatment condition (Adapted versus Standard). CMHC providers, local CMHC trainers, and patients knew whether their patients had been randomized to receive the immediate or delayed treatment. A facilitator informed the local trainer once a patient could start having sessions, who then informed the provider. In the immediate condition, the provider is asked to begin sessions as soon as possible. In the delayed condition, the provider was asked to wait until after the patient had completed the post-delay assessment (i.e., approximately four weeks in the Adapted condition or eight weeks in the Standard condition). Generation 2 provider and patient assessments were conducted by experienced assessors who also handled the consent process to reduce participant burden. As they needed to share study details (e.g., number of assessments, treatment sessions), assessors were unmasked at pre-treatment. Efforts were made to keep assessors masked at post-treatment and 6FU. Assessors received thorough training and ongoing supervision to ensure survey integrity and minimize bias. The UC Berkeley facilitation team transitioned CMHC sites from the Implementation Phase to the TTT Phase on a rolling basis. Each site’s readiness for TTT was assessed by the level of provider engagement, the number of patients who had completed sleep treatment, and the supportiveness of leadership. The first site was transitioned to TTT in December 2020, and all sites were transitioned by December 2022. Facilitator’s primary activities in the TTT Phase are summarized in Table 1 . Local CMHC trainers led Generation 2 trainings independent of the expert trainer. Due to the COVID-19 pandemic and in accordance with local preferences and requirements, all Generation 2 trainings were delivered over Zoom. Generation 2 trainers had varying degrees of access to and familiarity with Zoom and little time to master it. Thus, for the first training led by each local trainer, a UC Berkeley facilitator attended the meeting to provide support with Zoom technology. The facilitator only answered content-related questions if requested by the local trainer. The UC Berkeley facilitator had some content-knowledge regarding TSC, but they were not trainers. After the first training, facilitator attendance was offered but not required. Following conducting their first training, local CMHC trainers began holding drop-in supervision hours for Generation 2 providers. The expert trainer continued to hold drop-in consultation hours, open to Generation 1 providers. Also, the expert trainer held individual consultation for the local CMHC trainers to support their transition to a supervision role. Overall, we viewed the supports detailed above to be deviations from the ideal TTT structure yet crucial within the CMHC context and particularly during the pandemic. Local CMHC Trainers Trainers did not complete assessment batteries for the TTT Phase and are not a focus of this report. Generation 2 Providers Provider assessments were completed after the provider completed TSC training (i.e., post-training), as well as at post-treatment for each patient they treated. Generation 2 Patients Patient assessments in the immediate TSC treatment conditions were completed at pre-treatment, post-treatment, and six months after treatment (6FU). Patient assessments in the UC-DT condition were completed at pre-treatment and four or eight weeks after pre-treatment (i.e., at the end of usual care and before delayed treatment with TSC referred to as post UC-DT), depending on whether their county has been randomized to Adapted or Standard TSC, respectively. Patients did not complete a 6FU assessment after the delay portion of the UC-DT. This was a compromise made with CMHC partners, so that patients would have minimal wait time before receiving treatment. As a result, patients started delayed treatment with TSC after the post UC-DT assessment. Following delayed TSC treatment, patients completed the same assessments as those in the immediate TSC condition i.e., post-treatment and 6FU. Trial Registration, Data Transparency and Openness All research materials, data, and analysis code are available from the authors upon request. This study was preregistered on clinicaltrials.gov (identifier: NCT05805657), a protocol paper was published ( 23 ) and the study received approval from the Committee for the Protection of Human Subjects at the University of California, Berkeley. Raw data for most outcomes reported here have been uploaded into the National Data Archive. An update was made to clinicaltrials.gov to clarify that, for the primary outcome measures, assessments at mid-treatment were not of primary interest. This error was rectified by moving the mid-treatment assessment to “Other outcome measures”. Analyses Analyses were conducted with Stata Version 16.1. Percent of missing data for each aim are presented in Supplement Tables 4 – 7 , Additional File 1. Multilevel Models (Aims 1 & 2 and Exploratory Aims 1 & 2). Multilevel models (MLMs) were used to account for multiple observations nested within patient ( 46 ). All MLMs compared pre-treatment to post-treatment and, for level 1, included a dummy-coded time indicator as the predictor (1 = post-treatment, pre-treatment as the reference). Exploratory Aim 1 also compared pre-treatment to 6FU follow-up and included an additional time indicator accordingly. For all MLMs, the level 2 equation included dummy-coded treatment condition ( Aim 1 and Exploratory Aim 2 : 1 = immediate TSC, with UC-DT as the reference; Aim 2 and Exploratory Aim 1 : 1 = Adapted TSC, with Standard as reference) and treatment-by-time interaction terms, which were the parameters of interest. Additionally, Exploratory Aim 2 included three-way interactions between time, treatment, and the following pre-specified moderators: sex (dummy coded: 0 = male, 1 = female), age (dummy coded: 0 = < 50, 1 = ≥ 50), and continuous baseline variables of PROMIS-SD, PROMIS-SRI, SDS, and DSM-5 Cross-Cutting. Significant interactions were interpreted using graphs. We list the outcomes included in each MLM. For Aim 1 and 2 MLMs, the outcomes were PROMIS-SD, PROMIS-SRI, Sleep Health Composite, DSM-5 Cross-Cutting, and SDS. For Exploratory Aim 1, the MLM outcomes were severity of suicidal ideation, average number of caffeinated drinks per day, and number of days the patient consumed alcohol in the past 30 days. For Exploratory Aim 2, the outcomes mirrored Aims 1 and 2. Most outcomes were continuous, except for the following binary outcomes tested in Exploratory Aim 1: suicidal thoughts and behaviors and substance use. For these outcomes, multilevel logistic regression was used. However, because few participants endorsed these items, the models would not converge. Instead, the frequencies of patients’ endorsement of each item are presented in Additional File 1, Supplement Table 8 . Linear Regression Models (Aim 3 and Exploratory Aim 1). For Aim 3, residualized change models ( 47 ) were conducted via multiple linear regression to evaluate whether perceived fit at post-treatment predicted patient outcomes at post-treatment, adjusting for pre-treatment levels. The predictor was AIM, FIM, or IAM at post-treatment, and the outcomes were PROMIS-SD, PROMIS-SRI, Sleep Health Composite, DSM-5 Cross-Cutting, and SDS at post-treatment. For Exploratory Aim 1, linear regression models were used to test the effects of TSC treatment condition on credibility and perceived improvement at post-treatment. The predictor was dummy-coded TSC treatment condition (1 = Adapted TSC, with Standard as reference) and the outcomes were credibility, expectancy, and total CEQ. Structural Equation Modeling (SEM) (Aim 1). For the mediation models in Aim 1, SEM was used. The predictor was condition (immediate TSC vs. UC-DT), the mediator was PROMIS-SD or PROMIS-SRI at post-treatment, and the outcomes were DSM-5 Cross-Cutting and SDS at post-treatment. For all SEMs, the parameter of interest was the indirect effect. Results See Fig. 1 for the CONSORT diagram for patients. Attrition rates were significantly higher in Standard than Adapted TSC during the treatment phase (56.3% in Standard; 26.8% in Adapted; χ 2 = 4.55, df = 1, p = 0.03), but not significantly different prior to Session 1 (0% in Standard; 13.4% in Adapted; χ 2 = 1.32, df = 1, p = 0.25), or at 6FU (12.5% in Standard; 4.7% in Adapted; χ 2 = 0.49, df = 1, p = 0.49). See Fig. 2 for the CONSORT diagram for providers. Patient and provider demographic variables at pre-treatment by TSC condition (Adapted vs. Standard) are presented in Table 2 and Table 3 , respectively. Further information on patient and provider differences by TSC condition are reported in Additional File 1. Additional File 1, Supplement Table 9 presents the patient demographics by immediate TSC vs. UC-DT condition. Aim 1 See Tables 4 and 5 . TSC, relative to UC-DT, was associated with significant improvements from pre- to post-treatment in sleep disturbance, sleep-related impairment, psychiatric symptoms, and overall functional impairment. TSC, relative to UC-DT, was marginal ( p = 0.09) for the sleep health composite. Sleep disturbance (primary outcome) withstood the Benjamini-Hochberg correction. See Table 6 for SEM results. The indirect effects of treatment condition (TSC vs. UCT-DT) on psychiatric symptoms and overall functional impairment via sleep disturbance and sleep-related impairment were significant. Aim 2 See Tables 4 and 7 . There were no significant differences between Adapted and Standard TSC on changes from pre- to post-treatment on primary or secondary outcomes. Aim 3 See Tables 4 and 8 . Greater provider perceptions of acceptability (AIM) predicted improvements in patient sleep-related impairment at post-treatment. Greater feasibility (FIM) predicted improvements in patient sleep-related impairment and psychiatric symptoms at post-treatment. Greater FIM also marginally predicted improvements in sleep health composite and functional impairment. Greater appropriateness (IAM) predicted improvements in patient sleep-related impairment and psychiatric symptoms. Provider ratings on the AIM, FIM and IAM ranged from 4.39 to 5 on the 1 (completely disagree) to 5 (completely agree) scale. Exploratory Aims See Additional File 1, Supplement Tables 10 and 11. For Exploratory Aim 1, there were no significant differences between Adapted and Standard TSC on suicidal ideation severity, average daily caffeine use, or past 30-day alcohol use (all p s > .10). There were no differences between Adapted versus Standard TSC on credibility, perceived improvement, or total CEQ (all p s > .10). At post-treatment, the mean of the credibility items was 7.39 ( SD = 1.45) on the 0 (not at all) to 9 (very) scale and mean perceived improvement was 56.13% ( SD = 30.63). For Exploratory Aim 2, see Additional File 1, Supplement Table 12. Baseline sleep related-impairment marginally moderated the effects of treatment (UC-DT versus immediate TSC) on functional impairment from pre- to post-treatment. This effect was such that at the lowest level of baseline sleep-related impairment there was no difference between UC-DT vs. immediate TSC conditions ( p = 0.63), but at the highest level of baseline sleep-related impairment there were greater improvements in functional impairment for the immediate relative to the delayed group ( p = 0.01). None of the other planned demographics or baseline clinical symptoms moderated the effects of treatment on patient outcomes from pre- to post-treatment (all p s > 0.10). Discussion We sought to determine if TTT is an effective approach to delivering TSC in CMHCs. Table 1 and this discussion frames the findings through i-PARIHS. There are several overarching issues that emerged with regard to the recipients. The patient sample was diverse, largely unpartnered, low income, unemployed, and living with family. Most providers were female social workers using client-centered approaches, licensed, and managing high caseloads (avg. 33 patients). Providers rated both Adapted and Standard TSC as highly appropriate, acceptable, and feasible which suggests that Generation 2 providers recognized the value and practicality of both approaches. Although Standard providers were trained for 8 sessions and Adapted for 4, no significant difference was found in sessions completed. This may reflect feasibility issues in CMHCs and the higher attrition in the Standard group. We first focus on outcomes for the patient-level recipients of the innovations delivered in this study. Consistent with our hypothesis, Generation 2 patients treated with TSC reported larger reductions in sleep disturbance, sleep-related impairment, functional impairment, and psychiatric symptoms, relative to UC-DT. The same pattern of findings was evident for the sleep health composite, except the difference between TSC and UC-DT was marginally significant. These findings are important for at least four reasons. First, they extend prior research conducted with university-based providers ( 48 ) and CMHC providers trained by university-based trainers ( 33 ) and add to the growing support for both TSC ( 49 – 52 ) and the Sleep Health Framework ( 21 , 38 ). Second, they add to the evidence for TTT and to the handful of reports on Generation 2 patient-level outcomes from TTT ( 13 , 18 , 19 ). Third, they demonstrate the feasibility and effectiveness of TSC and TTT in CMHCs, with potential for broader application in under-resourced settings. Fourth, these results add to the evidence for external facilitation as a successful implementation strategy (e.g., 28, 29, 30) and expand knowledge by demonstrating the success of external facilitators in establishing a TTT structure. There was support for the hypothesis that TSC’s benefits for functional impairment and psychiatric symptoms would be mediated by improvements in sleep, as assessed by the PROMIS-SD and PROMIS-SRI. This replicates and extends the parallel finding for the Implementation Phase of this study in which UC Berkeley experts served as the trainers ( 33 ) as well as prior research showing that sleep treatment improves symptoms of comorbid mental health conditions (e.g., 53, 54, 55). Within the i-PARIHS framework, these results suggest that facilitation was effective in supporting trainers to train CMHC providers to deliver the innovation (TSC) within the CMHC context, resulting in improved outcomes for the SMI recipients. For the second aim, contrary to the hypothesis, there were no significant differences between Adapted and Standard TSC. This result aligns with the parallel findings from the Implementation Phase and might be explained by the relative advantages of each approach (see 33). Of note, Adapted and Standard TSC differed in the number of trainers (Adapted TSC = 20; Standard TSC = 9) and trainees (Adapted TSC = 47; Standard TSC = 6). Further, there was more attrition among patients who participated in Standard (56%) than Adapted (27%). For the third aim, we focused on the Generation 2 provider recipients who were trained to deliver TSC via TTT. The hypothesis tested was that greater provider perceived fit at post-treatment would be associated improvements in patient outcomes at post-treatment. Most of the results were in the predicted direction (see Table 8 ) with several comparisons reaching statistical significance, and 14 of the 15 comparisons showing small to large effect sizes. Together, these findings add to the growing evidence that underscores the importance of the fit between the treatment and the context ( 56 – 58 ) and extends these findings by showing the importance of fit for better patient outcomes. Consistent with i-PARIHS, these results suggest that the use of facilitation, as well as TTT, was effective in supporting the second group of recipients in the study—CMHC providers—to deliver the innovation (TSC) despite the many challenges faced at the local, organizational and outer context of CMHCs (see Table 1 for details). The only significant moderator of treatment effects was that TSC’s effect (compared to UC-DT) on improving functional impairment was particularly strong for those people who had greater sleep-related daytime impairment at baseline. This pattern of findings has been observed in the depression literature ( 59 ) and may reflect a floor effect for people with a lower level of symptoms at baseline. There are several limitations. First, we did not have the resources to collect data on specific aspects of the context that are important within i-PARIHS such as the outer context nor the dynamic relationships between the micro, meso and macro layers of context ( 1 ). Second, likely due to county-level cluster randomization and demographic differences, there were baseline differences between the Adapted and Standard TSC groups. Third, although we trained providers to deliver 8 sessions in Standard TSC, they administered an average of 3.69 sessions when considering the full sample. When focusing only on those who completed the full course of TSC, the number of TSC sessions received by patients was closer to the ideal with an average of 6.33 sessions delivered by Standard TSC providers. Perhaps delivering eight sessions in CMHCs may be unrealistic. Fourth, the study design did not allow for a comparison between TSC and UC-DT at the 6-month follow-up. Also, while mid-treatment data were intended for mediation analysis, difficulties in data collection led to a smaller sample size, so post-treatment data were used. Finally, it was not always possible to determine whether drop-outs were due to patient disengagement or provider limitations, underscoring the need for clearer tracking of drop-out causes in future studies. Conclusion Within the infrastructure of the Implementation Phase ( 24 , 33 ) of this three-part hybrid type 2 effectiveness-implementation study and with the support of facilitation, TTT was effective. Returning to the i-PARIHS framework, the results indicate that TTT can be used to train CMHC providers to deliver TSC via facilitation that is delivered by university-based external facilitators. These findings add to the growing evidence for the use of multi-component implementation strategies and external facilitation as effective approaches to promoting health-care innovations like TTT and TSC into routine practice (e.g., 28, 29, 30). These results also contribute to the dearth of evidence collected from Generation 2 providers who had been trained by local CMHC trainers and Generation 2 patients ( 13 , 18 , 60 ) and add support to using a briefer version of TSC in under-resourced settings ( 33 ). Abbreviations 6FU - six months after treatment AIM - Acceptability of Intervention Measure ANCOVA - analysis of covariance CBT - cognitive behavioral therapy CEQ - Credibility/Expectancy Questionnaire CMHCs - community mental health centers DSM-5 - DSM-5 Cross-Cutting Measure EBPT - evidence-based psychological treatments FIM - Feasibility of Intervention Measure IAM - Intervention Appropriateness measure ICC - intraclass correlation coefficients MDES - minimum detectable effect sizes MLMs - multilevel models MP - mediated proportions PROMIS-SD - PROMIS-Sleep Disturbance PROMIS-SRI - PROMIS-Sleep Related Impairment SDS - Sheehan Disability Scale SE - robust standard errors SEMs - structural equation models SHC - Sleep Health Composite SMI - serious mental illness SNAP - Supplemental Nutrition Assistance Program SSI/SSDI - Supplemental Social Security Income/Social Security Disability Insurance TSC – Transdiagnostic Intervention for Sleep and Circadian Problems TTT – Train-the-Trainer UC-DT - Usual Care followed by Delayed Treatment with TSC Declarations Acknowledgements We gratefully acknowledge all community partners, including the leadership, staff, and patients as well as Dr. Tanya Horwitz for data advice as well as the many assessors and research assistants who worked on the study. Funding This research is funded by the National Institute of Mental Health (R01MH120147; F32MH131284). The funder had no role in the design, collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit this manuscript for publication. Availability of data and materials Raw data for most outcomes reported herein has been uploaded into the NIMH National Data Archive. Authors’ contributions AGH led the conception and design of the study, acquired the funding, and drafted all sections of the paper except the data analysis and results sections. AMK, ERA, REH, JS, and AGH designed the implementation strategies. LDS, AM and EO led the data analysis and interpretation and wrote the data analysis and results sections of the paper. DJB, LD, and AMK were involved in the design of the study and acquiring funding. AGH, ERA, MD, JMS, REH and CAC were responsible for acquisition of data. All authors (i.e., AGH, ERA, REH, CAC, EOP, AM, JMS, MD, LD, AMK, DKB, ES, LDS) were involved in revising the manuscript. All authors (i.e., AGH, ERA, REH, CAC, EOP, AM, JMS, MD, LD, AMK, DKB, ES, LDS) read and approved the final manuscript. Ethics approval and consent to participate Approval to conduct the study was gained from the Committee for the Protection of Human Subjects at the University of California, Berkeley. Participants (providers and patients) were asked to provide written informed consent before participating in the study. Consent for publication Model consent forms are available upon request. Competing interests AGH, LDS, AMK, DJB, MD, LD and CC have received National Institutes of Health funding. AGH has received book royalties from Guilford Press and Oxford University Press. Over the past 3 years, DJB has served as a paid consultant to Sleep Number (not greater than $5000 per year). Consulting has focused on insomnia, measurement of sleep characteristics, and relationships between sleep and health outcomes. DJB is an author of questionnaires including the Pittsburgh Sleep Quality Index, Pittsburgh Sleep Quality Index Addendum for PTSD (PSQI-A), Brief Pittsburgh Sleep Quality Index (B-PSQI), Daytime Insomnia Symptoms Scale, Pittsburgh Sleep Diary, Insomnia Symptom Questionnaire, and RU_SATED (copyrights held by University of Pittsburgh). These instruments have been licensed to commercial entities for fees by the University of Pittsburgh. DJB receives a portion of the licensing fees, paid to him by the University of Pittsburgh. He is also co-author of the Consensus Sleep Diary (copyright held by Ryerson University), which is licensed to commercial entities for a fee by Ryerson University. DJB receives a portion of the licensing fees from the University of Pittsburgh through its agreement with Ryerson University. DJB has received grant/contract support from NIH, PCORI, AHRQ, VA and Sleep Number for research relating to sleep, insomnia, sleep interventions, and questionnaire development. References Harvey G, Kitson A. PARIHS revisited: from heuristic to integrated framework for the successful implementation of knowledge into practice. Implement Sci. 2015;11(1):1–13. Beidas RS, Kendall PC. Training therapists in evidence-based practice: a critical review of studies from a systems‐contextual perspective. Clin Psychol Sci Pract. 