Outpatient Mental Health Care Utilization among Frontline Healthcare Workers

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L. Bautista, K. A. Bourassa, N. N. Vasquez, A. Madan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6666192/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Healthcare workers are at high risk for burnout. Burnout can exacerbate anxiety, depression, and other psychiatric concerns, leading to impaired functioning and negative outcomes for healthcare workers, patients, and healthcare systems. Many workplace factors can contribute to burnout including high patient volumes, personal health risks, exposure to trauma, and scheduling and staffing issues. Mental health treatments such as Acceptance and Commitment Therapy (ACT) and Cognitive Behavioral Therapy (CBT) are effective for addressing burnout, but these psychotherapies can be difficult for healthcare workers to access due to stigma, cost, and scheduling challenges. To address these barriers, a large hospital system supported the creation of an outpatient mental health clinic to offer medication management and psychotherapy services without copays for employees and their dependents. After being in operation for three years, data are now available on patterns of psychiatric distress and mental health care utilization in this population. This paper provides an overview of these patterns in the patient population, as well as discussion of lessons learned, future challenges, and recommendations for continuing to help healthcare workers who are experiencing burnout and psychiatric distress. Biological sciences/Psychology Health sciences/Health care frontline healthcare workers burnout mental health healthcare utilization Introduction Physicians, nurses, and other healthcare workers are at high risk of developing burnout, a state of emotional exhaustion, negative attitudes, and perceived low achievement brought on by work-related stress (Maslach, 2003 ). In the field of healthcare, workplace stress often includes high patient volumes and pressure to see more patients, staffing shortages, exposure to traumatic and stressful events, and personal health risks (Adriaenssens, De Gucht, & Maes, 2015 ). As healthcare workers reach a critical mass of burnout, they leave the field, creating even greater staffing shortages and strain on the system. Over time, this has weakened healthcare systems globally just as healthcare costs are rising, leading to less access to care, longer wait times, and worsening patient outcomes (Salyers et al., 2016; Jun, Ojemeni, Kalamani, Tong, & Crecelius, 2021 ). As such, burnout has become a widespread crisis impacting not only healthcare workers, but also patients, the healthcare system, and communities. Psychological treatments such as Cognitive Behavioral Therapy (CBT) and mindfulness-based interventions are effective in treating burnout (Melnyk et al., 2020 ). Treatments targeting depression and anxiety, which are often associated with burnout, are also strongly supported by research (e.g., Munder et al., 2019 ). However, healthcare workers tend to underutilize mental health services (Papa, 2025 ). In addition to the typical barriers (e.g., cost/insurance, availability of providers), healthcare workers may also face challenges such as stigma, fears related to licensing and employment, and navigating long shifts and variable work schedules. Traditional Employee Assistance Programs (EAP) are widely available, but outcomes are mixed both in terms of symptom reduction and organizational return on investment (Csiernik, Cavell, & Csiernik, 2021 ). Given these findings and the observed needs of a large hospital system, an outpatient mental health clinic was created to expand the continuum of mental services to meet the needs of hospital employees and their dependents while reducing common barriers to care. For example, the self-insured health plan covers the cost of psychotherapy and medication management without a copay, and the course of treatment is based on clinical need rather than a pre-determined number of sessions. Appointments are offered virtually and in-person, and clinic hours are flexible to accommodate as many scheduling needs as possible, including early evening sessions. Additionally, extra layers of protection are in place to ensure privacy and confidentiality, such that employees’ records are not accessible by providers outside of the clinic. Finally, as an effort to combat stigma surrounding mental health care, multiple levels of hospital leadership have communicated their support for employees making use of the services. The present study aims to: 1) describe a sample of healthcare workers who sought care in the clinic, 2) characterize their presenting concerns, and 3) explore patterns of mental healthcare utilization of the population as a whole and between demographic groups within the sample. The findings will highlight areas of need within this population and inform novel solutions for increasing engagement with mental healthcare among this high-needs, low-utilization group. Method Participants and Setting Data were collected from Fall 2021 – Winter 2024 from treatment-seeking patients in an outpatient clinic that serves the needs of a large hospital system in the southwestern United States. Patients were eligible for care if they were an employee within the system or dependent on the health plan, were at least 18 years of age, and were able to provide informed consent. Only those identified as frontline healthcare workers were included in the present study (N = 481), which represents 40.3% of the total patient population seen in the clinic during the study period. Procedure The study procedure followed that of Bautista and colleagues (2024). Data on treatment utilization and psychiatric symptoms were abstracted from the medical record. The study was approved by the Institutional Review Board of Houston Methodist (IRB: MODCR00000028). All procedures were done in accordance with the Declaration of Helsinki. Given that the procedure involved only secondary data analysis, written informed consent was waived. Utilization data were obtained from patient enrollment records maintained by clinic staff and review of the medical records of individuals who expressed interest in clinic services. Data included appointments scheduled, cancelled, missed, and type of appointment (e.g., medication management, individual psychotherapy). Patient-reported outcomes (PROs), data on sociodemographic characteristics, and psychiatric conditions were collected via self-report questionnaires as standard of care. All patients who scheduled an intake appointment in the clinic were provided with a PROs questionnaire packet to complete prior to their initial visit. Patients had the option to complete PROs via: 1) a link to a secure electronic data collection platform managed by the hospital system, 2) an editable pdf packet sent electronically, or 3) hardcopy forms. Two primary measures of interest (Patient Health Questionnaire-8 and Generalized Anxiety Disorder-7) were selected for the study, given their representation of common mental health concerns and impact of symptoms on functioning. Abstracted utilization and psychiatric symptoms data were collated into a single electronic document. Data were stored on a secured shared drive accessible only to clinic staff. The primary authors