2010;17(1):1–30. Frank HE, Becker-Haimes EM, Kendall PC. Therapist training in evidence‐based interventions for mental health: A systematic review of training approaches and outcomes. Clin Psychol Sci Pract. 2020;27(3):e12330. Herschell AD, Kolko DJ, Baumann BL, Davis AC. The role of therapist training in the implementation of psychosocial treatments: A review and critique with recommendations. Clin Psychol Rev. 2010;30(4):448–66. Rakovshik SG, McManus F. Establishing evidence-based training in cognitive behavioral therapy: A review of current empirical findings and theoretical guidance. Clin Psychol Rev. 2010;30(5):496–516. Henrich D, Glombiewski JA, Scholten S. Systematic review of training in cognitive-behavioral therapy: Summarizing effects, costs and techniques. Clin Psychol Rev. 2023:102266. Brabson LA, Herschell AD, Snider MD, Jackson CB, Schaffner KF, Scudder AT, et al. Understanding the effectiveness of The Cascading Model to implement parent-child interaction therapy. J Behav Health Serv Res. 2021;48(3):427–45. Fairburn CG, Patel V. The global dissemination of psychological treatments: a road map for research and practice. Am J Psychiatry. 2014;171(5):495–8. Herschell AD, Kolko DJ, Scudder AT, Taber-Thomas SM, Schaffner KF, Hart JA et al. A Statewide Randomized Controlled Trial to Compare Three Models for Implementing Parent Child Interaction Therapy. J Clin Child Adolesc Psychol. 2022:1–17. Li T, McClelland MM, Tominey SL, Tracy A. Cost-Effectiveness Analyses on Various Models of The Red Light, Purple Light Self-Regulation Intervention for Young Children. Front Psychol. 2021;12:711578. Triplett NS, Sedlar G, Berliner L, Jungbluth N, Boyd M, Dorsey S. Evaluating a train-the-trainer approach for increasing EBP training capacity in community mental health. J Behav Health Serv Res. 2020;47(2):189–200. Martino S, Ball SA, Nich C, Canning-Ball M, Rounsaville BJ, Carroll KM. Teaching community program clinicians motivational interviewing using expert and train‐the‐trainer strategies. Addiction. 2011;106(2):428–41. Southam-Gerow MA, Daleiden EL, Chorpita BF, Bae C, Mitchell C, Faye M, et al. MAPping Los Angeles County: Taking an evidence-informed model of mental health care to scale. J Clin Child Adolesc Psychol. 2014;43(2):190–200. Wilfley DE, Agras WS, Fitzsimmons-Craft EE, Bohon C, Eichen DM, Welch RR, et al. Training models for implementing evidence-based psychological treatment: a cluster-randomized trial in college counseling centers. JAMA psychiatry. 2020;77(2):139–47. Fitzsimmons-Craft EE, Bohon C, Wilson GT, Jo B, Mondal S, Laing O, et al. Maintenance of Training Effects of Two Models for Implementing Evidence-Based Psychological Treatment. Psychiatric Serv. 2021;72(12):1451–4. Weisz JR, Ugueto AM, Herren J, Marchette LK, Bearman SK, Lee EH, et al. When the torch is passed, does the flame still burn? Testing a train the supervisor model for the Child STEPs treatment program. J Consult Clin Psychol. 2018;86(9):726. Demchak M, Browder DM. An evaluation of the pyramid model of staff training in group homes for adults with severe handicaps. Education and Training in Mental Retardation. 1990:150 – 63. Chamberlain P, Price J, Reid J, Landsverk J. Cascading implementation of a foster and kinship parent intervention. Child Welfare. 2008;87(5):27. Greif R, Becker CB, Hildebrandt T. Reducing eating disorder risk factors: A pilot effectiveness trial of a train-the‐trainer approach to dissemination and implementation. Int J Eat Disord. 2015;48(8):1122–31. Harvey AG, Buysse DJ. Treating Sleep Problems: A Transdiagnostic Approach. Guilford; 2017. Buysse DJ. Sleep health: can we define it? Does it matter? Sleep. 2014;37(1):9–17. Harvey AG. Treating sleep and circadian problems to promote mental health: perspectives on comorbidity, implementation science and behavior change. Sleep. 2022;45(4):zsac026. Callaway CA, Sarfan LD, Agnew ER, Dong L, Spencer JM, Hache RE, et al. The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 2: study protocol for a hybrid type 2 effectiveness-implementation cluster-randomized trial using train-the-trainer. Trials. 2023;24(1):503. Sarfan LD, Agnew ER, Diaz M, Dong L, Fisher K, Spencer JM, et al. The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 1: study protocol for a hybrid type 2 effectiveness-implementation cluster-randomized trial. Trials. 2023;24(1):1–18. Lee A, Gold R, Caskey R, Haider S, Schmidt T, Ott E, et al. Recruiting Community Health Centers for Implementation Research: Challenges, Implications, and Potential Solutions. New York: Mary Ann Liebert Inc.; 2024. pp. 113–6. Sarfan LD, Hilmoe HE, Gumport NB, Gasperetti CE, Harvey AG. Outcomes of Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) in a community setting: Unpacking comorbidity. Behav Res Ther. 2022;145:585–99. Rodríguez A, Southam-Gerow MA, O'Connor MK, Allin RB Jr. An analysis of stakeholder views on children's mental health services. J Clin Child Adolesc Psychol. 2014;43(6):862–76. Kauth MR, Sullivan G, Blevins D, Cully JA, Landes RD, Said Q, et al. Employing external facilitation to implement cognitive behavioral therapy in VA clinics: a pilot study. Implement Sci. 2010;5(1):75. Ritchie MJ, Kirchner JE, Parker LE, Curran GM, Fortney JC, Pitcock JA, et al. Evaluation of an implementation facilitation strategy for settings that experience significant implementation barriers. Implement Sci. 2015;10(1):A46. Kirchner JE, Ritchie MJ, Pitcock JA, Parker LE, Curran GM, Fortney JC. Outcomes of a partnered facilitation strategy to implement primary care–mental health. J Gen Intern Med. 2014;29(4):904–12. Ritchie MJ, Dollar KM, Miller CJ, Smith JL, Oliver KA, Kim B et al. Using implementation facilitation to improve healthcare [Internet]. Available from: https://wwwqueriresearchvagov/tools/implementation/Facilitation-Manualpdf . 2020. Harvey G, Kitson A. Implementing evidence-based practice in healthcare: a facilitation guide. Routledge; 2015. Harvey AG, Agnew ER, Esteva Hache R, Spencer JM, Diaz M, Patino EO et al. A randomized trial of Standard versus Adapted versions of the Transdiagnostic Intervention for Sleep and Circadian dysfunction implemented via facilitation and delivered by community mental health providers: Improving the fit of psychological treatments by adapting to context. Mauscript submitted for publication. in press. Yu L, Buysse DJ, Germain A, Moul D. Development of short forms from the PROMIS sleep disturbance and sleep-related impairment item banks. Behav sleep Med. 2012;10:6–24. Buysse DJ, Yu L, Moul DE, Germain A, Stover A, Dodds NE, et al. Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments. Sleep. 2010;33:781–92. Carney C, Edinger J, Olsen M, Stechuchak K, Krysta A, Wyatt J. Inter-rater reliability for insomnia diagnoses derived from the Duke Structured Interview for Sleep Disorders. Maryland: SLEEP 2008; Baltimore; 2008. Sheehan DV, Harnett-Sheehan K, Raj BA. The measurement of disability. Int Clin Psychopharmacol. 1996;11(suppl 3):89–95. Dong L, Martinez AJ, Buysse DJ, Harvey AG. A composite measure of sleep health predicts concurrent mental and physical health outcomes in adolescents prone to eveningness. Sleep health. 2019;5(2):166–74. Narrow WE, Clarke DE, Kuramoto SJ, Kraemer HC, Kupfer DJ, Greiner L, et al. DSM-5 field trials in the United States and Canada, Part III: development and reliability testing of a cross-cutting symptom assessment for DSM-5. Am J Psychiatry. 2013;170(1):71–82. Clarke DE, Kuhl EA. DSM-5 cross-cutting symptom measures: a step towards the future of psychiatric care? World Psychiatry. 2014;13(3):314–6. Hamilton CM, Strader LC, Pratt JG, Maiese D, Hendershot T, Kwok RK, et al. The PhenX Toolkit: get the most from your measures. Am J Epidemiol. 2011;174(3):253–60. Weiner BJ, Lewis CC, Stanick C, Powell BJ, Dorsey CN, Clary AS, et al. Psychometric assessment of three newly developed implementation outcome measures. Implement Sci. 2017;12(1):108. Harrow M, Grossman LS, Herbener ES, Davies EW. Ten-year outcome: patients with schizoaffective disorders, schizophrenia, affective disorders and mood-incongruent psychotic symptoms. Br J Psychiatry. 2000;177(5):421–6. Large M, Mullin K, Gupta P, Harris A, Nielssen O. Systematic meta-analysis of outcomes associated with psychosis and co-morbid substance use. Australian New Z J Psychiatry. 2014;48(5):418–32. Armstrong CC, Dong L, Harvey AG. Mediators and moderators of outcome from the Transdiagnostic Sleep and Circadian Intervention for adults with severe mental illness in a community setting. Behav Res Ther. 2022:104053. Rabe-Hesketh S, Skrondal A. Multilevel and longitudinal modeling using Stata. 3rd ed. Stata; 2011. Fu R, Holmer HK. Change score or follow-up score? Choice of mean difference estimates could impact meta-analysis conclusions. J Clin Epidemiol. 2016;76:108–17. Harvey AG, Dong L, Hein K, Yu S, Martinez A, Gumport N, et al. A randomized controlled trial of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) to improve serious mental illness outcomes in a community setting. J Consult Clin Psychol. 2021;89(6):537–44. Tighe CA, Phares A, Weiner DK, Beehler GP, VanSwearingen J, Harvey AG et al. Adapting and Evaluating the Feasibility and Acceptability of a Behavioral Intervention to Promote Sleep Health in Cardiac Rehabilitation. Behav sleep Med. 2024:1–16. Yau AY-Y, Ng K-Y, Lau W-Y, Poon C-Y, Yeung W-F, Chung K-F et al. A group-based transdiagnostic sleep and circadian treatment for major depressive disorder: A randomized controlled trial. J Consult Clin Psychol. 2024. Imes CC, Kline CE, Patel SR, Sereika SM, Buysse DJ, Harvey AG et al. An adapted transdiagnostic sleep and circadian intervention (TranS-C) for adults with excess weight and suboptimal sleep health: Pilot study results. Sleep Adv. 2024:zpae037. Harvey AG, Hein K, Dolsen MR, Dong L, Rabe-Hesketh S, Gumport NB, et al. Modifying the impact of eveningness chronotype (night-owls) in youth: a randomized controlled trial. J Am Acad Child Adolesc Psychiatry. 2018;57(10):742–54. Taylor DJ, Pruiksma KE. Cognitive and behavioural therapy for insomnia (CBT-I) in psychiatric populations: a systematic review. Int Rev psychiatry. 2014;26(2):205–13. Geiger-Brown JM, Rogers VE, Liu W, Ludeman EM, Downton KD, Diaz-Abad M. Cognitive behavioral therapy in persons with comorbid insomnia: a meta-analysis. Sleep Med Rev. 2015;23:54–67. Scott J, Kallestad H, Vedaa O, Sivertsen B, Etain B. Sleep disturbances and first onset of major mental disorders in adolescence and early adulthood: a systematic review and meta-analysis. Sleep Med Rev. 2021;57:101429. Durlak JA, DuPre EP. Implementation matters: A review of research on the influence of implementation on program outcomes and the factors affecting implementation. Am J Community Psychol. 2008;41(3–4):327–50. Barnett M, Brookman-Frazee L, Yu SH, Lind T, Lui J, Timmer S, et al. Train-to-sustain: Predictors of sustainment in a large-scale implementation of parent–child interaction therapy. Evidence-based Pract child Adolesc mental health. 2021;6(2):262–76. Rodriguez A, Lau AS, Wright B, Regan J, Brookman-Frazee L. Mixed-method analysis of program leader perspectives on the sustainment of multiple child evidence-based practices in a system-driven implementation. Implement Sci. 2018;13:1–14. Driessen E, Cuijpers P, Hollon SD, Dekker JJ. Does pretreatment severity moderate the efficacy of psychological treatment of adult outpatient depression? A meta-analysis. J Consult Clin Psychol. 2010;78(5):668. Price JM, Roesch SC, Walsh NE. Effectiveness of the KEEP foster parent intervention during an implementation trial. Child Youth Serv Rev. 2012;34(12):2487–94. Armstrong CC, Harvey AG. Barriers and facilitators to behavior change for individuals with severe mental illness who received the transdiagnostic intervention for sleep and circadian dysfunction in a community mental health setting. J Behav Health Serv Res. 2021:1017. Gumport NB, Yu SH, Harvey AG. Implementing a transdiagnostic sleep and circadian intervention in a community mental health setting: A qualitative process evaluation with community stakeholders. Psychiatry Res. 2020;293:113443. Harvey G, Kitson A. Translating evidence into healthcare policy and practice: Single versus multi-faceted implementation strategies - is there a simple answer to a complex question? Int J Health Policy Manag. 2015;4(3):123–6. Footnotes Previously referred to as TranS-C Tables Table 1. Overview of i-PARIHS Core Constructs, Background Considerations, and Key Findings i-PARIHS core constructs Background Considerations Key Findings from the Present Study Innovation Efficacy data for TTT Promising data on TTT for EBPTs but insufficient research, some mixed findings and methodological problems with existing research. TTT is an effective approach to delivering TSC in CMHCs. Consideration of the characteristics of TTT that impact uptake Community partners saw value in TTT as a low-cost path to implementing TSC and other new EBPTs. TTT can be feasible within CMHCs. Aligning evidence with local priorities and practice Staff turnover must be considered when planning TTT. TSC training and training to train other providers had potential to create excessive burden on Generation 2 local CMHC trainers and providers. Due to staff turnover, multiple trainers and providers were trained. Local CMHC trainers can train future cohorts of providers and/or new local CMHC trainers as needed in response to staff turnover and patient demand. The dose and timing of training was designed to reduce burden on local CMHC trainers and providers. Efficacy data for TSC The standard version of TSC had been associated with improvements in outcomes, when delivered by providers employed in an academic setting and CMHC providers trained by expert trainers. TSC alongside usual care is superior to usual care alone. The providers of TSC were employed in CMHC contexts and were trained to deliver TSC by CMHC trainers. An Adapted and Standard version of TSC yielded positive outcomes. Consideration of the characteristics of TSC that impact uptake While engaging with community partners, there was a clear need and preference for treatments with improved feasibility. The length and complexity of Standard TSC may have contributed to the lower recruitment rates and higher drop-out, compared to Adapted TSC. There were no significant differences between Adapted and Standard TSC on the number of treatment sessions completed. The number of sessions completed for Standard was below the 8 sessions that was recommended. Delivering 8 sessions in the CMHC context may be unrealistic. We provided guidance on how to integrate TSC into sessions alongside other treatments in order to reduce burden and increase feasibility. Aligning evidence with local priorities and practice Adapted TSC was designed to fit with local needs, including fewer and shorter sessions and trainings. Provider ratings of the fit and credibility of Adapted TSC did not differ from Standard TSC. Generation 2 providers recognized the value and practicality of both Adapted and Standard TranS-C and perceived that TSC was a fit with their expectations and needs within the CMHC setting. Recipient People diagnosed with SMI who received TSC In a prior qualitative research (61), concerns were raised about potential cognitive overload experienced by patients who received the standard version of TSC. Combining Adapted and Standard TSC, patient improvements were observed in sleep, psychiatric symptoms and functional impairment at the post-treatment assessment. Improvements in psychiatric symptoms and functional impairment were mediated through the proposed mechanism of change – namely, sleep and circadian functioning. CMHC providers who were trained by CMHC trainers to deliver TSC CMHC providers have insufficient time and resources, carry a heavy caseload, and the patients they serve experience high rates of comorbidity and complexity. Training and supervision in EBPTs tend to not be reimbursed by payers. In a prior qualitative research (62), concerns were raised about the fit between the standard version of TSC and the high workload of providers. Providers in both conditions rated TSC as acceptable, appropriate and feasible. Context Local level (micro) The micro level was the main focus of facilitation. The type and intensity of facilitation varied across providers and sites. Example activities: Establishing CE credits for participating in training and to help providers meet license requirements; offering certification in TSC for CMHC providers and trainers; providing leadership and professional development opportunities; facilitating providers to be seen as sleep experts by county leadership and providing networking opportunities through our cross-county meetings. Findings for the present study focused on the innovation and recipient levels. Organizational level (meso) Example activities: organization-wide trainings; establishing relationships with leadership; email listserve; meetings between leaders at different organizations to solve commonly-faced problems (e.g., insurance codes, provider incentives, etc); supporting sites in creating dedicated sleep programs. As above Outer context / Wider health system (macro) Example activity: efforts to promote sleep health as essential for mental health. As above Facilitation External facilitation, supported by project leadership Facilitators’ primary activities were (1) recruiting, training, and providing consultation for local CMHC trainers and (2) recruiting and enrolling Generation 2 providers and patients. While local CMHC trainers were heavily involved in increasing provider adoption and utilization of TSC, the facilitators remained in charge of recruiting and enrolling providers and patients through the formal study procedures (e.g., consent, assessments) to reduce burden. Facilitators also held as-needed consultation for TSC providers across generations, offered certification in sleep treatment and sleep training, processed Continuing Education credits, and organized regular meetings with CMHC leadership to provide ongoing support and problem-solve barriers in implementing TSC. After local CMHC trainers held their first training, the facilitation team gradually transferred select responsibilities to them (e.g., presenting to CMHC providers on advanced sleep-related topics; supervising TSC cases on the path to certification). Facilitation was effective in supporting CMHCs to promote the adoption of TTT and in supporting providers to deliver TSC. Facilitation was well suited to the variety of unique challenges and obstacles faced by trainers and trainees and at each site. Note. i-PARIHS core constructs are derived from Harvey & Kitson’s theoretical papers (1, 63). Several entries in this table are identical to Table 1 in the Phase 1 report (33) because several findings replicated the Phase 1 results. Table 2. Patient Demographics and Number of Sessions by Treatment Condition (Standard versus Adapted TSC) at Pre-Treatment collapsed over UC-DT and immediate TSC conditions Characteristic Standard TSC ( n = 16) Adapted TSC ( n = 127) n % n % c 2 p -value Sex 0.01 0.75 Female 9 56.25 81 63.78 Male 7 43.75 46 36.22 Ethnicity 0.66 0.42 Hispanic or Latino 5 31.25 26 20.47 Not Hispanic or Latino 10 62.50 101 79.53 Race 12.70 0.05 American Indian/Alaska Native 0 0.00 7 5.51 Native Hawaiian/Pacific Islander 0 0.00 3 2.36 Asian 2 12.50 13 10.24 Black or African American 5 31.25 24 18.90 White 4 25.00 54 42.52 More than one race 1 6.25 20 15.75 Other/category not listed 4 25.00 6 4.72 Education 7.87 0.10 High school graduate or below 3 18.75 11 8.66 Some or completed college or vocational school 12 75.00 79 64.20 Some or completed graduate school 4 25.00 34 26.77 Other/category not listed 1 6.25 0 0.00 Missing/declined to answer 0 0.00 3 2.36 Employment 3.34 0.34 Full-time 4 25.00 24 18.90 Part-time 3 18.75 19 14.96 Not employed 7 43.75 79 62.20 Other/category not listed 2 12.50 5 3.94 Civil Status 8.11 0.04 Partnered 3 25.00 25 19.69 Unpartnered 12 75.00 101 79.53 Other/category not listed 1 6.25 0 0.00 Missing/declined to answer 0 0.00 1 0.79 Living Arrangement 4.38 0.36 Alone 1 6.25 25 19.69 With family 12 75.00 69 54.33 With friend or roommate or pet 2 12.50 20 15.75 Supported housing 0 0.00 10 7.87 Other/category not listed 0 0.00 3 2.36 Government Assistance a 4.01 0.86 Unemployment 0 0.00 3 2.36 Medicare 1 6.25 11 8.66 Medicaid 4 25.00 45 35.43 Social Security 3 18.75 13 10.24 Food Stamps 3 18.75 27 21.26 SSI/SSDI 2 12.50 25 19.69 SNAP 1 6.25 14 11.02 None 0 0.00 0 0.00 Other/category not listed 4 25.00 17 13.39 Missing/declined to answer 5 31.25 38 29.92 Annual Personal Income 11.48 0.12 = $50,000 1 6.25 17 13.39 I don’t know my income 3 18.75 20 15.75 Missing/declined to answer 0 0.00 1 0.79 Annual Household income 14.09 0.05 = $50,000 1 6.25 31 24.41 I don’t know my income 3 18.75 29 22.83 Missing/declined to answer 0 0.00 2 1.57 Self-reported diagnosis b 14.76 0.14 Neurodevelopmental disorders 1 6.25 21 16.54 Psychosis 3 18.75 33 25.98 Mood Disorder Features (Bipolar) 2 12.50 23 18.11 Mood Disorder Features (Unipolar) 7 43.75 59 46.46 Anxiety disorders 8 50.00 64 50.39 Obsessive-compulsive and related disorders 0 0.00 5 3.94 Trauma and stressor-related disorders 2 12.50 31 24.41 Dissociative disorders 0 0.00 0 0.00 Personality disorders 0 0.00 2 1.57 Feeding and eating disorders 0 0.00 0 0.00 Substance-related and addictive disorders 0 0.00 5 3.94 Other/category not listed 2 12.50 1 0.79 Missing/declined to answer 1 6.25 14 11.02 Mean SD Mean SD t p -value Age 45.00 10.68 43.33 14.08 0.56 0.58 Education (years) 15.00 4.19 14.82 3.57 0.16 0.88 No. of sessions received (all) c 3.69 3.32 4.33 3.95 -0.71 0.48 No. of sessions received (completers) d 6.33 2.42 5.34 3.70 0.93 0.38 Note. a Some patients endorsed more than one government assistance category. b Comorbidity was common. c Number of TSC sessions received by all enrolled patients in the study. d Number of TSC sessions received by patients who completed treatment. Chi-squared was used for categorical variables, and t tests were used for continuous variables. Table 3. Provider Demographics by TSC Treatment Condition (Standard versus Adapted TSC) at Post-Training Characteristic Standard TSC ( n = 6) Adapted TSC ( n = 47) n % n % c 2 p -value Sex 0.55 0.76 Female 5 83.33 33 70.21 Male 0 0.00 2 4.26 Missing/declined to answer 1 16.67 12 25.53 Ethnicity 0.50 0.78 Hispanic or Latino 1 16.67 8 17.02 Not Hispanic or Latino 4 66.67 25 53.19 Missing/declined to answer 1 16.67 14 29.79 Race 13.80 0.008 Asian 1 16.67 7 14.89 Black or African American 2 33.33 1 2.13 White 1 16.67 24 51.06 More than one race 1 16.67 1 2.13 Missing/declined to answer 1 16.67 14 29.79 Degree Type a 4.58 0.60 Marriage and Family Therapy 0 0.00 4 8.51 Psychology 1 16.67 4 8.51 Social Work 4 66.67 15 31.91 Nursing 0 0.00 8 17.02 Medical 0 0.00 1 2.13 Other 0 0.00 4 8.51 Missing 1 16.67 12 25.53 Therapeutic Approach a 2.19 0.90 Client Centered 5 83.33 29 61.70 Family Systems 0 0.00 8 17.02 CBT 2 33.33 20 42.55 Psychodynamic 1 16.67 11 23.40 Humanistic 1 16.67 7 14.89 Integrative/Holistic 0 0.00 2 4.26 Missing/declined to answer 1 16.67 13 27.66 Licensure 0.39 0.82 Licensed 3 50.00 24 51.06 Not Licensed 2 33.33 11 24.40 Missing/declined to answer 1 16.67 12 25.53 Mean SD Mean SD t p -value Age 39.50 12.40 40.21 10.19 -0.11 0.92 Caseload 33.00 37.24 33.29 40.18 -0.01 0.99 Employment Duration 1.20 1.10 5.41 6.23 -3.54 0.001 Years Since Degree Earned 6.40 6.43 9.63 9.69 -0.98 0.36 Note. a Some providers endorsed more than one degree type and therapeutic approach. Chi-squared was used for categorical variables, and t tests were used for continuous variables. CBT = cognitive behavioral therapy. Caseload = number of patients on caseload. Employment duration = length of time employed at current CMHC in years. Table 4. Means, Standard Deviations, and Effect Sizes for Primary and Secondary Outcomes Pre-Treatment (for patients) & Post-Training (for providers) Post-Treatment Patient Outcomes UC-DT ( n = 65) TSC ( n = 78) UC-DT TSC d Mean SD Mean SD Mean SD Mean SD PROMIS-SD* 62.05 7.14 62.71 7.69 61.08 8.59 54.74 11.04 -0.90 PROMIS-SRI 59.98 8.79 60.59 8.76 57.88 8.39 52.46 11.9 -0.69 SHC 2.43 1.46 2.31 1.28 2.69 1.44 3.09 1.63 0.43 DSM-5 22.69 9.15 23.68 8.69 20.67 8.46 17.59 9.64 -0.48 SDS 11.86 7.38 12.77 7.62 11.07 7.46 7.86 6.95 -0.54 Standard ( n = 16) Adapted ( n = 127) Standard Adapted d Mean SD Mean SD Mean SD Mean SD PROMIS-SD* 63.81 6.66 62.23 7.52 54.21 12.22 55.21 11.18 0.51 PROMIS-SRI 61.18 10.54 60.2 8.54 52.74 10.56 53.54 11.14 0.02 SHC 2.62 1.33 2.33 1.37 2.50 2.07 3.07 1.52 0.63 DSM-5 22.06 7.84 23.37 9.03 16.14 8.07 18.00 9.34 0.16 SDS 13.50 8.43 12.21 7.40 8.86 7.73 8.36 6.47 0.03 Provider Outcomes Standard ( n = 16) Adapted ( n = 127) Standard Adapted d Mean SD Mean SD Mean SD Mean SD AIM 4.9 0.22 4.74 0.41 5 0 4.54 0.58 -0.94 FIM 4.95 0.11 4.59 0.57 4.44 0.72 4.43 0.65 4.36 IAM 5 0 4.79 0.35 4.75 0.5 4.39 0.67 NA Note. * indicates primary outcome. PROMIS-SD = PROMIS Sleep Disruption. PROMIS-SD = PROMIS Sleep Disturbance. PROMIS-SRI = PROMIS Sleep-Related Impairment. SHC = Sleep Health Composite (note, scored such that higher scores indicate better sleep health). DSM-5 = DSM-5 Cross-Cutting. SDS = Sheehan Disability Scale. AIM = Acceptability of Intervention Measure. FIM = Feasibility of Intervention Measure. IAM = Intervention Appropriateness measure. TSC = Transdiagnostic Intervention for Sleep and Circadian Dysfunction. UC-DT = usual care followed by delayed treatment with TSC. NA = not applicable (there was no variability in IAM across conditions). Effect sizes are represented with ‘ d ’ and were calculated following Feingold (2009, equation 5), using unadjusted change scores (mean difference between pre- and post-treatment) and raw standard deviations at pre-treatment from each treatment condition. The pre-treatment Ns reflect the size of the intent-to-treat sample. Please see Supplement Tables 4-7 for missing data by aim, timepoint, and outcome. Table 5. Aim 1: Multilevel Modeling Results for Treatment Condition (UC-DT versus TSC) on Patient Outcomes from Pre- to Post-Treatment b SE p- value Outcome PROMIS-SD -7.16 1.68 < 0.001 PROMIS-SRI -6.44 2.00 0.001 SHC 0.55 0.32 0.09 DSM-5 -4.25 1.38 0.002 SDS -4.33 1.39 0.002 Note. Bold indicates significant p -values. b = time-by-treatment interaction. SE = robust standard errors. PROMIS-SD = PROMIS Sleep Disturbance. PROMIS-SRI = PROMIS Sleep-Related Impairment. SHC = Sleep Health Composite. DSM-5 = DSM-5 Cross-Cutting. SDS = Sheehan Disability Scale. Table 6. Aim 1: Mediation Models of Sleep Outcomes on Relations between Treatment Condition (TSC vs. UC-DT) and Psychiatric Symptoms and Overall Functional Impairment at Post-Treatment coefficient SE z p 95% Confidence Interval of effect %MP Aim 1 Model 1: TSC vs. UC-DT à PROMIS-SD at Post à DSM-5 at Post Path a -6.65 1.77 -3.76 <0.001 -10.11 -3.19 - Path b 0.29 0.08 3.73 <0.001 0.14, 0.45 - Total effect -4.26 1.42 -3.00 0.003 -7.04, -1.48 - Indirect effect -1.95 0.81 -2.40 0.02 -3.54, -0.36 45.77% Aim 1 Model 2: TSC vs. UC-DT à PROMIS-SD at Post à SDS at Post Path a -6.50 1.77 -3.68 <0.001 -9.97, -3.04 - Path b 0.34 0.08 4.33 <0.001 0.18, 0.49 - Total effect -3.95 1.18 -3.35 0.001 -6.26, -1.64 - Indirect effect -2.20 0.70 -3.15 0.002 -3.57, -0.83 55.70% Aim 1 Model 3: TSC vs. UC-DT à PROMIS-SRI at Post à DSM-5 at Post Path a -6.42 1.92 -3.35 0.001 -10.19, -2.66 - Path b 0.25 0.06 4.11 <0.001 0.13, 0.37 - Total effect -4.27 1.44 -2.97 0.003 -7.09, -1.45 - Indirect effect -1.62 0.63 -2.56 0.01 -2.87, -0.38 37.94% Aim 1 Model 4: TSC vs. UC-DT à PROMIS-SRI at Post à SDS at Post Path a -6.31 1.89 -3.34 0.001 -10.01, -2.61 - Path b 0.44 0.06 7.62 <0.001 0.32, 0.55 - Total effect -3.86 1.18 -3.26 0.001 -6.17, -1.54 - Indirect effect -2.76 0.86 -3.22 0.001 -4.44, -1.08 71.50% Note. Significant effects for parameters of primary interest (i.e., indirect effects) are highlighted in bold. "-" indicates that value is not relevant to model. SE = robust standard errors. %MP = mediated proportion (i.e., the proportion of the total effect that is explained by the indirect effect expressed as a percentage). TSC = Transdiagnostic Intervention for Sleep and Circadian Dysfunction. UC-DT = usual care followed by delayed treatment with TSC. PROMIS-SD = PROMIS Sleep Disturbance. PROMIS-SRI = PROMIS Sleep-Related Impairment. SDS = Sheehan Disability Scale. DSM-5 = DSM-5 Cross-Cutting. POST = post-treatment assessment. Path a = path from the independent variable to mediator (i.e., Treatment condition à PROMIS-SD or PROMIS-SRI). Path b = path from the mediator to the outcome (PROMIS-SD or PROMIS-SRI à DSM-5 Cross Cutting or SDS). All models adjusted for pre-treatment levels of the relevant mediator (i.e., PROMIS-SD or PROMIS-SRI) and relevant outcome (i.e., DSM-5 Cross-Cutting or SDS). Table 7. Aim 2: Multilevel Modeling Results for TSC Treatment Condition (Standard vs. Adapted) on Patient Outcomes from Pre- to Post-Treatment b SE p- value Outcome PROMIS-SD 2.22 3.40 0.52 PROMIS-SRI 1.36 3.72 0.72 SHC 1.05 0.66 0.11 DSM-5 1.79 3.05 0.56 SDS 0.84 2.93 0.78 Note. Bold indicates significant p -values. b = time-by-treatment interaction. SE = standard errors. PROMIS-SD = PROMIS Sleep Disturbance. PROMIS-SRI = PROMIS Sleep-Related Impairment. SHC = Sleep Health Composite. DSM-5 = DSM-5 Cross-Cutting. SDS = Sheehan Disability Scale. Table 8. Aim 3: Provider Perceived Fit Predicting Patient Outcomes b SE p- value n p 2 Predictor: AIM PROMIS-SD -10.89 4.31 0.02 0.12 D PROMIS-SRI -3.90 3.69 0.30 0.02 SHC 0.45 0.84 0.59 0.01 DSM-5 -3.46 3.61 0.34 0.03 SDS -1.41 2.04 0.49 0.01 Predictor: FIM PROMIS-SD -10.37 2.86 0.001 0.21 PROMIS-SRI -4.45 3.06 0.15 0.04 SHC 0.89 0.46 0.06 0.07 D DSM-5 -5.65 2.16 0.01 0.12 D SDS -2.07 1.21 0.09 0.04 D Predictor: IAM PROMIS-SD -6.26 2.95 0.04 0.09 D PROMIS-SRI 0.79 2.81 0.78 0.002 SHC 0.72 0.47 0.14 0.05 DSM-5 -5.54 1.98 0.01 0.14 SDS -1.62 1.44 0.27 0.03 b = effect of fit at post-treatment on patient outcomes. SE = robust standard errors. n p 2 = partial eta squared. AIM = Acceptability of Intervention Measure. FIM = Feasibility of Intervention Measure. IAM = Intervention Appropriateness measure. PROMIS-SD = PROMIS Sleep Disruption. PROMIS-SD = PROMIS Sleep Disturbance. PROMIS-SRI = PROMIS Sleep-Related Impairment. SHC = Sleep Health Composite (note, scored such that higher scores indicate better sleep health). DSM-5 = DSM-5 Cross-Cutting. SDS = Sheehan Disability Scale. D = Differences when including TSC condition (Standard vs. Adapted) and provider degree as covariates instead of county, all comparisons become less significant. AIM predicting sleep disturbance changes from significant to non-significant ( b = -5.74, SE = 4.31, p = 0.19, n p 2 = 0.04). FIM predicting sleep health composite ( b = 0.64, SE = 0.50, p = 0.21, n p 2 = 0.04) and functional impairment ( b = -1.33, SE = 1.39, p = 0.35, n p 2 = 0.02) changes from marginally significant to non-significant. FIM predicting psychiatric symptoms changes from significant to marginally significant ( b = -4.12, SE = 2.31, p = 0.08, n p 2 = 0.07). IAM predicting sleep disturbance changes from significant to marginally significant ( b = -4.93, SE = 2.84, p = 0.09, n p 2 = 0.06). 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train-the-trainer","fulltext":[{"header":"Contributions to the Literature","content":"\u003cul\u003e\n \u003cli\u003eTrain-the-Trainer holds promise for expanding access to evidence-based treatments\u003c/li\u003e\n \u003cli\u003eUsing external facilitation grounded in the i-PARIHS framework, external experts trained local CMHC trainers to train other CMHC providers\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCMHC providers trained by the local CMHC trainers effectively delivered\u0026nbsp;a transdiagnostic sleep treatment\u003c/li\u003e\n \u003cli\u003eThe sleep treatment improved outcomes for CMHC patients with serious mental illness\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eProviders rated the sleep treatment as a strong fit for the CMHC context.\u003c/li\u003e\n \u003cli\u003eHigher provider ratings of fit were associated with better patient outcomes\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eAccording to the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e), the successful implementation of an evidence-based psychological treatment (EBPT) into practice is a function of the quality of evidence for the innovation, the recipients of the innovation, the characteristics of the context into which the innovation will be implemented, and the approach by which the innovation is integrated or facilitated into the context. The present study \u0026ndash; focused on the implementation of the Transdiagnostic Intervention for Sleep and Circadian dysfunction (TSC) via Train-the-Trainer (TTT) \u0026ndash; will be introduced through the lens of the i-PARIHS framework (also see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eInnovations and Recipients\u003c/h3\u003e\n\u003cp\u003eEBPTs typically require specialized training for providers. Prior research has established that an effective approach to training providers in EBPTs includes a training workshop utilizing active learning strategies, a provider manual, and ongoing clinical supervision (e.g., 2, 3\u0026ndash;6). However, barriers to the use of these multicomponent training initiatives in routine practice settings include insufficient time and funding, shortage of trainers and supervisors, and staff turnover (e.g., 7, 8). As a potential solution, the first \u0026ldquo;innovation\u0026rdquo; tested in the present study is the Train-the-Trainer (TTT) which involved the external university-based \u0026ldquo;expert trainers\u0026rdquo; training an initial cohort of providers in an EBPT. These providers are referred to as \u0026ldquo;Generation 1.\u0026rdquo; These providers were offered additional training on how to \u003cem\u003etrain others\u003c/em\u003e in the EBPT and became \u0026ldquo;local\u0026rdquo; trainers. These local CMHC trainers then trained the next cohort of providers within their organization, referred to as \u0026ldquo;Generation 2.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eAlthough relatively few studies have been conducted on TTT for EBPTs, the existing research has been encouraging (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e). At the provider-level, prior studies show no difference between generations on select outcomes, such as training effectiveness (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e), provider competence (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e) and fidelity (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e). However, there is also evidence of poorer outcomes in Generation 2 relative to expert-led trainings in the domains of provider skill acquisition (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e), quality case materials (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e), provider knowledge about the EBT (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e) and provider satisfaction with the training (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e). Less research has measured TTT patient-level outcomes and relatively few clinical populations and contexts have been investigated (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e). Furthermore, the existing research is qualified by small samples and methodological limitations (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e). The present study aims to help fill these gaps by focusing on two groups of \u0026ldquo;recipients\u0026rdquo; (per i-PARIHS). The main focus is on the patient recipients who received the EBPT from Generation 2 providers. This group will be referred to as \u0026ldquo;Generation 2 patients.\u0026rdquo; An additional focus is on the Generation 2 providers.\u003c/p\u003e\n\u003cp\u003eThe second \u0026ldquo;innovation\u0026rdquo; tested was the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC)\u003csup\u003e1\u003c/sup\u003e(20). TSC is an EBPT that aims to improve six dimensions of sleep health (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e) and targets sleep and circadian dysfunction, which is a common transdiagnostic contributor to serious mental illness (SMI) (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). In the present study, the patient recipients were SMI patients who has sleep and/or circadian problems. They were randomized to either Standard or Adapted TSC. Standard TSC was developed within a university setting. As described in the protocol papers (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e), Adapted TSC was developed to address the concern that Standard TSC may not \u0026ldquo;fit\u0026rdquo; a routine practice setting. Thus, Adapted TSC was customized to fit the context for this study using theory, data, end-user input, consideration of TSC\u0026rsquo;s theoretical underpinnings and mechanisms of action and treatment strategies that addressed the key mechanisms.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eContext\u003c/h2\u003e\n \u003cp\u003eThe context for this study (as per i-PARIHS) was community mental health centers (CMHCs) which, in the United States, are a large provider of affordable mental health services for people who are low-income and diverse with respect to demographic and clinical presentations. CMHC providers often have insufficient time and resources, carry a heavy caseload, and the patients they serve experience high rates of comorbidity and complexity (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e). It can be difficult for CMHC providers to receive training and supervision in EBPTs (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e), because of the cost associated with training and supervision. Before broadly recommending TTT to CMHCs and similar services, TTT must first be tested to assess its effectiveness for training providers in EBPTs within CMHCs.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eFacilitation\u003c/h3\u003e\n\u003cp\u003eFacilitation was chosen as the implementation strategy to support TTT due to its strong evidence base (e.g., 28, 29, 30). In this study, each CMHC received direct support from the lead facilitator, a licensed clinical social worker (ERA) with expertise in community mental health and sleep treatment, along with a team of trained facilitators employed by the research team. The facilitation team was overseen by the Principal Investigator (PI; AGH) and received periodic guidance from a Replicating Effective Programs and facilitation expert (AMK). Facilitation activities were also informed by the Veterans Affairs facilitation manual (\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e) and Harvey and Kitson\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e) Facilitation Guide. Additionally, the lead facilitator (ERA) and postdoctoral scholar (LDS) completed the Behavioral Health Veterans Affairs Quality Enhancement Research Initiative Implementation (BH QUERI) Facilitation Training and ERA regularly attended BH QUERI\u0026rsquo;S monthly drop-in consultation group.