coded all data. They reviewed clinic enrollment records and selected those whose profession was a frontline healthcare worker for inclusion in the study. “Frontline healthcare worker” was defined as any role that directly supports patient care. Positions that support employees or serve only administrative functions were not included (e.g., human resources, IT). The primary authors then reviewed PROs and medical records for each participant to code information related to sociodemographic background (i.e., age, race, ethnicity, profession), psychiatric diagnosis, and service utilization to characterize the population. Codes for psychiatric diagnosis and service utilization were as follows. Psychiatric diagnosis was obtained from the “visit diagnosis” and/or identified by the presenting concern listed in the intake assessment notes. All active diagnoses were included. Diagnoses were categorized according to the DSM 5, with an additional “other diagnosis” category reflecting psychosocial stressors or “not otherwise specified” diagnoses. Service utilization data (i.e., visit type and status of visit) were abstracted from the clinic and medical records. The visit types of interest for the present study were medication management and individual psychotherapy. Visit status included: 1) scheduled, 2) completed, 3) cancelled, and 4) no showed. The visit status for each visit for each patient was tallied by the primary authors. Total scores for each PROs questionnaire were calculated to reflect symptom severity. Measures The following measures were selected from the PROs battery for the current study. Intake Questionnaire. An intake questionnaire was developed for use in the clinic to obtain sociodemographic information and psychiatric history. Patient Health Questionnaire – 8 (PHQ-8). The PHQ-8 is an 8-item measure of depression severity over the past two weeks that has been modified from the original 9-item questionnaire to remove the suicidality question (Kroenke & Spitzer, 2002 ). Generalized Anxiety Disorder Questionnaire – 7 (GAD-7). The GAD-7 is a 7-item measure of anxiety severity over the past two weeks (Spitzer et al., 2006 ). Data Analysis Data were analyzed using SPSS version 29 (IBM, 2022). Descriptive statistics were used to characterize the patient population and utilization of services. Of note, demographic characteristics were reported based on patient responses to initial clinic paperwork, which used open-ended questions rather than multiple choice response options. For this reason, race and ethnicity could not be separated based on the responses provided. Additionally, non-responses are captured using “Unknown/Declined to Answer” for all variables except average age, which was calculated after excluding individuals who did not provide a date of birth. Finally, PHQ-8 and GAD-7 scores were available for about half of the sample ( n = 224) due to inconsistent clinic procedures for collecting the measures. This process was standardized in 2023, and collection became more consistent, meaning that the scores reported below over-represent patients who began treatment in 2023 or later. Data analysis followed the procedure described by Bautista and colleagues (Bautista et al., 2024 ). To describe and compare patterns of treatment utilization, a “percentage of sessions attended” variable was computed to capture the average appointment attendance for each of the treatment modalities. Given the wide range in the number of sessions scheduled per patient, quartiles were used as cut points (25, 50, 75) to divide the sample into groups of non-utilizers, and low, moderate, and high service utilization. The bottom quartiles for both medication management and psychotherapy were made up of patients with zero appointments in that category (i.e., non-utilizers), so the low, moderate, and high utilization groups are made up of Q2-Q4. These groups were used to explore whether attendance rate was affected by level of service utilization. A series of one-way ANOVAs were conducted to explore the effect of race/ethnicity, gender, sexual orientation, age, and utilization on average session attendance for psychotherapy and medication management services. Post-hoc Tukey HSD tests were conducted to explore group differences in average attendance. Non-responses (i.e., “Unknown/Declined to Answer”) were treated as missing data and not included in the analyses. Additionally, given the small number of patients who identified as Native American ( n = 2) and Hawaiian/Pacific Islander ( n = 1) and non-binary ( n = 1), these patients were not included in analyses. Sexual orientations were collapsed into fewer groupings given the presence of several self-identified labels with n < 3. Specifically, bisexual and “mostly straight” were combined, and pansexual, asexual, demisexual, and “exploring” were combined for analyses. Full demographic details are included in Table 1 . Table 1 Sample Demographics. Variable n % Gender Identity (n = 480) Cisgender Woman 398 82.7 Cisgender Man 81 16.8 Non-binary 1 0.2 Race/Ethnicity (n = 461) Asian 58 12.1 Black 112 23.3 Hawaiian/Pacific Islander 1 0.2 Hispanic 111 23.1 Multiracial 16 3.2 Native American 2 0.4 White 161 33.5 Sexual Orientation (n = 354) Heterosexual 300 84.7 “Mostly straight” 2 0.4 Gay/lesbian 27 5.6 Bisexual/“mostly straight” 20 4.1 Pansexual 2 0.4 Demisexual 1 0.2 Asexual 1 0.2 “Exploring” 1 0.2 Age ( n = 471) M = 38.9 Range = 20–74 Note. These data represent the full details of patients’ questionnaire responses. Some analyses used collapsed groupings as described in the text. Results During the study period, 481 healthcare workers requested mental health services. The average age was 38.9 (range = 20–74) and the majority identified as female ( n = 398, 82.7%) and heterosexual ( n = 300; 84.7%). The sample was diverse in terms of race/ethnicity, with about one third identifying as White ( n = 161), one quarter identifying as Black ( n = 116), and one quarter identifying as Hispanic/Latino ( n = 111). See Table 1 for full demographic details. Some healthcare workers who requested services in the clinic were never scheduled ( n = 19), and some were scheduled but never completed an appointment ( n = 5). Among the patients who were seen for at least an intake evaluation ( n = 457), depressive disorders ( n = 239; 52.3%) and anxiety disorders ( n = 198; 43.3%) were the most common diagnoses at intake. See Table 2 for full diagnostic details. Table 2 Presenting Problems. Diagnosis n % Depressive disorder 239 52.3 Anxiety disorder 198 43.3 Adjustment disorder 105 23.0 PTSD or related disorder 69 15.1 Neurodevelopmental disorder 52 11.4 Substance use disorder 17 3.7 Eating disorder 13 2.82.1 Bipolar spectrum disorder 11 2.4 Psychotic disorder 3 0.7 Personality disorder 3 0.7 Obsessive-compulsive spectrum disorder 2 0.4 Notes. Total adds up to greater than 100% for diagnosis as some patients presented with multiple diagnoses. Mean scores for both the PHQ-8 ( M = 11.45; SD = 6.21) and GAD-7 ( M = 11.92; SD = 5.86) were both in the moderate range, and approximately one third of the sample reported moderately severe or severe symptoms. See Table 3 . Table 3 Patient-Reported Outcome Scores PHQ-9 ( n = 224) n % GAD-7 ( n = 222) n % Minimal 34 15.2 Minimal 23 10.4 Mild 56 25.0 Mild 61 27.5 Moderate 60 26.8 Moderate 53 23.9 Moderately Severe 53 23.7 -- -- - Severe 21 9.3 Severe 85 