\u003c/p\u003e\n\u003ch3\u003eThe Present Study\u003c/h3\u003e\n\u003cp\u003eThis paper describes Phase 2 of a three-part hybrid type 2 effectiveness-implementation study. As described in the protocol paper (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e), this study builds upon the infrastructure of Phase 1, the Implementation Phase (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e). During the Implementation Phase, sites were cluster-randomized by county to Adapted or Standard TSC with 1:1 allocation. External expert trainers trained an initial cohort of providers (i.e., Generation 1 providers) in TSC. Then, within each county, patients were randomized to receive immediate TSC or usual care and delayed treatment with TSC (UC-DT) from Generation 1 providers. In Phase 1, TSC (combining Adapted and Standard) was associated with improvement from pre- to post-treatment relative to UC-DT. However, Adapted versus Standard TSC did not differ on provider ratings of fit and better fit did not mediate the relation between TSC condition and patient outcome (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe first aim of Phase 2 was to assess the effectiveness of TSC, compared to UC-DT, for Generation 2 patients who were treated by Generation 2 providers. We hypothesized that compared to UC-DT, TSC (combining Adapted and Standard) would be associated with larger reductions in the primary patient outcome of sleep disturbance, and the secondary patient outcomes of sleep-related impairment, sleep health, functional impairment, and psychiatric symptoms. To assess sleep and circadian problems as a mediator of the effects of TSC on patient outcomes, we further hypothesized that TSC\u0026rsquo;s benefits for functional impairment and psychiatric symptoms would be mediated by improvements in sleep and circadian problems. The second aim was to assess the effects of TSC treatment condition (Adapted vs. Standard) on primary and secondary outcomes in Generation 2. We hypothesized that Adapted TSC, relative to Standard TSC, would be associated with greater improvements from pre- to post-treatment. The third aim was to examine whether provider ratings of the perceived fit of TSC at post-treatment predicts change in patient outcomes at post-treatment. We hypothesized that greater provider perceived fit of TSC at post-treatment would be associated with improvements in patient outcomes at post-treatment, adjusting for perceived fit at post-training and outcome at pre-treatment. Exploratory analyses focused on (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) comparing Adapted and Standard TSC on patient perceptions of credibility/improvement and select PhenX Toolkit outcomes measuring suicidal ideation/behaviors and substance use; and (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) determining whether treatment effects of TSC versus UC-DT are moderated by risk factors.\u003c/p\u003e\n\u003cp\u003eAim 1 and the exploratory analyses were pre-specified (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e). However, due to a small sample size for provider variables in the Standard condition, pre-specified Aims 2 and 3 could not yield reliable estimates. These results are reported in Additional File 1, see Supplement Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and have been replaced with revised Aims 2 and 3 that reflect the original intent and align with sample size constraints. The pre-treatment intent-to-treat sample sizes for the original pre-specified Aims and those reported in the final analyses are presented in Supplement Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Additional File 1. Exploratory Aim 1 from the protocol paper is not included here, as it will be part of a forthcoming report comparing Generations 1 and 2 on patient outcomes.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u003c/h2\u003e\n \u003cp\u003eThis study focused on 53 Generation 2 providers (Adapted TSC\u0026thinsp;=\u0026thinsp;47; Standard TSC\u0026thinsp;=\u0026thinsp;6), and 143 Generation 2 patients (Adapted TSC\u0026thinsp;=\u0026thinsp;127; Standard TSC\u0026thinsp;=\u0026thinsp;16). Participants were recruited from CMHCs and consisted of Generation 2 providers who had been trained by local CMHC trainers and Generation 2 patients. Participants were masked to condition (Adapted vs. Standard TSC), but not patient treatment allocation (immediate vs. delayed). All CMHC sites from the Implementation Phase were invited to participate in the TTT Phase. The inclusion criteria for selecting the CMHC sites for the Implementation Phase were: 1) provision of publicly funded adult mental health outpatient services and 2) support from CMHC leadership.\u003c/p\u003e\n \u003cp\u003eCMHC sites in the following eight counties in California, USA participated: Alameda, Contra Costa, Kings, Monterey, Placer, Santa Cruz, Solano, and Santa Clara. There were 29 CMHC trainers (Adapted TSC\u0026thinsp;=\u0026thinsp;20; Standard TSC\u0026thinsp;=\u0026thinsp;9), 53 CMHC providers (Adapted TSC\u0026thinsp;=\u0026thinsp;47; Standard TSC\u0026thinsp;=\u0026thinsp;6) and 143 CMHC patients. Of the patients, 78 were randomized to receive TSC immediately (Adapted TSC\u0026thinsp;=\u0026thinsp;70; Standard TSC\u0026thinsp;=\u0026thinsp;8) and 65 were randomized to UC-DT (Adapted TSC\u0026thinsp;=\u0026thinsp;57; Standard TSC\u0026thinsp;=\u0026thinsp;8). The larger number of providers and patients in Adapted TSC was driven by stronger recruitment in the counties cluster randomized to this condition and perhaps also provider and patient preference for the shorter, \u0026ldquo;fitted\u0026rdquo; approach, as compared to Standard TSC.\u003c/p\u003e\n \u003cp\u003eThe inclusion criteria for local CMHC trainers were: 1) employed in participating CMHCs; 2) completed a Generation 1 TSC training (i.e., led by UC Berkeley expert trainers); and 3) volunteered to participate and formally consent to participate.\u003c/p\u003e\n \u003cp\u003eCMHCs determined eligibility for Generation 2 providers (e.g., case managers, nurses, psychiatrists, training department staff), because this mirrors their real-world practice of determining who acquires additional training. For some CMHCs, this involved mandating TSC training for all untrained staff, whereas in others, leadership advertised the opportunity and allowed anyone who was interested to register. The other inclusion criteria for Generation 2 providers were: 1) employed or able to deliver patient-facing services to patients within the CMHC; 2) interested in learning and delivering TSC; and 3) voluntarily consented to participate.\u003c/p\u003e\n \u003cp\u003eThe inclusion criteria for patients were: 1) aged 18 years and older; 2) met criteria for an SMI per self-report and confirmed by referring provider or administration of the Mini International Neuropsychiatric Interview (DSM-5, Version 7.0.0) by a licensed clinical social worker on the research team; 3) exhibited a sleep or circadian disturbance as determined by endorsing 4 (quite a bit) or 5 (very much), or the equivalent for reverse scored items, on one or more items on the PROMIS-Sleep Disturbance (\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e); 4) guaranteed place to sleep for at least two months that is not a shelter; 5) receiving the standard of care for the SMI and consented to regular communications between the research team and provider; and 6) consented to access their medical record and to participate in the study.\u003c/p\u003e\n \u003cp\u003ePatients were excluded if they met any of the following criteria: 1) presence of an active and progressive physical illness or neurological degenerative disease that was directly related to the onset and course of the sleep and circadian problems, or that made participation in the study unfeasible, as assessed by the Checklist of Medical Conditions and Symptoms on the Duke Structured Interview for Sleep Disorders (\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e) and clinical interview; 2) presence of substance abuse/dependence only if it made participation in the study unfeasible; 3) current active intent or plan to commit suicide (those with suicidal ideation are eligible) only if it made participation in the study unfeasible, or homicide risk; 4) night shift work for more than two nights per week in the past three months (i.e., regularly scheduled work from 12 a.m. \u0026ndash; 6 a.m.); or 5) pregnant or breastfeeding.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eInterventions\u003c/h2\u003e\n \u003cp\u003eTwo variations of TSC were tested: Adapted TSC and Standard TSC. Both were delivered alongside the usual care offered by each CMHC. The control condition was usual care followed by delayed treatment (UC-DT). See Additional File 2 for more detailed description of all conditions.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStandard TSC\u003c/h3\u003e\n\u003cp\u003eCMHC providers were trained to deliver Standard TSC across eight 50-minute, weekly sessions (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e). It was comprised of 4 cross-cutting modules featured in every session, 4 core modules, and 7 optional modules, used based on clinical presentation, treatment goals, and provider case conceptualization. Training for the Standard TSC condition consisted of a 1-day workshop (i.e., 6\u0026ndash;8 hours) or two, 3-hour training blocks, based on CMHC preference.\u003c/p\u003e\n\u003ch3\u003eAdapted TSC\u003c/h3\u003e\n\u003cp\u003eWe grounded the process for adapting TSC in theory, data, and end-user input. Adapted TSC was delivered by CMHC providers across four, 20-minute, weekly sessions (see Additional File 2 for description). Treatment consisted of the same four \u003cem\u003ecross-cutting modules\u003c/em\u003e and three of the four \u003cem\u003ecore modules\u003c/em\u003e as Standard TSC along with one \u003cem\u003eoptional module\u003c/em\u003e focused on reducing sleep-related worry. Training for the Adapted TSC condition consisted of four, 1-hour workshops or two, 2-hour workshops, based on CMHC preferences.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eUsual Care and Delayed Treatment with TSC (UC-DT)\u003c/h2\u003e\n \u003cp\u003eIn UC-DT, patients began with usual care for four or eight weeks, depending on whether their CMHC was randomized to Adapted TSC or Standard TSC, respectively. After the delay, they received Adapted or Standard TSC, similarly based on the condition to which their CMHC had been randomized. Usual care in CMHCs involves working with a service provider\u0026mdash;such as a psychologist, case manager, occupational therapist, psychiatrist, or nurse practitioner\u0026mdash;who delivers mental health support within their professional scope.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasures\u003c/h2\u003e\n \u003cp\u003eIn addition to the measures below, a sociodemographics form was completed by providers and patients. Only measures analyzed for the aims of this paper are briefly reported below. See Additional File 3 for further details on each measure.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eGeneration 2 Patients\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Disturbance.\u003c/strong\u003e The 8-item PROMIS-Sleep Disturbance (PROMIS-SD) assessed disruption to sleep (e.g., trouble staying asleep) over the past seven days and has demonstrated acceptable reliability and validity (\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e). This was the primary outcome for the patient-level analyses.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSleep-Related Impairment.\u003c/strong\u003e The 8-item PROMIS-Sleep Related Impairment (PROMIS-SRI) assessed daytime impairment related to sleep problems using the same scale as the PROMIS-SD.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional Impairment.\u003c/strong\u003e Functional impairment was assessed via the Sheehan Disability Scale (SDS) (\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e) which has demonstrated good reliability and validity (\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Sleep Health\u003c/strong\u003e. The Sleep Health Composite measured overall sleep health for the complexity of sleep and circadian problems experienced by people diagnosed with SMI and that are covered by TSC (\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e). The initial validity of this measure has been established (\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePsychiatric Symptoms\u003c/strong\u003e. The DSM-5 Cross-Cutting Measure assessed psychiatric symptoms across 13 mental health domains (e.g., depression, anger, mania, psychosis, substance use). This measure has demonstrated good test-retest reliability and clinical utility (\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePhenX Toolkit.\u003c/strong\u003e (\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e). Two subscales from the screening version of the Columbia-Suicide Severity Rating Scale\u0026mdash;Severity of Suicidal Ideation and Suicidal Behavior\u0026mdash;were administered. The PhenX \u0026lsquo;Alcohol \u0026ndash; 30-Day Quantity and Frequency\u0026rsquo;, \u0026lsquo;Tobacco \u0026ndash; 30 Day Quantity and Frequency\u0026rsquo;, \u0026lsquo;Substances \u0026ndash; 30-Day Frequency\u0026rsquo;, and \u0026lsquo;Supplemental Beverage Questionnaire\u0026rsquo; were used to assess alcohol, tobacco, psychoactive substance, and caffeine consumption over the past 30 days.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCredibility and Perceived Improvement\u003c/strong\u003e. At the post-treatment assessment, perceptions of TSC\u0026rsquo;s credibility and symptom improvement were assessed by four questions adapted from the Credibility/Expectancy Questionnaire (CEQ) (Devilly \u0026amp; Borkovec, 2000).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eGeneration 2 Providers\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eAcceptability.\u003c/strong\u003e Providers rated the acceptability of TSC via the \u003cem\u003eAcceptability of Intervention Measure\u003c/em\u003e (AIM) (Weiner et al., 2017) which has satisfactory validity, internal reliability, test-retest reliability, and sensitivity to change (\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e). This was the primary outcome for the provider-level analyses.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAppropriateness and Feasibility\u003c/strong\u003e. Providers rated the appropriateness and feasibility of TSC via the \u003cem\u003eFeasibility of Intervention Measure\u003c/em\u003e (FIM) and \u003cem\u003eIntervention Appropriateness Measure\u003c/em\u003e (IAM) (\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of TSC Sessions.\u003c/strong\u003e The number of sessions delivered to each enrolled patient by each provider was counted.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e. Providers were asked to report their current position, professional degree, and work history, including their caseload, theoretical orientation, licensure status, and previous training in sleep treatment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eProcedure\u003c/h2\u003e\n \u003cp\u003eCMHCs and patients were randomized through a computerized randomization sequence. We did not stratify during randomization at the CMHC level. When randomizing patients, we stratified for the presence of psychosis or not (current), presence of substance use or not (current) and age (\u0026ge;\u0026thinsp;50 or not), as there is evidence these variables can impact sleep or treatment outcome (\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e). Only the facilitators, assessors, and research team (i.e., not CMHCs, local trainers, providers, or patients) were privy to which CMHCs and patients were allocated to which TSC treatment condition (Adapted versus Standard). CMHC providers, local CMHC trainers, and patients knew whether their patients had been randomized to receive the immediate or delayed treatment. A facilitator informed the local trainer once a patient could start having sessions, who then informed the provider. In the immediate condition, the provider is asked to begin sessions as soon as possible. In the delayed condition, the provider was asked to wait until after the patient had completed the post-delay assessment (i.e., approximately four weeks in the Adapted condition or eight weeks in the Standard condition).\u003c/p\u003e\n \u003cp\u003eGeneration 2 provider and patient assessments were conducted by experienced assessors who also handled the consent process to reduce participant burden. As they needed to share study details (e.g., number of assessments, treatment sessions), assessors were unmasked at pre-treatment. Efforts were made to keep assessors masked at post-treatment and 6FU. Assessors received thorough training and ongoing supervision to ensure survey integrity and minimize bias.\u003c/p\u003e\n \u003cp\u003eThe UC Berkeley facilitation team transitioned CMHC sites from the Implementation Phase to the TTT Phase on a rolling basis. Each site\u0026rsquo;s readiness for TTT was assessed by the level of provider engagement, the number of patients who had completed sleep treatment, and the supportiveness of leadership. The first site was transitioned to TTT in December 2020, and all sites were transitioned by December 2022. Facilitator\u0026rsquo;s primary activities in the TTT Phase are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eLocal CMHC trainers led Generation 2 trainings independent of the expert trainer. Due to the COVID-19 pandemic and in accordance with local preferences and requirements, all Generation 2 trainings were delivered over Zoom. Generation 2 trainers had varying degrees of access to and familiarity with Zoom and little time to master it. Thus, for the first training led by each local trainer, a UC Berkeley facilitator attended the meeting to provide support with Zoom technology. The facilitator only answered content-related questions if requested by the local trainer. The UC Berkeley facilitator had some content-knowledge regarding TSC, but they were not trainers. After the first training, facilitator attendance was offered but not required.\u003c/p\u003e\n \u003cp\u003eFollowing conducting their first training, local CMHC trainers began holding drop-in supervision hours for Generation 2 providers. The expert trainer continued to hold drop-in consultation hours, open to Generation 1 providers. Also, the expert trainer held individual consultation for the local CMHC trainers to support their transition to a supervision role. Overall, we viewed the supports detailed above to be deviations from the ideal TTT structure yet crucial within the CMHC context and particularly during the pandemic.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eLocal CMHC Trainers\u003c/h2\u003e\n \u003cp\u003eTrainers did not complete assessment batteries for the TTT Phase and are not a focus of this report.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eGeneration 2 Providers\u003c/h2\u003e\n \u003cp\u003eProvider assessments were completed after the provider completed TSC training (i.e., post-training), as well as at post-treatment for each patient they treated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eGeneration 2 Patients\u003c/h2\u003e\n \u003cp\u003ePatient assessments in the immediate TSC treatment conditions were completed at pre-treatment, post-treatment, and six months after treatment (6FU). Patient assessments in the UC-DT condition were completed at pre-treatment and four or eight weeks after pre-treatment (i.e., at the end of usual care and before delayed treatment with TSC referred to as post UC-DT), depending on whether their county has been randomized to Adapted or Standard TSC, respectively. Patients did not complete a 6FU assessment after the delay portion of the UC-DT. This was a compromise made with CMHC partners, so that patients would have minimal wait time before receiving treatment. As a result, patients started delayed treatment with TSC after the post UC-DT assessment. Following delayed TSC treatment, patients completed the same assessments as those in the immediate TSC condition i.e., post-treatment and 6FU.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eTrial Registration, Data Transparency and Openness\u003c/h2\u003e\n \u003cp\u003eAll research materials, data, and analysis code are available from the authors upon request. This study was preregistered on clinicaltrials.gov (identifier: NCT05805657), a protocol paper was published (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e) and the study received approval from the Committee for the Protection of Human Subjects at the University of California, Berkeley. Raw data for most outcomes reported here have been uploaded into the National Data Archive. An update was made to clinicaltrials.gov to clarify that, for the primary outcome measures, assessments at mid-treatment were not of primary interest. This error was rectified by moving the mid-treatment assessment to \u0026ldquo;Other outcome measures\u0026rdquo;.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalyses\u003c/h2\u003e\n \u003cp\u003eAnalyses were conducted with Stata Version 16.1. Percent of missing data for each aim are presented in Supplement Tables \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, Additional File 1.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMultilevel Models (Aims 1 \u0026amp; 2 and Exploratory Aims 1 \u0026amp; 2).\u003c/strong\u003e Multilevel models (MLMs) were used to account for multiple observations nested within patient (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e). All MLMs compared pre-treatment to post-treatment and, for level 1, included a dummy-coded time indicator as the predictor (1\u0026thinsp;=\u0026thinsp;post-treatment, pre-treatment as the reference). Exploratory Aim 1 also compared pre-treatment to 6FU follow-up and included an additional time indicator accordingly. For all MLMs, the level 2 equation included dummy-coded treatment condition (\u003cem\u003eAim 1 and Exploratory Aim 2\u003c/em\u003e: 1\u0026thinsp;=\u0026thinsp;immediate TSC, with UC-DT as the reference; \u003cem\u003eAim 2 and Exploratory Aim 1\u003c/em\u003e: 1\u0026thinsp;=\u0026thinsp;Adapted TSC, with Standard as reference) and treatment-by-time interaction terms, which were the parameters of interest. Additionally, Exploratory Aim 2 included three-way interactions between time, treatment, and the following pre-specified moderators: sex (dummy coded: 0\u0026thinsp;=\u0026thinsp;male, 1\u0026thinsp;=\u0026thinsp;female), age (dummy coded: 0\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;50, 1\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;50), and continuous baseline variables of PROMIS-SD, PROMIS-SRI, SDS, and DSM-5 Cross-Cutting. Significant interactions were interpreted using graphs.\u003c/p\u003e\n \u003cp\u003eWe list the outcomes included in each MLM. For Aim 1 and 2 MLMs, the outcomes were PROMIS-SD, PROMIS-SRI, Sleep Health Composite, DSM-5 Cross-Cutting, and SDS. For Exploratory Aim 1, the MLM outcomes were severity of suicidal ideation, average number of caffeinated drinks per day, and number of days the patient consumed alcohol in the past 30 days. For Exploratory Aim 2, the outcomes mirrored Aims 1 and 2. Most outcomes were continuous, except for the following binary outcomes tested in Exploratory Aim 1: suicidal thoughts and behaviors and substance use. For these outcomes, multilevel logistic regression was used. However, because few participants endorsed these items, the models would not converge. Instead, the frequencies of patients\u0026rsquo; endorsement of each item are presented in Additional File 1, Supplement Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLinear Regression Models (Aim 3 and Exploratory Aim 1).\u003c/strong\u003e For Aim 3, residualized change models (\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e) were conducted via multiple linear regression to evaluate whether perceived fit at post-treatment predicted patient outcomes at post-treatment, adjusting for pre-treatment levels. The predictor was AIM, FIM, or IAM at post-treatment, and the outcomes were PROMIS-SD, PROMIS-SRI, Sleep Health Composite, DSM-5 Cross-Cutting, and SDS at post-treatment.\u003c/p\u003e\n \u003cp\u003eFor Exploratory Aim 1, linear regression models were used to test the effects of TSC treatment condition on credibility and perceived improvement at post-treatment. The predictor was dummy-coded TSC treatment condition (1\u0026thinsp;=\u0026thinsp;Adapted TSC, with Standard as reference) and the outcomes were credibility, expectancy, and total CEQ.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStructural Equation Modeling (SEM) (Aim 1).\u003c/strong\u003e For the mediation models in Aim 1, SEM was used. The predictor was condition (immediate TSC vs. UC-DT), the mediator was PROMIS-SD or PROMIS-SRI at post-treatment, and the outcomes were DSM-5 Cross-Cutting and SDS at post-treatment. For all SEMs, the parameter of interest was the indirect effect.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSee Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for the CONSORT diagram for patients. Attrition rates were significantly higher in Standard than Adapted TSC during the treatment phase (56.3% in Standard; 26.8% in Adapted; \u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.55, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), but not significantly different prior to Session 1 (0% in Standard; 13.4% in Adapted; \u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.32, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25), or at 6FU (12.5% in Standard; 4.7% in Adapted; \u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.49, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49). See Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for the CONSORT diagram for providers. Patient and provider demographic variables at pre-treatment by TSC condition (Adapted vs. Standard) are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively. Further information on patient and provider differences by TSC condition are reported in Additional File 1. Additional File 1, Supplement Table 9 presents the patient demographics by immediate TSC vs. UC-DT condition.\u003c/p\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eAim 1\u003c/h2\u003e\n \u003cp\u003eSee Tables \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. TSC, relative to UC-DT, was associated with significant improvements from pre- to post-treatment in sleep disturbance, sleep-related impairment, psychiatric symptoms, and overall functional impairment. TSC, relative to UC-DT, was marginal (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09) for the sleep health composite. Sleep disturbance (primary outcome) withstood the Benjamini-Hochberg correction.\u003c/p\u003e\n \u003cp\u003eSee Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e for SEM results. The indirect effects of treatment condition (TSC vs. UCT-DT) on psychiatric symptoms and overall functional impairment via sleep disturbance and sleep-related impairment were significant.\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eAim 2\u003c/h2\u003e\n \u003cp\u003eSee Tables \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. There were no significant differences between Adapted and Standard TSC on changes from pre- to post-treatment on primary or secondary outcomes.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eAim 3\u003c/h2\u003e\n \u003cp\u003eSee Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. Greater provider perceptions of acceptability (AIM) predicted improvements in patient sleep-related impairment at post-treatment. Greater feasibility (FIM) predicted improvements in patient sleep-related impairment and psychiatric symptoms at post-treatment. Greater FIM also marginally predicted improvements in sleep health composite and functional impairment. Greater appropriateness (IAM) predicted improvements in patient sleep-related impairment and psychiatric symptoms. Provider ratings on the AIM, FIM and IAM ranged from 4.39 to 5 on the 1 (completely disagree) to 5 (completely agree) scale.\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eExploratory Aims\u003c/h2\u003e\n \u003cp\u003eSee Additional File 1, Supplement Tables\u0026nbsp;10 and 11. For Exploratory Aim 1, there were no significant differences between Adapted and Standard TSC on suicidal ideation severity, average daily caffeine use, or past 30-day alcohol use (all \u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026gt;\u0026thinsp;.10).\u003c/p\u003e\n \u003cp\u003eThere were no differences between Adapted versus Standard TSC on credibility, perceived improvement, or total CEQ (all \u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026gt;\u0026thinsp;.10). At post-treatment, the mean of the credibility items was 7.39 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.45) on the 0 (not at all) to 9 (very) scale and mean perceived improvement was 56.13% (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30.63).\u003c/p\u003e\n \u003cp\u003eFor Exploratory Aim 2, see Additional File 1, Supplement Table\u0026nbsp;12. Baseline sleep related-impairment marginally moderated the effects of treatment (UC-DT versus immediate TSC) on functional impairment from pre- to post-treatment. This effect was such that at the lowest level of baseline sleep-related impairment there was no difference between UC-DT vs. immediate TSC conditions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.63), but at the highest level of baseline sleep-related impairment there were greater improvements in functional impairment for the immediate relative to the delayed group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). None of the other planned demographics or baseline clinical symptoms moderated the effects of treatment on patient outcomes from pre- to post-treatment (all \u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026gt;\u0026thinsp;0.10).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe sought to determine if TTT is an effective approach to delivering TSC in CMHCs. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and this discussion frames the findings through i-PARIHS. There are several overarching issues that emerged with regard to the recipients. The patient sample was diverse, largely unpartnered, low income, unemployed, and living with family. Most providers were female social workers using client-centered approaches, licensed, and managing high caseloads (avg. 33 patients). Providers rated both Adapted and Standard TSC as highly appropriate, acceptable, and feasible which suggests that Generation 2 providers recognized the value and practicality of both approaches. Although Standard providers were trained for 8 sessions and Adapted for 4, no significant difference was found in sessions completed. This may reflect feasibility issues in CMHCs and the higher attrition in the Standard group.\u003c/p\u003e\u003cp\u003eWe first focus on outcomes for the patient-level recipients of the innovations delivered in this study. Consistent with our hypothesis, Generation 2 patients treated with TSC reported larger reductions in sleep disturbance, sleep-related impairment, functional impairment, and psychiatric symptoms, relative to UC-DT. The same pattern of findings was evident for the sleep health composite, except the difference between TSC and UC-DT was marginally significant. These findings are important for at least four reasons. First, they extend prior research conducted with university-based providers (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) and CMHC providers trained by university-based trainers (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and add to the growing support for both TSC (\u003cspan additionalcitationids=\"CR50 CR51\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) and the Sleep Health Framework (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Second, they add to the evidence for TTT and to the handful of reports on Generation 2 patient-level outcomes from TTT (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Third, they demonstrate the feasibility and effectiveness of TSC and TTT in CMHCs, with potential for broader application in under-resourced settings. Fourth, these results add to the evidence for external facilitation as a successful implementation strategy (e.g., 28, 29, 30) and expand knowledge by demonstrating the success of external facilitators in establishing a TTT structure.\u003c/p\u003e\u003cp\u003eThere was support for the hypothesis that TSC\u0026rsquo;s benefits for functional impairment and psychiatric symptoms would be mediated by improvements in sleep, as assessed by the PROMIS-SD and PROMIS-SRI. This replicates and extends the parallel finding for the Implementation Phase of this study in which UC Berkeley experts served as the trainers (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) as well as prior research showing that sleep treatment improves symptoms of comorbid mental health conditions (e.g., 53, 54, 55). Within the i-PARIHS framework, these results suggest that facilitation was effective in supporting trainers to train CMHC providers to deliver the innovation (TSC) within the CMHC context, resulting in improved outcomes for the SMI recipients.\u003c/p\u003e\u003cp\u003eFor the second aim, contrary to the hypothesis, there were no significant differences between Adapted and Standard TSC. This result aligns with the parallel findings from the Implementation Phase and might be explained by the relative advantages of each approach (see 33). Of note, Adapted and Standard TSC differed in the number of trainers (Adapted TSC\u0026thinsp;=\u0026thinsp;20; Standard TSC\u0026thinsp;=\u0026thinsp;9) and trainees (Adapted TSC\u0026thinsp;=\u0026thinsp;47; Standard TSC\u0026thinsp;=\u0026thinsp;6). Further, there was more attrition among patients who participated in Standard (56%) than Adapted (27%).\u003c/p\u003e\u003cp\u003eFor the third aim, we focused on the Generation 2 provider recipients who were trained to deliver TSC via TTT. The hypothesis tested was that greater provider perceived fit at post-treatment would be associated improvements in patient outcomes at post-treatment. Most of the results were in the predicted direction (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e8\u003c/span\u003e) with several comparisons reaching statistical significance, and 14 of the 15 comparisons showing small to large effect sizes. Together, these findings add to the growing evidence that underscores the importance of the fit between the treatment and the context (\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) and extends these findings by showing the importance of fit for better patient outcomes. Consistent with i-PARIHS, these results suggest that the use of facilitation, as well as TTT, was effective in supporting the second group of recipients in the study\u0026mdash;CMHC providers\u0026mdash;to deliver the innovation (TSC) despite the many challenges faced at the local, organizational and outer context of CMHCs (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details).\u003c/p\u003e\u003cp\u003eThe only significant moderator of treatment effects was that TSC\u0026rsquo;s effect (compared to UC-DT) on improving functional impairment was particularly strong for those people who had greater sleep-related daytime impairment at baseline. This pattern of findings has been observed in the depression literature (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) and may reflect a floor effect for people with a lower level of symptoms at baseline.\u003c/p\u003e\u003cp\u003eThere are several limitations. First, we did not have the resources to collect data on specific aspects of the context that are important within i-PARIHS such as the outer context nor the dynamic relationships between the micro, meso and macro layers of context (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Second, likely due to county-level cluster randomization and demographic differences, there were baseline differences between the Adapted and Standard TSC groups. Third, although we trained providers to deliver 8 sessions in Standard TSC, they administered an average of 3.69 sessions when considering the full sample. When focusing only on those who completed the full course of TSC, the number of TSC sessions received by patients was closer to the ideal with an average of 6.33 sessions delivered by Standard TSC providers. Perhaps delivering eight sessions in CMHCs may be unrealistic. Fourth, the study design did not allow for a comparison between TSC and UC-DT at the 6-month follow-up. Also, while mid-treatment data were intended for mediation analysis, difficulties in data collection led to a smaller sample size, so post-treatment data were used. Finally, it was not always possible to determine whether drop-outs were due to patient disengagement or provider limitations, underscoring the need for clearer tracking of drop-out causes in future studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWithin the infrastructure of the Implementation Phase (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) of this three-part hybrid type 2 effectiveness-implementation study and with the support of facilitation, TTT was effective. Returning to the i-PARIHS framework, the results indicate that TTT can be used to train CMHC providers to deliver TSC via facilitation that is delivered by university-based external facilitators. These findings add to the growing evidence for the use of multi-component implementation strategies and external facilitation as effective approaches to promoting health-care innovations like TTT and TSC into routine practice (e.g., 28, 29, 30). These results also contribute to the dearth of evidence collected from Generation 2 providers who had been trained by local CMHC trainers and Generation 2 patients (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e) and add support to using a briefer version of TSC in under-resourced settings (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e6FU - six months after treatment\u003c/p\u003e\n\u003cp\u003eAIM - Acceptability of Intervention Measure\u003c/p\u003e\n\u003cp\u003eANCOVA - analysis of covariance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCBT - cognitive behavioral therapy\u003c/p\u003e\n\u003cp\u003eCEQ - Credibility/Expectancy Questionnaire\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCMHCs - community mental health centers\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDSM-5 - DSM-5 Cross-Cutting Measure\u003c/p\u003e\n\u003cp\u003eEBPT - evidence-based psychological treatments\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFIM - Feasibility of Intervention Measure\u003c/p\u003e\n\u003cp\u003eIAM - Intervention Appropriateness measure\u003c/p\u003e\n\u003cp\u003eICC - intraclass correlation coefficients\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMDES - minimum detectable effect sizes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMLMs - multilevel models\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMP - mediated proportions\u003c/p\u003e\n\u003cp\u003ePROMIS-SD - PROMIS-Sleep Disturbance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePROMIS-SRI - PROMIS-Sleep Related Impairment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSDS - Sheehan Disability Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSE -\u0026nbsp;robust standard errors\u003c/p\u003e\n\u003cp\u003eSEMs - structural equation models\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSHC - Sleep Health Composite\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSMI - serious mental illness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSNAP - Supplemental Nutrition Assistance Program\u003c/p\u003e\n\u003cp\u003eSSI/SSDI - Supplemental Social Security Income/Social Security Disability Insurance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTSC \u0026ndash; Transdiagnostic Intervention for Sleep and Circadian Problems\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTTT \u0026ndash; Train-the-Trainer\u003c/p\u003e\n\u003cp\u003eUC-DT - Usual Care followed by Delayed Treatment with TSC\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge all community partners, including the leadership, staff, and patients as well as Dr. Tanya Horwitz for data advice as well as the many assessors and research assistants who worked on the study.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research is funded by the National Institute of Mental Health (R01MH120147; F32MH131284). The funder had no role in the design, collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit this manuscript for publication.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eRaw data for most outcomes reported herein has been uploaded into the NIMH National Data Archive.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAGH led the conception and design of the study, acquired the funding, and drafted all sections of the paper except the data analysis and results sections. AMK, ERA, REH, JS, and AGH designed the implementation strategies. LDS, AM and EO led the data analysis and interpretation and wrote the data analysis and results sections of the paper. DJB, LD, and AMK\u0026nbsp;were involved in the design of the study and acquiring funding. AGH, ERA, MD, JMS, REH and CAC were responsible for acquisition of data. All authors (i.e., AGH, ERA, REH, CAC, EOP, AM, JMS, MD, LD, AMK, DKB, ES, LDS) were involved in revising the manuscript.\u0026nbsp;All authors (i.e., AGH, ERA, REH, CAC, EOP, AM, JMS, MD, LD, AMK, DKB, ES, LDS)\u0026nbsp;read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eApproval to conduct the study was gained from the Committee for the Protection of Human Subjects at the University of California, Berkeley. Participants (providers and patients) were asked to provide written informed consent before participating in the study.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eModel consent forms are available upon request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eAGH, LDS, AMK, DJB, MD, LD and CC have received National Institutes of Health funding. AGH has received book royalties from Guilford Press and Oxford University Press.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOver the past 3 years, DJB has served as a paid consultant to Sleep Number (not greater than $5000 per year). Consulting has focused on insomnia, measurement of sleep characteristics, and relationships between sleep and health outcomes. DJB is an author of questionnaires including the Pittsburgh Sleep Quality Index, Pittsburgh Sleep Quality Index Addendum for PTSD (PSQI-A), Brief Pittsburgh Sleep Quality Index (B-PSQI), Daytime Insomnia Symptoms Scale, Pittsburgh Sleep Diary, Insomnia Symptom Questionnaire, and RU_SATED (copyrights held by University of Pittsburgh). These instruments have been licensed to commercial entities for fees by the University of Pittsburgh. DJB receives a portion of the licensing fees, paid to him by the University of Pittsburgh. He is also co-author of the Consensus Sleep Diary (copyright held by Ryerson University), which is licensed to commercial entities for a fee by Ryerson University. DJB receives a portion of the licensing fees from the University of Pittsburgh through its agreement with Ryerson University. DJB has received grant/contract support from NIH, PCORI, AHRQ, VA and Sleep Number for research relating to sleep, insomnia, sleep interventions, and questionnaire development.