38.3 Service Utilization Individual Psychotherapy . The majority of patients were scheduled for at least one individual psychotherapy appointment ( n = 338; 70.27%) and completed at least one psychotherapy appointment ( n = 322; 66.9%). Of those who began psychotherapy, the average number of scheduled sessions was 23.3 (SD = 23.5) and the average number of completed sessions was 16.8 (SD = 17.8). Overall attendance rate for psychotherapy was 72.2%. See Table 4 for full breakdown of psychotherapy session attendance. Attendance rate did not vary by sexual orientation F (3,250) = 0.23, p = 0.90 or gender F (1,334) = 3.42, p = 0.07. Age was not significantly correlated with average session attendance, r (334) = 0.06, p = 0.26. Attendance did vary by race/ethnicity F (4,322) = 3.32, p = 0.01, such that patients who identified as Black attended a lower percentage of sessions on average ( M = 60.93, SD = 25.48) compared to those who identified as Asian ( M = 73.47, SD = 18.52), p = 0.03, or White ( M = 70.52, SD = 20.46), p = 0.03. Table 4 Service Utilization by Service Type. Medication Management Individual Therapy Number of patients 340 338 Scheduled Appointments (M, SD) 12.2 (17.4) 23.3 (23.5) Completed Appointments (M, SD) 9.0 (13.9) 16.8 (17.8) Cancelled Appointments (M, SD) 2.4 (3.2) 4.8 (5.0) No Showed Appointments (M, SD) 0.8 (1.7) 1.7 (2.8) Attendance rate (%) 74.0 72.2 Medication Management. Most patients were scheduled for at least one medication management appointment ( n = 340, 70.69%) and completed at least one medication management appointment ( n = 330; 68.6%). Approximately half of patients ( n = 237) engaged in both psychotherapy and medication management. Of those who began medication management, the average number of scheduled sessions was 12.2 (SD = 17.4) and the average number of completed sessions was 9.0 (SD = 13.9). The average attendance rate was 74.0%. See Table 4 for full breakdown of medication management session attendance. Attendance rate did not vary by sexual orientation F (3,251) = 0.11, p = 0.10, gender F (1,354) = 0.55), p = 0.46, or race/ethnicity F(4,342) = 1.06, p = 0.38. Age was not significantly correlated with average session attendance r (353) = 0.08, p = 0.14. Discussion The present study described the psychiatric needs of a representative sample of frontline healthcare workers and their patterns of mental health care utilization. The sample’s gender identity and sexual orientation makeup were similar to national averages for healthcare workers (HRSA, 2024) and the racial/ethnic diversity reflected that of the Houston area. Healthcare workers received more sessions than typically offered by EAP and insurance-based mental healthcare, likely due to the lack of session limits and copays which are common barriers to care. Average attendance rates were lower than rates found in general outpatient healthcare globally (Dantas, Fleck, Oliveira, & Hamacher, 2018 ), but similar to or slightly higher than rates found in other specialty mental healthcare settings (e.g., Childs 2021; Milicevic et al., 2020 ). Notably, healthcare workers who identified as Black attended psychotherapy sessions at a lower rate than their Asian and White colleagues. Overall, the present findings reflect some success in engaging frontline healthcare workers in mental health care and highlight the need for continued improvement. Although healthcare workers tend to underutilize mental health services in general (Papa, 2025 ), the present sample’s attendance rates were similar to the general population. Thus, the clinic’s efforts to overcome common barriers to care may have compensated for the expected pattern of underutilization, but the rate of missed appointments continues to have negative impacts on patient outcomes, waiting times, and costs for the organization. Additionally, the finding that Black healthcare workers had lower psychotherapy attendance rates may reflect unique barriers to engagement in psychotherapy services among this group despite the clinic’s efforts to deliver services in a manner that increases access. Further assessment of these patient-level factors is needed to optimize accessibility and acceptability of mental health services for all healthcare workers. Healthcare workers who sought care in the clinic presented with moderate to severe symptoms of depression and anxiety. This is consistent with the level of symptom severity reported in other treatment studies of healthcare workers (Ward et al., 2023 ). Future research is needed to examine whether symptom severity is associated with treatment utilization among this population, and to explore how symptom severity impacts self-selection in types of mental health services. For example, Houston Methodist offers tiers of mental health support to their staff, ranging from informal peer support and supportive rounding to formal outpatient mental health care, partial hospitalization programming, and inpatient mental health services (Bourassa et al., 2024). It is possible that staff with less psychiatric distress may select more informal support mechanisms while those in severe distress may seek higher levels of care elsewhere. Understanding the specific treatment needs and barriers to care of healthcare workers who experience severe levels of distress is necessary to refine clinical programming and increase access to care. The findings of the current study have significance for the broader healthcare industry. First, results suggest that efforts to remove barriers to care are successful in increasing healthcare workers’ engagement with mental health services, and that additional solutions are needed to fully meet the needs of this at-risk group. While the present study was descriptive, other recent work has demonstrated that promoting access to low-cost mental health services for healthcare workers is associated with reduced psychiatric distress, increased staff tenure, and organizational cost savings (Ward et al., 2023 ). Patients in the study by Ward and colleagues were offered six free psychotherapy and/or medication management appointments (Ward et al., 2023 ). In the present study, most staff participated in significantly more psychotherapy sessions in the clinic ( M = 16.83) than available within the mental health service delivery models traditionally offered by healthcare organizations (i.e., EAP, insurance-based care). As Houston Methodist is self-insured, the clinic can offer services with no insurance pre-authorization, copay, or pre-determined session limits. It can be hypothesized that this service model allowed staff to engage in a personalized treatment plan that more fully addressed their needs as compared to traditional mental health services. This further underscores the benefit of providing covered, in-house mental health services. This model of service delivery may be suitable for a range of healthcare organizations, facilitate the delivery of tailored services for this unique population, and remove common barriers to care. Given the potential benefit to absenteeism and turnover associated with addressing staff mental health (e.g., Ward et al., 2023 ), investing in the creation of in-house services may provide cost-savings to healthcare organizations. Furthermore, as psychiatric distress among healthcare workers is associated with poor patient care outcomes (Salyers et al., 2016), addressing the mental health needs of staff is foundational to successful and sustainable healthcare systems. Limitations and Future Directions The present study relied on archival data collected as part of the clinic’s routine practice, which limits specificity on some variables (e.g., race and ethnicity), and does not allow for collection of some relevant data points (e.g., reasons for missed appointments). Another limitation is the lack of follow-up scores for the PHQ-8 and GAD-7, which would provide information about the effectiveness of mental health services for healthcare workers in the clinic. As mentioned previously, logistical challenges have limited the consistency of data collection and management in the clinic to date. However, a digital solution has since been implemented and a more complete picture of patient-reported outcomes at intake and follow-up appointments is forthcoming. Finally, the present results represent employees of a single hospital system and may not be generalizable to healthcare workers across all regions of the United States. Future research is needed to explore the long-term outcomes of engagement in mental health care among frontline healthcare staff. Furthermore, additional research exploring the relationship among psychiatric symptoms, burnout, treatment engagement, and metrics of tenure and patient care quality is needed to demonstrate the impact of mental health care on individual and system-level outcomes. Moreover, research that explores the barriers and facilitators to care access, engagement, and treatment preferences among the diverse patient population in EHWC will support the development of more fully patient-centered services. Declarations Funding Declaration The authors declare no competing interests. Author Contribution C.B. and K.B. extracted and collated the data and wrote the main manuscript text. N.V. assisted with data analysis. A.M. assisted with procuring archival data and preparing the manuscript. Data Availability The datasets generated and/or analysed during the current study are not publicly available due to institutional policy at Houston Methodist but are available from the corresponding author on reasonable request. References Adriaenssens, J., De Gucht, V. & Maes, S. Determinants and prevalence of burnout in emergency nurses: A systematic review of 25 years of research. Int. J. Nurs. Stud. 52 (2), 649–661 (2015). Bautista, C. L. et al. Nursing staff in a large hospital system underutilize insurance-based mental health services. In Healthcare (Vol. 12, No. 12, 1188). MDPI. (2024). Dantas, L. F., Fleck, J. L., Oliveira, F. L. C. & Hamacher, S. No-shows in appointment scheduling–a systematic literature review. Health Policy . 122 (4), 412–421 (2018). Childs, A. W. et al. Showing up is half the battle: The impact of telehealth on psychiatric appointment attendance for hospital-based intensive outpatient services during COVID-19. Telemedicine e-Health . 27 (8), 835–842 (2021). Csiernik, R., Cavell, M. & Csiernik, B. EAP evaluation 2010–2019: What do we now know? J. Workplace Behav. Health . 36 (2), 105–124 (2021). Health Resources and Services Administration. State of the health workforce report 2024 . U.S. Department of Health and Human Services. (2024). https://bhw.hrsa.gov/sites/default/files/bureau-health-workforce/state-of-the-health-workforce-report-2024.pdf IBM Statistics for Windows. Version 29 (IBM Corp, 2022). Jun, J., Ojemeni, M. M., Kalamani, R., Tong, J. & Crecelius, M. L. Relationship between nurse burnout, patient and organizational outcomes: Systematic review. Int. J. Nurs. Stud. 119 , 103933 (2021). Kroenke, K. & Spitzer, R. L. The PHQ-9: A new depression diagnostic and severity measure. Psychiatric Annals . 32 (9), 509–515 (2002). Maslach, C. Burnout: The Cost of Caring (Malor Books, 2003). Melnyk, B. M. et al. Interventions to improve mental health, well-being, physical health, and lifestyle behaviors in physicians and nurses: a systematic review. Am. J. Health Promotion . 34 (8), 929–941 (2020). Milicevic, A. S. et al. Modeling patient no-show history and predicting future appointment behavior at the veterans administration’s outpatient mental health clinics: NIRMO-2. Mil. Med. 185 (7–8), e988–e994 (2020). Munder, T. et al. Is psychotherapy effective? A re-analysis of treatments for depression. Epidemiol. Psychiatric Sci. 28 (3), 268–274 (2019). Papa, A. Gaps in Mental Health Care–Seeking Among Health Care Providers During the COVID-19 Pandemic—United States, September 2022–May 2023. MMWR. Morbidity and Mortality Weekly Report , 74 . (2025). Salyers, M. P. et al. The relationship between professional burnout and quality and safety in healthcare: a meta-analysis. J. Gen. Intern. Med. 32 , 475–482 (2017). Spitzer, R. L., Kroenke, K., Williams, J. B. & Löwe, B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern. Med. 166 (10), 1092–1097 (2006). Ward, E. J. et al. Assessing the impact of comprehensive mental health program on frontline health service workers. PLoS ONE . 18 (11), e0294414 (2023). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Bautista","email":"data:image/png;base64,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","orcid":"","institution":"Houston Methodist","correspondingAuthor":true,"prefix":"","firstName":"C.","middleName":"L.","lastName":"Bautista","suffix":""},{"id":527064673,"identity":"1e7cc822-5631-4a6c-bf5c-02709f119a66","order_by":1,"name":"K. A. 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Madan","email":"","orcid":"","institution":"Houston Methodist","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"","lastName":"Madan","suffix":""}],"badges":[],"createdAt":"2025-05-14 16:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6666192/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6666192/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93189305,"identity":"2ec1b1e6-0a4d-462c-b3c4-2eb120ca4908","added_by":"auto","created_at":"2025-10-10 03:43:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63962,"visible":true,"origin":"","legend":"","description":"","filename":"HealthcareWorkerUtilizationScientificReports.docx","url":"https://assets-eu.researchsquare.com/files/rs-6666192/v1/0d370056b882502e27223c55.docx"},{"id":93189669,"identity":"0d0e51b9-24ac-4d3b-a17b-567d08e78ee9","added_by":"auto","created_at":"2025-10-10 03:51:44","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5740,"visible":true,"origin":"","legend":"","description":"","filename":"7f407440cdc240518581a05c53d1874d.json","url":"https://assets-eu.researchsquare.com/files/rs-6666192/v1/220eb476f351138e4612644d.json"},{"id":93189670,"identity":"232b4294-bae0-4bf9-badc-9a19c10ab715","added_by":"auto","created_at":"2025-10-10 03:51:44","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69313,"visible":true,"origin":"","legend":"","description":"","filename":"7f407440cdc240518581a05c53d1874d1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-6666192/v1/abea65c2ebc8bc7600d6ab86.xml"},{"id":93189309,"identity":"4d793dbe-3ec5-4408-a21a-c951336a304c","added_by":"auto","created_at":"2025-10-10 03:43:44","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66435,"visible":true,"origin":"","legend":"","description":"","filename":"7f407440cdc240518581a05c53d1874d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-6666192/v1/1aec449a1a4f1f41e9b6d067.xml"},{"id":93189306,"identity":"09a1f0bb-f383-4b36-a28e-a8bedd144391","added_by":"auto","created_at":"2025-10-10 