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHarvey G, Kitson A. PARIHS revisited: from heuristic to integrated framework for the successful implementation of knowledge into practice. Implement Sci. 2015;11(1):1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeidas RS, Kendall PC. Training therapists in evidence-based practice: a critical review of studies from a systems‐contextual perspective. Clin Psychol Sci Pract. 2010;17(1):1\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrank HE, Becker-Haimes EM, Kendall PC. Therapist training in evidence‐based interventions for mental health: A systematic review of training approaches and outcomes. Clin Psychol Sci Pract. 2020;27(3):e12330.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerschell AD, Kolko DJ, Baumann BL, Davis AC. The role of therapist training in the implementation of psychosocial treatments: A review and critique with recommendations. Clin Psychol Rev. 2010;30(4):448\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRakovshik SG, McManus F. Establishing evidence-based training in cognitive behavioral therapy: A review of current empirical findings and theoretical guidance. Clin Psychol Rev. 2010;30(5):496\u0026ndash;516.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenrich D, Glombiewski JA, Scholten S. Systematic review of training in cognitive-behavioral therapy: Summarizing effects, costs and techniques. Clin Psychol Rev. 2023:102266.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrabson LA, Herschell AD, Snider MD, Jackson CB, Schaffner KF, Scudder AT, et al. Understanding the effectiveness of The Cascading Model to implement parent-child interaction therapy. J Behav Health Serv Res. 2021;48(3):427\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFairburn CG, Patel V. The global dissemination of psychological treatments: a road map for research and practice. Am J Psychiatry. 2014;171(5):495\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerschell AD, Kolko DJ, Scudder AT, Taber-Thomas SM, Schaffner KF, Hart JA et al. A Statewide Randomized Controlled Trial to Compare Three Models for Implementing Parent Child Interaction Therapy. J Clin Child Adolesc Psychol. 2022:1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi T, McClelland MM, Tominey SL, Tracy A. Cost-Effectiveness Analyses on Various Models of The Red Light, Purple Light Self-Regulation Intervention for Young Children. Front Psychol. 2021;12:711578.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTriplett NS, Sedlar G, Berliner L, Jungbluth N, Boyd M, Dorsey S. Evaluating a train-the-trainer approach for increasing EBP training capacity in community mental health. J Behav Health Serv Res. 2020;47(2):189\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartino S, Ball SA, Nich C, Canning-Ball M, Rounsaville BJ, Carroll KM. Teaching community program clinicians motivational interviewing using expert and train‐the‐trainer strategies. Addiction. 2011;106(2):428\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoutham-Gerow MA, Daleiden EL, Chorpita BF, Bae C, Mitchell C, Faye M, et al. MAPping Los Angeles County: Taking an evidence-informed model of mental health care to scale. J Clin Child Adolesc Psychol. 2014;43(2):190\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilfley DE, Agras WS, Fitzsimmons-Craft EE, Bohon C, Eichen DM, Welch RR, et al. Training models for implementing evidence-based psychological treatment: a cluster-randomized trial in college counseling centers. JAMA psychiatry. 2020;77(2):139\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFitzsimmons-Craft EE, Bohon C, Wilson GT, Jo B, Mondal S, Laing O, et al. Maintenance of Training Effects of Two Models for Implementing Evidence-Based Psychological Treatment. Psychiatric Serv. 2021;72(12):1451\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeisz JR, Ugueto AM, Herren J, Marchette LK, Bearman SK, Lee EH, et al. When the torch is passed, does the flame still burn? Testing a train the supervisor model for the Child STEPs treatment program. J Consult Clin Psychol. 2018;86(9):726.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDemchak M, Browder DM. An evaluation of the pyramid model of staff training in group homes for adults with severe handicaps. Education and Training in Mental Retardation. 1990:150\u0026thinsp;\u0026ndash;\u0026thinsp;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChamberlain P, Price J, Reid J, Landsverk J. Cascading implementation of a foster and kinship parent intervention. Child Welfare. 2008;87(5):27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreif R, Becker CB, Hildebrandt T. Reducing eating disorder risk factors: A pilot effectiveness trial of a train-the‐trainer approach to dissemination and implementation. Int J Eat Disord. 2015;48(8):1122\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarvey AG, Buysse DJ. Treating Sleep Problems: A Transdiagnostic Approach. Guilford; 2017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuysse DJ. Sleep health: can we define it? Does it matter? Sleep. 2014;37(1):9\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarvey AG. Treating sleep and circadian problems to promote mental health: perspectives on comorbidity, implementation science and behavior change. Sleep. 2022;45(4):zsac026.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCallaway CA, Sarfan LD, Agnew ER, Dong L, Spencer JM, Hache RE, et al. The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 2: study protocol for a hybrid type 2 effectiveness-implementation cluster-randomized trial using train-the-trainer. Trials. 2023;24(1):503.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSarfan LD, Agnew ER, Diaz M, Dong L, Fisher K, Spencer JM, et al. The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 1: study protocol for a hybrid type 2 effectiveness-implementation cluster-randomized trial. Trials. 2023;24(1):1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee A, Gold R, Caskey R, Haider S, Schmidt T, Ott E, et al. Recruiting Community Health Centers for Implementation Research: Challenges, Implications, and Potential Solutions. New York: Mary Ann Liebert Inc.; 2024. pp. 113\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSarfan LD, Hilmoe HE, Gumport NB, Gasperetti CE, Harvey AG. Outcomes of Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) in a community setting: Unpacking comorbidity. Behav Res Ther. 2022;145:585\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez A, Southam-Gerow MA, O'Connor MK, Allin RB Jr. An analysis of stakeholder views on children's mental health services. J Clin Child Adolesc Psychol. 2014;43(6):862\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKauth MR, Sullivan G, Blevins D, Cully JA, Landes RD, Said Q, et al. Employing external facilitation to implement cognitive behavioral therapy in VA clinics: a pilot study. Implement Sci. 2010;5(1):75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRitchie MJ, Kirchner JE, Parker LE, Curran GM, Fortney JC, Pitcock JA, et al. Evaluation of an implementation facilitation strategy for settings that experience significant implementation barriers. Implement Sci. 2015;10(1):A46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirchner JE, Ritchie MJ, Pitcock JA, Parker LE, Curran GM, Fortney JC. Outcomes of a partnered facilitation strategy to implement primary care\u0026ndash;mental health. J Gen Intern Med. 2014;29(4):904\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRitchie MJ, Dollar KM, Miller CJ, Smith JL, Oliver KA, Kim B et al. Using implementation facilitation to improve healthcare [Internet]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwqueriresearchvagov/tools/implementation/Facilitation-Manualpdf\u003c/span\u003e\u003cspan address=\"https://wwwqueriresearchvagov/tools/implementation/Facilitation-Manualpdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarvey G, Kitson A. Implementing evidence-based practice in healthcare: a facilitation guide. Routledge; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarvey AG, Agnew ER, Esteva Hache R, Spencer JM, Diaz M, Patino EO et al. A randomized trial of Standard versus Adapted versions of the Transdiagnostic Intervention for Sleep and Circadian dysfunction implemented via facilitation and delivered by community mental health providers: Improving the fit of psychological treatments by adapting to context. Mauscript submitted for publication. in press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu L, Buysse DJ, Germain A, Moul D. Development of short forms from the PROMIS sleep disturbance and sleep-related impairment item banks. Behav sleep Med. 2012;10:6\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuysse DJ, Yu L, Moul DE, Germain A, Stover A, Dodds NE, et al. Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments. Sleep. 2010;33:781\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarney C, Edinger J, Olsen M, Stechuchak K, Krysta A, Wyatt J. Inter-rater reliability for insomnia diagnoses derived from the Duke Structured Interview for Sleep Disorders. Maryland: SLEEP 2008; Baltimore; 2008.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheehan DV, Harnett-Sheehan K, Raj BA. The measurement of disability. Int Clin Psychopharmacol. 1996;11(suppl 3):89\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong L, Martinez AJ, Buysse DJ, Harvey AG. A composite measure of sleep health predicts concurrent mental and physical health outcomes in adolescents prone to eveningness. Sleep health. 2019;5(2):166\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNarrow WE, Clarke DE, Kuramoto SJ, Kraemer HC, Kupfer DJ, Greiner L, et al. DSM-5 field trials in the United States and Canada, Part III: development and reliability testing of a cross-cutting symptom assessment for DSM-5. Am J Psychiatry. 2013;170(1):71\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClarke DE, Kuhl EA. DSM-5 cross-cutting symptom measures: a step towards the future of psychiatric care? World Psychiatry. 2014;13(3):314\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamilton CM, Strader LC, Pratt JG, Maiese D, Hendershot T, Kwok RK, et al. The PhenX Toolkit: get the most from your measures. Am J Epidemiol. 2011;174(3):253\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeiner BJ, Lewis CC, Stanick C, Powell BJ, Dorsey CN, Clary AS, et al. Psychometric assessment of three newly developed implementation outcome measures. Implement Sci. 2017;12(1):108.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrow M, Grossman LS, Herbener ES, Davies EW. Ten-year outcome: patients with schizoaffective disorders, schizophrenia, affective disorders and mood-incongruent psychotic symptoms. Br J Psychiatry. 2000;177(5):421\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLarge M, Mullin K, Gupta P, Harris A, Nielssen O. Systematic meta-analysis of outcomes associated with psychosis and co-morbid substance use. Australian New Z J Psychiatry. 2014;48(5):418\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArmstrong CC, Dong L, Harvey AG. Mediators and moderators of outcome from the Transdiagnostic Sleep and Circadian Intervention for adults with severe mental illness in a community setting. Behav Res Ther. 2022:104053.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRabe-Hesketh S, Skrondal A. Multilevel and longitudinal modeling using Stata. 3rd ed. Stata; 2011.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu R, Holmer HK. Change score or follow-up score? Choice of mean difference estimates could impact meta-analysis conclusions. J Clin Epidemiol. 2016;76:108\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarvey AG, Dong L, Hein K, Yu S, Martinez A, Gumport N, et al. A randomized controlled trial of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) to improve serious mental illness outcomes in a community setting. J Consult Clin Psychol. 2021;89(6):537\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTighe CA, Phares A, Weiner DK, Beehler GP, VanSwearingen J, Harvey AG et al. Adapting and Evaluating the Feasibility and Acceptability of a Behavioral Intervention to Promote Sleep Health in Cardiac Rehabilitation. Behav sleep Med. 2024:1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYau AY-Y, Ng K-Y, Lau W-Y, Poon C-Y, Yeung W-F, Chung K-F et al. A group-based transdiagnostic sleep and circadian treatment for major depressive disorder: A randomized controlled trial. J Consult Clin Psychol. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eImes CC, Kline CE, Patel SR, Sereika SM, Buysse DJ, Harvey AG et al. An adapted transdiagnostic sleep and circadian intervention (TranS-C) for adults with excess weight and suboptimal sleep health: Pilot study results. Sleep Adv. 2024:zpae037.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarvey AG, Hein K, Dolsen MR, Dong L, Rabe-Hesketh S, Gumport NB, et al. Modifying the impact of eveningness chronotype (night-owls) in youth: a randomized controlled trial. J Am Acad Child Adolesc Psychiatry. 2018;57(10):742\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaylor DJ, Pruiksma KE. Cognitive and behavioural therapy for insomnia (CBT-I) in psychiatric populations: a systematic review. Int Rev psychiatry. 2014;26(2):205\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeiger-Brown JM, Rogers VE, Liu W, Ludeman EM, Downton KD, Diaz-Abad M. Cognitive behavioral therapy in persons with comorbid insomnia: a meta-analysis. Sleep Med Rev. 2015;23:54\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScott J, Kallestad H, Vedaa O, Sivertsen B, Etain B. Sleep disturbances and first onset of major mental disorders in adolescence and early adulthood: a systematic review and meta-analysis. Sleep Med Rev. 2021;57:101429.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDurlak JA, DuPre EP. Implementation matters: A review of research on the influence of implementation on program outcomes and the factors affecting implementation. Am J Community Psychol. 2008;41(3\u0026ndash;4):327\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarnett M, Brookman-Frazee L, Yu SH, Lind T, Lui J, Timmer S, et al. Train-to-sustain: Predictors of sustainment in a large-scale implementation of parent\u0026ndash;child interaction therapy. Evidence-based Pract child Adolesc mental health. 2021;6(2):262\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodriguez A, Lau AS, Wright B, Regan J, Brookman-Frazee L. Mixed-method analysis of program leader perspectives on the sustainment of multiple child evidence-based practices in a system-driven implementation. Implement Sci. 2018;13:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDriessen E, Cuijpers P, Hollon SD, Dekker JJ. Does pretreatment severity moderate the efficacy of psychological treatment of adult outpatient depression? A meta-analysis. J Consult Clin Psychol. 2010;78(5):668.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrice JM, Roesch SC, Walsh NE. Effectiveness of the KEEP foster parent intervention during an implementation trial. Child Youth Serv Rev. 2012;34(12):2487\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArmstrong CC, Harvey AG. Barriers and facilitators to behavior change for individuals with severe mental illness who received the transdiagnostic intervention for sleep and circadian dysfunction in a community mental health setting. J Behav Health Serv Res. 2021:1017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGumport NB, Yu SH, Harvey AG. Implementing a transdiagnostic sleep and circadian intervention in a community mental health setting: A qualitative process evaluation with community stakeholders. Psychiatry Res. 2020;293:113443.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarvey G, Kitson A. Translating evidence into healthcare policy and practice: Single versus multi-faceted implementation strategies - is there a simple answer to a complex question? Int J Health Policy Manag. 2015;4(3):123\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Previously referred to as TranS-C\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Overview of i-PARIHS Core Constructs, Background Considerations, and Key Findings\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ei-PARIHS core constructs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBackground Considerations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey Findings from the Present Study\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 63.8051%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eInnovation\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eEfficacy data for TTT\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003ePromising data on TTT for EBPTs but insufficient research, some mixed findings and methodological problems with existing research.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eTTT is an effective approach to delivering TSC in CMHCs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eConsideration of the characteristics of TTT that impact uptake\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eCommunity partners saw value in TTT as a low-cost path to implementing TSC and other new EBPTs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eTTT can be feasible within CMHCs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eAligning evidence with local priorities and practice\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eStaff turnover must be considered when planning TTT. TSC training and training to train other providers had potential to create excessive burden on Generation 2 local CMHC trainers and providers.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eDue to staff turnover, multiple trainers and providers were trained. Local CMHC trainers can train future cohorts of providers and/or new local CMHC trainers as needed in response to staff turnover and patient demand. The dose and timing of training was designed to reduce burden on local CMHC trainers and providers.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eEfficacy data for TSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eThe standard version of TSC had been associated with improvements in outcomes, when delivered by providers employed in an academic setting and CMHC providers trained by expert trainers.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eTSC alongside usual care is superior to usual care alone. The providers of TSC were employed in CMHC contexts and were trained to deliver TSC by CMHC trainers. An Adapted and Standard version of TSC yielded positive outcomes.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eConsideration of the characteristics of TSC that impact uptake\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eWhile engaging with community partners, there was a clear need and preference for treatments with improved feasibility.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eThe length and complexity of Standard TSC may have contributed to the lower recruitment rates and higher drop-out, compared to Adapted TSC. There were no significant differences between Adapted and Standard TSC on the number of treatment sessions completed. The number of sessions completed for Standard was below the 8 sessions that was recommended. Delivering 8 sessions in the CMHC context may be unrealistic. We provided guidance on how to integrate TSC into sessions alongside other treatments in order to reduce burden and increase feasibility.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eAligning evidence with local priorities and practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eAdapted TSC was designed to fit with local needs, including fewer and shorter sessions and trainings.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eProvider ratings of the fit and credibility of Adapted TSC did not differ from Standard TSC. Generation 2 providers recognized the value and practicality of both Adapted and Standard TranS-C and perceived that TSC was a fit with their expectations and needs within the CMHC setting.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 63.8051%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eRecipient\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003ePeople diagnosed with SMI who received TSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eIn a prior qualitative research (61), concerns were raised about potential cognitive overload experienced by patients who received the standard version of TSC.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eCombining Adapted and Standard TSC, patient improvements were observed in sleep, psychiatric symptoms and functional impairment at the post-treatment assessment. Improvements in psychiatric symptoms and functional impairment were mediated through the proposed mechanism of change \u0026ndash; namely, sleep and circadian functioning.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eCMHC providers who were trained by CMHC trainers to deliver TSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eCMHC providers have insufficient time and resources, carry a heavy caseload, and the patients they serve experience high rates of comorbidity and complexity. Training and supervision in EBPTs tend to not be reimbursed by payers. In a prior qualitative research (62), concerns were raised about the fit between the standard version of TSC and the high workload of providers.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eProviders in both conditions rated TSC as acceptable, appropriate and feasible.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 63.8051%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eContext\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eLocal level (micro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eThe micro level was the main focus of facilitation. The type and intensity of facilitation varied across providers and sites. Example activities: Establishing CE credits for participating in training and to help providers meet license requirements; offering certification in TSC for CMHC providers and trainers; providing leadership and professional development opportunities; facilitating providers to be seen as sleep experts by county leadership and providing networking opportunities through our cross-county meetings.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eFindings for the present study focused on the innovation and recipient levels.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eOrganizational level (meso)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eExample activities: organization-wide trainings; establishing relationships with leadership; email listserve; meetings between leaders at different organizations to solve commonly-faced problems (e.g., insurance codes, provider incentives, etc); supporting sites in creating dedicated sleep programs.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eAs above\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eOuter context / Wider health system (macro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eExample activity: efforts to promote sleep health as essential for mental health.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eAs above\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 63.8051%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eFacilitation\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5499%;\"\u003e\n \u003cp\u003eExternal facilitation, supported by project leadership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2552%;\"\u003e\n \u003cp\u003eFacilitators\u0026rsquo; primary activities were (1) recruiting, training, and providing consultation for local CMHC trainers and (2) recruiting and enrolling Generation 2 providers and patients. While local CMHC trainers were heavily involved in increasing provider adoption and utilization of TSC, the facilitators remained in charge of recruiting and enrolling providers and patients through the formal study procedures (e.g., consent, assessments) to reduce burden. Facilitators also held as-needed consultation for TSC providers across generations, offered certification in sleep treatment and sleep training, processed Continuing Education credits, and organized regular meetings with CMHC leadership to provide ongoing support and problem-solve barriers in implementing TSC. After local CMHC trainers held their first training, the facilitation team gradually transferred select responsibilities to them (e.g., presenting to CMHC providers on advanced sleep-related topics; supervising TSC cases on the path to certification).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1949%;\"\u003e\n \u003cp\u003eFacilitation was effective in supporting CMHCs to promote the adoption of TTT and in\u0026nbsp;supporting providers to deliver TSC. Facilitation was well suited to the variety of unique challenges and obstacles faced by trainers and trainees and at each site.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e i-PARIHS core constructs are derived from Harvey \u0026amp; Kitson\u0026rsquo;s theoretical papers (1, 63). Several entries in this table are identical to Table 1 in the Phase 1 report (33) because several findings replicated the Phase 1 results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Patient Demographics and Number of Sessions by Treatment Condition (Standard versus Adapted TSC) at Pre-Treatment collapsed over UC-DT and immediate TSC conditions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"902\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22.0245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard TSC (\u003cem\u003en\u003c/em\u003e = 16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 22.0245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdapted TSC (\u003cem\u003en\u003c/em\u003e = 127)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e%\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e%\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ec\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e56.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e63.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e43.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e36.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eHispanic or Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e31.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e20.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eNot Hispanic or Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e62.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e79.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e12.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eNative Hawaiian/Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e10.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e31.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e18.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e42.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMore than one race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e15.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eOther/category not listed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eHigh school graduate or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eSome or completed college or vocational school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e75.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e64.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eSome or completed graduate school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e26.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eOther/category not listed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eFull-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e18.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003ePart-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e14.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eNot employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e43.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e62.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eOther/category not listed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eCivil Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003ePartnered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e19.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eUnpartnered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e75.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e79.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eOther/category not listed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eLiving Arrangement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eAlone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e19.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eWith family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e75.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e54.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eWith friend or roommate or pet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e15.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eSupported housing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eOther/category not listed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eGovernment Assistance\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e35.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eSocial Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e10.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eFood Stamps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e21.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eSSI/SSDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e19.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eSNAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e11.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eOther/category not listed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e13.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e31.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eAnnual Personal Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e11.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;$10,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;$10,000-$20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e37.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e22.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;$20,000-$30,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;$30,00-$40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e17.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;$40,000-$50,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026gt;= $50,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e13.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eI don\u0026rsquo;t know my income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e15.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eAnnual Household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e14.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;$10,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e15.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;$10,000-$20,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e18.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;$20,000-$30,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e31.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;$30,00-$40,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;$40,000-$50,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026gt;= $50,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e24.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eI don\u0026rsquo;t know my income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e22.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eSelf-reported diagnosis\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e14.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eNeurodevelopmental disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e16.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003ePsychosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e25.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMood Disorder Features (Bipolar)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e18.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMood Disorder Features (Unipolar)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e43.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e46.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eAnxiety disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e50.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eObsessive-compulsive and related disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eTrauma and stressor-related disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e24.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eDissociative disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003ePersonality disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eFeeding and eating disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eSubstance-related and addictive disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eOther/category not listed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e11.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e45.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e10.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e43.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e14.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eEducation (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e14.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eNo. of sessions received (all)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8131%;\"\u003e\n \u003cp\u003eNo. of sessions received (completers)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4624%;\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5621%;\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1361%;\"\u003e\n \u003cp\u003e5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.0515%;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.4835%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 95.1131%;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003eSome patients endorsed more than one government assistance category. \u003csup\u003eb\u003c/sup\u003eComorbidity was common. \u003csup\u003ec\u003c/sup\u003eNumber of TSC sessions received by all enrolled patients in the study. \u003csup\u003ed\u003c/sup\u003eNumber of TSC sessions received by patients who completed treatment. Chi-squared was used for categorical variables, and \u003cem\u003et\u0026nbsp;\u003c/em\u003etests were used for continuous variables.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Provider Demographics by TSC Treatment Condition (Standard versus Adapted TSC) at Post-Training\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"898\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21.0762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard TSC (\u003cem\u003en\u003c/em\u003e = 6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 20.6213%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdapted TSC (\u003cem\u003en\u003c/em\u003e = 47)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16.0725%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e%\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e%\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ec\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e83.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e70.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e25.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eHispanic or Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e17.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eNot Hispanic or Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e66.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e53.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e29.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e13.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e14.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e51.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eMore than one race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e29.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eDegree Type\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eMarriage and Family Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003ePsychology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eSocial Work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e66.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e31.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eNursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e17.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eMedical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e25.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eTherapeutic Approach\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eClient Centered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e83.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e61.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eFamily Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e17.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eCBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e42.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003ePsychodynamic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e23.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eHumanistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e14.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eIntegrative/Holistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e27.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eLicensure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eLicensed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e51.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eNot Licensed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e24.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eMissing/declined to answer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e25.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e39.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e12.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e40.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e10.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eCaseload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e33.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e37.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e33.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e40.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eEmployment Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e-3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.3543%;\"\u003e\n \u003cp\u003eYears Since Degree Earned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0976%;\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1302%;\"\u003e\n \u003cp\u003e6.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6428%;\"\u003e\n \u003cp\u003e9.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9786%;\"\u003e\n \u003cp\u003e9.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.0362%;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 88.3987%;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003eSome providers endorsed more than one degree type and therapeutic approach. Chi-squared was used for categorical variables, and \u003cem\u003et\u0026nbsp;\u003c/em\u003etests were used for continuous variables. CBT = cognitive behavioral therapy. Caseload = number of patients on caseload. Employment duration = length of time employed at current CMHC in years.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Means, Standard Deviations, and Effect Sizes for Primary and Secondary Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"913\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 330px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-Treatment (for patients) \u0026amp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePost-Training (for providers)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 347px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-Treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003ePatient Outcomes\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eUC-DT (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 65)\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eTSC (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 78)\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eUC-DT\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eTSC\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ed\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePROMIS-SD*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e62.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e62.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e61.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e8.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e54.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e11.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePROMIS-SRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e59.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e8.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e60.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e57.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e52.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eDSM-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e22.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e23.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e20.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e17.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e9.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e11.