03:43:44","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74845,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6666192/v1/cd100e623368f13c858736eb.html"},{"id":98598558,"identity":"af3024c4-10fa-43c3-a3c3-fb873414df7a","added_by":"auto","created_at":"2025-12-19 12:09:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":580780,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6666192/v1/87aa4f80-7293-4e3c-8570-ca21565330ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Outpatient Mental Health Care Utilization among Frontline Healthcare Workers","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhysicians, nurses, and other healthcare workers are at high risk of developing burnout, a state of emotional exhaustion, negative attitudes, and perceived low achievement brought on by work-related stress (Maslach, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In the field of healthcare, workplace stress often includes high patient volumes and pressure to see more patients, staffing shortages, exposure to traumatic and stressful events, and personal health risks (Adriaenssens, De Gucht, \u0026amp; Maes, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As healthcare workers reach a critical mass of burnout, they leave the field, creating even greater staffing shortages and strain on the system. Over time, this has weakened healthcare systems globally just as healthcare costs are rising, leading to less access to care, longer wait times, and worsening patient outcomes (Salyers et al., 2016; Jun, Ojemeni, Kalamani, Tong, \u0026amp; Crecelius, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As such, burnout has become a widespread crisis impacting not only healthcare workers, but also patients, the healthcare system, and communities.\u003c/p\u003e\u003cp\u003ePsychological treatments such as Cognitive Behavioral Therapy (CBT) and mindfulness-based interventions are effective in treating burnout (Melnyk et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Treatments targeting depression and anxiety, which are often associated with burnout, are also strongly supported by research (e.g., Munder et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, healthcare workers tend to underutilize mental health services (Papa, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition to the typical barriers (e.g., cost/insurance, availability of providers), healthcare workers may also face challenges such as stigma, fears related to licensing and employment, and navigating long shifts and variable work schedules. Traditional Employee Assistance Programs (EAP) are widely available, but outcomes are mixed both in terms of symptom reduction and organizational return on investment (Csiernik, Cavell, \u0026amp; Csiernik, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven these findings and the observed needs of a large hospital system, an outpatient mental health clinic was created to expand the continuum of mental services to meet the needs of hospital employees and their dependents while reducing common barriers to care. For example, the self-insured health plan covers the cost of psychotherapy and medication management without a copay, and the course of treatment is based on clinical need rather than a pre-determined number of sessions. Appointments are offered virtually and in-person, and clinic hours are flexible to accommodate as many scheduling needs as possible, including early evening sessions. Additionally, extra layers of protection are in place to ensure privacy and confidentiality, such that employees\u0026rsquo; records are not accessible by providers outside of the clinic. Finally, as an effort to combat stigma surrounding mental health care, multiple levels of hospital leadership have communicated their support for employees making use of the services. The present study aims to: 1) describe a sample of healthcare workers who sought care in the clinic, 2) characterize their presenting concerns, and 3) explore patterns of mental healthcare utilization of the population as a whole and between demographic groups within the sample. The findings will highlight areas of need within this population and inform novel solutions for increasing engagement with mental healthcare among this high-needs, low-utilization group.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and Setting\u003c/h2\u003e\u003cp\u003eData were collected from Fall 2021 \u0026ndash; Winter 2024 from treatment-seeking patients in an outpatient clinic that serves the needs of a large hospital system in the southwestern United States. Patients were eligible for care if they were an employee within the system or dependent on the health plan, were at least 18 years of age, and were able to provide informed consent. Only those identified as frontline healthcare workers were included in the present study (N\u0026thinsp;=\u0026thinsp;481), which represents 40.3% of the total patient population seen in the clinic during the study period.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eThe study procedure followed that of Bautista and colleagues (2024). Data on treatment utilization and psychiatric symptoms were abstracted from the medical record. The study was approved by the Institutional Review Board of Houston Methodist (IRB: MODCR00000028). All procedures were done in accordance with the Declaration of Helsinki. Given that the procedure involved only secondary data analysis, written informed consent was waived.\u003c/p\u003e\u003cp\u003eUtilization data were obtained from patient enrollment records maintained by clinic staff and review of the medical records of individuals who expressed interest in clinic services. Data included appointments scheduled, cancelled, missed, and type of appointment (e.g., medication management, individual psychotherapy).\u003c/p\u003e\u003cp\u003ePatient-reported outcomes (PROs), data on sociodemographic characteristics, and psychiatric conditions were collected via self-report questionnaires as standard of care. All patients who scheduled an intake appointment in the clinic were provided with a PROs questionnaire packet to complete prior to their initial visit. Patients had the option to complete PROs via: 1) a link to a secure electronic data collection platform managed by the hospital system, 2) an editable pdf packet sent electronically, or 3) hardcopy forms. Two primary measures of interest (Patient Health Questionnaire-8 and Generalized Anxiety Disorder-7) were selected for the study, given their representation of common mental health concerns and impact of symptoms on functioning.\u003c/p\u003e\u003cp\u003eAbstracted utilization and psychiatric symptoms data were collated into a single electronic document. Data were stored on a secured shared drive accessible only to clinic staff. The primary authors coded all data. They reviewed clinic enrollment records and selected those whose profession was a frontline healthcare worker for inclusion in the study. \u0026ldquo;Frontline healthcare worker\u0026rdquo; was defined as any role that directly supports patient care. Positions that support employees or serve only administrative functions were not included (e.g., human resources, IT). The primary authors then reviewed PROs and medical records for each participant to code information related to sociodemographic background (i.e., age, race, ethnicity, profession), psychiatric diagnosis, and service utilization to characterize the population.\u003c/p\u003e\u003cp\u003eCodes for psychiatric diagnosis and service utilization were as follows. Psychiatric diagnosis was obtained from the \u0026ldquo;visit diagnosis\u0026rdquo; and/or identified by the presenting concern listed in the intake assessment notes. All active diagnoses were included. Diagnoses were categorized according to the DSM 5, with an additional \u0026ldquo;other diagnosis\u0026rdquo; category reflecting psychosocial stressors or \u0026ldquo;not otherwise specified\u0026rdquo; diagnoses. Service utilization data (i.e., visit type and status of visit) were abstracted from the clinic and medical records. The visit types of interest for the present study were medication management and individual psychotherapy. Visit status included: 1) scheduled, 2) completed, 3) cancelled, and 4) no showed. The visit status for each visit for each patient was tallied by the primary authors. Total scores for each PROs questionnaire were calculated to reflect symptom severity.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eThe following measures were selected from the PROs battery for the current study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntake Questionnaire.\u003c/b\u003e An intake questionnaire was developed for use in the clinic to obtain sociodemographic information and psychiatric history.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatient Health Questionnaire \u0026ndash; 8 (PHQ-8).\u003c/b\u003e The PHQ-8 is an 8-item measure of depression severity over the past two weeks that has been modified from the original 9-item questionnaire to remove the suicidality question (Kroenke \u0026amp; Spitzer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeneralized Anxiety Disorder Questionnaire \u0026ndash; 7 (GAD-7).\u003c/b\u003e The GAD-7 is a 7-item measure of anxiety severity over the past two weeks (Spitzer et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS version 29 (IBM, 2022). Descriptive statistics were used to characterize the patient population and utilization of services. Of note, demographic characteristics were reported based on patient responses to initial clinic paperwork, which used open-ended questions rather than multiple choice response options. For this reason, race and ethnicity could not be separated based on the responses provided. Additionally, non-responses are captured using \u0026ldquo;Unknown/Declined to Answer\u0026rdquo; for all variables except average age, which was calculated after excluding individuals who did not provide a date of birth. Finally, PHQ-8 and GAD-7 scores were available for about half of the sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;224) due to inconsistent clinic procedures for collecting the measures. This process was standardized in 2023, and collection became more consistent, meaning that the scores reported below over-represent patients who began treatment in 2023 or later.\u003c/p\u003e\u003cp\u003eData analysis followed the procedure described by Bautista and colleagues (Bautista et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To describe and compare patterns of treatment utilization, a \u0026ldquo;percentage of sessions attended\u0026rdquo; variable was computed to capture the average appointment attendance for each of the treatment modalities. Given the wide range in the number of sessions scheduled per patient, quartiles were used as cut points (25, 50, 75) to divide the sample into groups of non-utilizers, and low, moderate, and high service utilization. The bottom quartiles for both medication management and psychotherapy were made up of patients with zero appointments in that category (i.e., non-utilizers), so the low, moderate, and high utilization groups are made up of Q2-Q4. These groups were used to explore whether attendance rate was affected by level of service utilization. A series of one-way ANOVAs were conducted to explore the effect of race/ethnicity, gender, sexual orientation, age, and utilization on average session attendance for psychotherapy and medication management services. Post-hoc Tukey HSD tests were conducted to explore group differences in average attendance. Non-responses (i.e., \u0026ldquo;Unknown/Declined to Answer\u0026rdquo;) were treated as missing data and not included in the analyses. Additionally, given the small number of patients who identified as Native American (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) and Hawaiian/Pacific Islander (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) and non-binary (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1), these patients were not included in analyses. Sexual orientations were collapsed into fewer groupings given the presence of several self-identified labels with \u003cem\u003en\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;3. Specifically, bisexual and \u0026ldquo;mostly straight\u0026rdquo; were combined, and pansexual, asexual, demisexual, and \u0026ldquo;exploring\u0026rdquo; were combined for analyses. Full demographic details are included in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample Demographics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender Identity\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;480)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCisgender Woman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCisgender Man\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-binary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace/Ethnicity\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;461)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHawaiian/Pacific Islander\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiracial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNative American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSexual Orientation\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;354)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeterosexual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ldquo;Mostly straight\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGay/lesbian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBisexual/\u0026ldquo;mostly straight\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePansexual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemisexual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsexual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ldquo;Exploring\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;471)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u0026thinsp;=\u0026thinsp;38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRange\u0026thinsp;=\u0026thinsp;20\u0026ndash;74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e These data represent the full details of patients\u0026rsquo; questionnaire responses. Some analyses used collapsed groupings as described in the text.