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e11.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eStandard (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 16)\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eAdapted (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 127)\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eStandard\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eAdapted\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ed\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePROMIS-SD*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e63.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e6.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e62.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e54.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e12.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e55.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e11.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePROMIS-SRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e61.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e10.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e52.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e10.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e53.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eDSM-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e22.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e23.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e16.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e8.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e18.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e9.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e7.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e8.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eProvider Outcomes\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eStandard (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 16)\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eAdapted (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 127)\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eStandard\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eAdapted\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003ed\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e-0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eFIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003eIAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" style=\"width: 913px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e * indicates primary outcome. PROMIS-SD = PROMIS Sleep Disruption. PROMIS-SD = PROMIS Sleep Disturbance. PROMIS-SRI = PROMIS Sleep-Related Impairment. SHC = Sleep Health Composite (note, scored such that higher scores indicate better sleep health). DSM-5 = DSM-5 Cross-Cutting. SDS = Sheehan Disability Scale. AIM = Acceptability of Intervention Measure. FIM = Feasibility of Intervention Measure. IAM = Intervention Appropriateness measure. TSC = Transdiagnostic Intervention for Sleep and Circadian Dysfunction. UC-DT = usual care followed by delayed treatment with TSC. NA = not applicable (there was no variability in IAM across conditions). Effect sizes are represented with \u0026lsquo;\u003cem\u003ed\u003c/em\u003e\u0026rsquo; and were calculated following Feingold (2009, equation 5), using unadjusted change scores (mean difference between pre- and post-treatment) and raw standard deviations at pre-treatment from each treatment condition. The pre-treatment \u003cem\u003eNs\u0026nbsp;\u003c/em\u003ereflect the size of the intent-to-treat sample. Please see Supplement Tables 4-7 for missing data by aim, timepoint, and outcome.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Aim 1: Multilevel Modeling Results for Treatment Condition (UC-DT versus TSC) on Patient Outcomes\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efrom Pre- to Post-Treatment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"444\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003ePROMIS-SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003ePROMIS-SRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eSHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eDSM-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eSDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eBold indicates significant \u003cem\u003ep\u003c/em\u003e-values. \u003cem\u003eb\u003c/em\u003e = time-by-treatment interaction. SE = robust standard errors. PROMIS-SD = PROMIS Sleep Disturbance. PROMIS-SRI = PROMIS Sleep-Related Impairment. SHC = Sleep Health Composite. DSM-5 = DSM-5 Cross-Cutting. SDS = Sheehan Disability Scale.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Aim 1: Mediation Models of Sleep Outcomes on Relations between Treatment Condition (TSC vs. UC-DT) and Psychiatric Symptoms and Overall Functional Impairment at Post-Treatment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"906\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003ecoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e95% Confidence Interval of effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e%MP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 769px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAim 1 Model 1: TSC vs. UC-DT\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026agrave; PROMIS-SD at Post\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026agrave; DSM-5 at Post\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePath a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e-10.11 -3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePath b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e0.14, 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e-7.04, -1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.54, -0.36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e45.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 906px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAim 1 Model 2: TSC vs. UC-DT\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026agrave;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;PROMIS-SD at Post\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026agrave;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;SDS at Post\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePath a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e-9.97, -3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePath b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e0.18, 0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e-6.26, -1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.57, -0.83\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e55.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 906px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAim 1 Model 3: TSC vs. UC-DT\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026agrave; PROMIS-SRI at Post\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026agrave; DSM-5 at Post\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePath a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e-10.19, -2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePath b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e0.13, 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e-7.09, -1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.87, -0.38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e37.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 906px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAim 1 Model 4: TSC vs. UC-DT\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026agrave;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;PROMIS-SRI at Post\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026agrave;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;SDS at Post\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePath a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e-10.01, -2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePath b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e0.32, 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e-6.17, -1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e-2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e-3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 263px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-4.44, -1.08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e71.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 906px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eSignificant effects for parameters of primary interest (i.e., indirect effects) are highlighted in bold.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026quot;-\u0026quot; indicates that value is not relevant to model. SE = robust standard errors.\u003cem\u003e\u0026nbsp;\u003c/em\u003e%MP = mediated proportion (i.e., the proportion of the total effect that is explained by the indirect effect expressed as a percentage). TSC = Transdiagnostic Intervention for Sleep and Circadian Dysfunction. UC-DT = usual care followed by delayed treatment with TSC. PROMIS-SD = PROMIS Sleep Disturbance. PROMIS-SRI = PROMIS Sleep-Related Impairment. SDS = Sheehan Disability Scale. DSM-5 = DSM-5 Cross-Cutting. POST = post-treatment assessment. Path a = path from the independent variable to mediator (i.e., Treatment condition \u0026agrave; PROMIS-SD or PROMIS-SRI). Path b = path from the mediator to the outcome (PROMIS-SD or PROMIS-SRI \u0026agrave; DSM-5 Cross Cutting or SDS). All models adjusted for pre-treatment levels of the relevant mediator (i.e., PROMIS-SD or PROMIS-SRI) and relevant outcome (i.e., DSM-5 Cross-Cutting or SDS).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Aim 2: Multilevel Modeling Results for TSC Treatment Condition (Standard vs. Adapted) on Patient Outcomes from Pre- to Post-Treatment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"444\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003ePROMIS-SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003ePROMIS-SRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eSHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eDSM-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eSDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eBold indicates significant \u003cem\u003ep\u003c/em\u003e-values. \u003cem\u003eb\u003c/em\u003e = time-by-treatment interaction. SE = standard errors. PROMIS-SD = PROMIS Sleep Disturbance. PROMIS-SRI = PROMIS Sleep-Related Impairment. SHC = Sleep Health Composite. DSM-5 = DSM-5 Cross-Cutting. SDS = Sheehan Disability Scale.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8. Aim 3: Provider Perceived Fit Predicting Patient Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"918\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor: AIM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003ePROMIS-SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-10.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.12\u003csup\u003e\u0026nbsp;D\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003ePROMIS-SRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003eSHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003eDSM-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003eSDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor: FIM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003ePROMIS-SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003ePROMIS-SRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003eSHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.07\u003csup\u003e\u0026nbsp;D\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003eDSM-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.12\u003csup\u003e\u0026nbsp;D\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003eSDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.04\u003csup\u003e\u0026nbsp;D\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor: IAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003ePROMIS-SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.09\u003csup\u003e\u0026nbsp;D\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003ePROMIS-SRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003eSHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003eDSM-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.3711%;\"\u003e\n \u003cp\u003eSDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.2426%;\"\u003e\n \u003cp\u003e-1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.5181%;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9528%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7586%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 79.5479%;\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e = effect of fit at post-treatment on patient outcomes. SE = robust standard errors. \u003cem\u003en\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e\u003csup\u003e2\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/sup\u003e= partial eta squared. AIM = Acceptability of Intervention Measure. FIM = Feasibility of Intervention Measure. IAM = Intervention Appropriateness measure. PROMIS-SD = PROMIS Sleep Disruption. PROMIS-SD = PROMIS Sleep Disturbance. PROMIS-SRI = PROMIS Sleep-Related Impairment. SHC = Sleep Health Composite (note, scored such that higher scores indicate better sleep health). DSM-5 = DSM-5 Cross-Cutting. SDS = Sheehan Disability Scale. \u003csup\u003eD\u0026nbsp;\u003c/sup\u003e= Differences when including TSC condition (Standard vs. Adapted) and provider degree as covariates instead of county, all comparisons become less significant. AIM predicting sleep disturbance changes from significant to non-significant (\u003cem\u003eb\u0026nbsp;\u003c/em\u003e= -5.74, \u003cem\u003eSE\u0026nbsp;\u003c/em\u003e= 4.31, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.19, \u003cem\u003en\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e\u003csup\u003e2\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/sup\u003e= 0.04). FIM predicting sleep health composite (\u003cem\u003eb\u0026nbsp;\u003c/em\u003e= 0.64, \u003cem\u003eSE\u0026nbsp;\u003c/em\u003e= 0.50, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.21, \u003cem\u003en\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e\u003csup\u003e2\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/sup\u003e= 0.04) and functional impairment (\u003cem\u003eb\u0026nbsp;\u003c/em\u003e= -1.33, \u003cem\u003eSE\u0026nbsp;\u003c/em\u003e= 1.39, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.35, \u003cem\u003en\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e\u003csup\u003e2\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/sup\u003e= 0.02) changes from marginally significant to non-significant. FIM predicting psychiatric symptoms changes from significant to marginally significant (\u003cem\u003eb\u0026nbsp;\u003c/em\u003e= -4.12, \u003cem\u003eSE\u0026nbsp;\u003c/em\u003e= 2.31, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.08, \u003cem\u003en\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e\u003csup\u003e2\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/sup\u003e= 0.07). IAM predicting sleep disturbance changes from significant to marginally significant (\u003cem\u003eb\u0026nbsp;\u003c/em\u003e= -4.93, \u003cem\u003eSE\u0026nbsp;\u003c/em\u003e= 2.84, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.09, \u003cem\u003en\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e\u003csup\u003e2\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/sup\u003e= 0.06).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"implementation-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"imps","sideBox":"Learn more about [Implementation Science](http://implementationscience.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/IMPS/default.aspx","title":"Implementation Science","twitterHandle":"@ImplementSci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"community mental health, train-the-trainer, facilitation, adaptation, i-PARIHS, mental illness, sleep, circadian, insomnia, transdiagnostic, psychosis, depression, anxiety disorder, bipolar disorder","lastPublishedDoi":"10.21203/rs.3.rs-6414484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6414484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground.\u003c/strong\u003e\u003c/em\u003e Grounded in the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, we investigated the Train-the-Trainer (TTT) to expand access to evidence-based psychological treatments (EBPTs) in community mental health centers (CMHCs), focusing on the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods. \u003c/strong\u003e\u003c/em\u003eEight Californian counties were cluster-randomized to Standard TSC or an adapted version designed to improve the “fit” of TSC to CMHCs. University-based trainers trained CMHC providers (\"Generation 1 providers\") in either Adapted or Standard TSC. These trained providers were then trained to become local CMHC trainers (“Generation 1 trainers”), who then trained a new cohort of providers (“Generation 2 providers”) in TSC. Within each county, patients diagnosed with serious mental illness (SMI) were randomized to receive either immediate TSC or usual care and delayed treatment with TSC (UC-DT) from the Generation 2 providers (“Generation 2 patients”). This study focused on 53 Generation 2 providers (Adapted TSC = 47; Standard TSC = 6), and 143 Generation 2 patients (Adapted TSC = 127; Standard TSC = 16) (the larger Adapted sample was driven by recruitment, perhaps reflecting preference for the “fitted” approach). Patient assessments were conducted pre-treatment, post-treatment, and six-month follow-up (6FU). Provider assessments occurred after completing TSC training and post-treatment for each patient treated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults. \u003c/strong\u003e\u003c/em\u003eCombining Adapted and Standard, TSC was associated with improvements for Generation 2 patients from pre- to post-treatment in sleep disturbance (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, \u003cem\u003ed = \u003c/em\u003e-0.90), sleep-related impairment (\u003cem\u003ep \u003c/em\u003e= 0.001,\u003cem\u003e d = \u003c/em\u003e-0.69), psychiatric symptoms (\u003cem\u003ep \u003c/em\u003e= 0.002, \u003cem\u003ed\u003c/em\u003e\u003csup\u003e \u003c/sup\u003e= -0.48), and functional impairment (\u003cem\u003ep \u003c/em\u003e= 0.002, \u003cem\u003ed\u003c/em\u003e = -0.54), relative to UC-DT. The effects of sleep disturbance and impairment on the relationship between treatment condition (TSC vs. UC-DT) and psychiatric symptoms and functional impairment were significant. Higher provider perception of TSC fit predicted improvements in selected patient outcomes.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion.\u003c/strong\u003e TSC can be delivered by CMHC providers trained by local CMHC trainers with strong outcomes. These data contribute to the dearth of evidence for TTT collected from locally trained providers and from patients treated by local CMHC trainers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e\u0026nbsp;Clinicaltrials.gov identifier: NCT05805657. Registered on March 10, 2023.\u003c/p\u003e\n\u003cp\u003ehttps://clinicaltrials.gov/ct2/show/NCT05805657\u003c/p\u003e","manuscriptTitle":"A randomized trial of Adapted versus Standard versions the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TSC) implemented via facilitation and delivered by community mental health providers using train-the-trainer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 11:40:44","doi":"10.21203/rs.3.rs-6414484/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-07-22T13:01:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-10T08:22:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-01T03:33:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Implementation Science","date":"2025-06-27T19:40:56+00:00","index":"","fulltext":""},{"type":"decision","content":"Major revision","date":"2025-04-26T01:00:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"implementation-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"imps","sideBox":"Learn more about [Implementation Science](http://implementationscience.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/IMPS/default.aspx","title":"Implementation Science","twitterHandle":"@ImplementSci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e23d4cd9-c28c-4e73-961c-f3cce5b1df5f","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:02:31+00:00","versionOfRecord":{"articleIdentity":"rs-6414484","link":"https://doi.org/10.1186/s13012-025-01467-y","journal":{"identity":"implementation-science","isVorOnly":false,"title":"Implementation Science"},"publishedOn":"2025-11-29 15:56:57","publishedOnDateReadable":"November 29th, 2025"},"versionCreatedAt":"2025-07-14 11:40:44","video":"","vorDoi":"10.1186/s13012-025-01467-y","vorDoiUrl":"https://doi.org/10.1186/s13012-025-01467-y","workflowStages":[]},"version":"v1","identity":"rs-6414484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6414484","identity":"rs-6414484","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}