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDuring the study period, 481 healthcare workers requested mental health services. The average age was 38.9 (range\u0026thinsp;=\u0026thinsp;20\u0026ndash;74) and the majority identified as female (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;398, 82.7%) and heterosexual (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;300; 84.7%). The sample was diverse in terms of race/ethnicity, with about one third identifying as White (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;161), one quarter identifying as Black (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;116), and one quarter identifying as Hispanic/Latino (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;111). See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for full demographic details.\u003c/p\u003e\u003cp\u003eSome healthcare workers who requested services in the clinic were never scheduled (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19), and some were scheduled but never completed an appointment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5). Among the patients who were seen for at least an intake evaluation (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;457), depressive disorders (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;239; 52.3%) and anxiety disorders (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;198; 43.3%) were the most common diagnoses at intake. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for full diagnostic details.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePresenting Problems.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepressive disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjustment disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTSD or related disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurodevelopmental disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubstance use disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEating disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.82.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBipolar spectrum disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychotic disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersonality disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObsessive-compulsive spectrum disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNotes.\u003c/em\u003e Total adds up to greater than 100% for diagnosis as some patients presented with multiple diagnoses.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMean scores for both the PHQ-8 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.45; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.21) and GAD-7 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.92; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.86) were both in the moderate range, and approximately one third of the sample reported moderately severe or severe symptoms. See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient-Reported Outcome Scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHQ-9\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;224)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGAD-7\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;222)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMinimal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerately Severe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSevere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eService Utilization\u003c/h2\u003e\u003cp\u003e\u003cem\u003eIndividual Psychotherapy\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe majority of patients were scheduled for at least one individual psychotherapy appointment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;338; 70.27%) and completed at least one psychotherapy appointment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;322; 66.9%). Of those who began psychotherapy, the average number of scheduled sessions was 23.3 (SD\u0026thinsp;=\u0026thinsp;23.5) and the average number of completed sessions was 16.8 (SD\u0026thinsp;=\u0026thinsp;17.8). Overall attendance rate for psychotherapy was 72.2%. See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for full breakdown of psychotherapy session attendance. Attendance rate did not vary by sexual orientation \u003cem\u003eF\u003c/em\u003e(3,250)\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90 or gender \u003cem\u003eF\u003c/em\u003e(1,334)\u0026thinsp;=\u0026thinsp;3.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07. Age was not significantly correlated with average session attendance, \u003cem\u003er\u003c/em\u003e(334)\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26. Attendance did vary by race/ethnicity \u003cem\u003eF\u003c/em\u003e(4,322)\u0026thinsp;=\u0026thinsp;3.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, such that patients who identified as Black attended a lower percentage of sessions on average (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;60.93, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25.48) compared to those who identified as Asian (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;73.47, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18.52), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, or White (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;70.52, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20.46), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eService Utilization by Service Type.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedication Management\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndividual Therapy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScheduled Appointments\u003c/p\u003e\u003cp\u003e(M, SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.2 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.3\u003c/p\u003e\u003cp\u003e(23.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompleted Appointments\u003c/p\u003e\u003cp\u003e(M, SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003cp\u003e(13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.8 (17.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancelled\u003c/p\u003e\u003cp\u003eAppointments\u003c/p\u003e\u003cp\u003e(M, SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003cp\u003e(3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003cp\u003e(5.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Showed\u003c/p\u003e\u003cp\u003eAppointments\u003c/p\u003e\u003cp\u003e(M, SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003cp\u003e(1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003cp\u003e(2.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttendance rate (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMedication Management.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMost patients were scheduled for at least one medication management appointment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;340, 70.69%) and completed at least one medication management appointment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;330; 68.6%). Approximately half of patients (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;237) engaged in both psychotherapy and medication management. Of those who began medication management, the average number of scheduled sessions was 12.2 (SD\u0026thinsp;=\u0026thinsp;17.4) and the average number of completed sessions was 9.0 (SD\u0026thinsp;=\u0026thinsp;13.9). The average attendance rate was 74.0%. See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for full breakdown of medication management session attendance. Attendance rate did not vary by sexual orientation \u003cem\u003eF\u003c/em\u003e(3,251)\u0026thinsp;=\u0026thinsp;0.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10, gender \u003cem\u003eF\u003c/em\u003e(1,354)\u0026thinsp;=\u0026thinsp;0.55), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46, or race/ethnicity F(4,342)\u0026thinsp;=\u0026thinsp;1.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.38. Age was not significantly correlated with average session attendance \u003cem\u003er\u003c/em\u003e(353)\u0026thinsp;=\u0026thinsp;0.08, p\u0026thinsp;=\u0026thinsp;0.14.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study described the psychiatric needs of a representative sample of frontline healthcare workers and their patterns of mental health care utilization. The sample\u0026rsquo;s gender identity and sexual orientation makeup were similar to national averages for healthcare workers (HRSA, 2024) and the racial/ethnic diversity reflected that of the Houston area. Healthcare workers received more sessions than typically offered by EAP and insurance-based mental healthcare, likely due to the lack of session limits and copays which are common barriers to care. Average attendance rates were lower than rates found in general outpatient healthcare globally (Dantas, Fleck, Oliveira, \u0026amp; Hamacher, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), but similar to or slightly higher than rates found in other specialty mental healthcare settings (e.g., Childs 2021; Milicevic et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, healthcare workers who identified as Black attended psychotherapy sessions at a lower rate than their Asian and White colleagues.\u003c/p\u003e\u003cp\u003e Overall, the present findings reflect some success in engaging frontline healthcare workers in mental health care and highlight the need for continued improvement. Although healthcare workers tend to underutilize mental health services in general (Papa, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the present sample\u0026rsquo;s attendance rates were similar to the general population. Thus, the clinic\u0026rsquo;s efforts to overcome common barriers to care may have compensated for the expected pattern of underutilization, but the rate of missed appointments continues to have negative impacts on patient outcomes, waiting times, and costs for the organization. Additionally, the finding that Black healthcare workers had lower psychotherapy attendance rates may reflect unique barriers to engagement in psychotherapy services among this group despite the clinic\u0026rsquo;s efforts to deliver services in a manner that increases access. Further assessment of these patient-level factors is needed to optimize accessibility and acceptability of mental health services for all healthcare workers.\u003c/p\u003e\u003cp\u003eHealthcare workers who sought care in the clinic presented with moderate to severe symptoms of depression and anxiety. This is consistent with the level of symptom severity reported in other treatment studies of healthcare workers (Ward et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Future research is needed to examine whether symptom severity is associated with treatment utilization among this population, and to explore how symptom severity impacts self-selection in types of mental health services. For example, Houston Methodist offers tiers of mental health support to their staff, ranging from informal peer support and supportive rounding to formal outpatient mental health care, partial hospitalization programming, and inpatient mental health services (Bourassa et al., 2024). It is possible that staff with less psychiatric distress may select more informal support mechanisms while those in severe distress may seek higher levels of care elsewhere. Understanding the specific treatment needs and barriers to care of healthcare workers who experience severe levels of distress is necessary to refine clinical programming and increase access to care.\u003c/p\u003e\u003cp\u003eThe findings of the current study have significance for the broader healthcare industry. First, results suggest that efforts to remove barriers to care are successful in increasing healthcare workers\u0026rsquo; engagement with mental health services, and that additional solutions are needed to fully meet the needs of this at-risk group. While the present study was descriptive, other recent work has demonstrated that promoting access to low-cost mental health services for healthcare workers is associated with reduced psychiatric distress, increased staff tenure, and organizational cost savings (Ward et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Patients in the study by Ward and colleagues were offered six free psychotherapy and/or medication management appointments (Ward et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the present study, most staff participated in significantly more psychotherapy sessions in the clinic (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.83) than available within the mental health service delivery models traditionally offered by healthcare organizations (i.e., EAP, insurance-based care). As Houston Methodist is self-insured, the clinic can offer services with no insurance pre-authorization, copay, or pre-determined session limits. It can be hypothesized that this service model allowed staff to engage in a personalized treatment plan that more fully addressed their needs as compared to traditional mental health services. This further underscores the benefit of providing covered, in-house mental health services. This model of service delivery may be suitable for a range of healthcare organizations, facilitate the delivery of tailored services for this unique population, and remove common barriers to care. Given the potential benefit to absenteeism and turnover associated with addressing staff mental health (e.g., Ward et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), investing in the creation of in-house services may provide cost-savings to healthcare organizations. Furthermore, as psychiatric distress among healthcare workers is associated with poor patient care outcomes (Salyers et al., 2016), addressing the mental health needs of staff is foundational to successful and sustainable healthcare systems.\u003c/p\u003e\n\u003ch3\u003eLimitations and Future Directions\u003c/h3\u003e\n\u003cp\u003eThe present study relied on archival data collected as part of the clinic\u0026rsquo;s routine practice, which limits specificity on some variables (e.g., race and ethnicity), and does not allow for collection of some relevant data points (e.g., reasons for missed appointments). Another limitation is the lack of follow-up scores for the PHQ-8 and GAD-7, which would provide information about the effectiveness of mental health services for healthcare workers in the clinic. As mentioned previously, logistical challenges have limited the consistency of data collection and management in the clinic to date. However, a digital solution has since been implemented and a more complete picture of patient-reported outcomes at intake and follow-up appointments is forthcoming. Finally, the present results represent employees of a single hospital system and may not be generalizable to healthcare workers across all regions of the United States.\u003c/p\u003e\u003cp\u003eFuture research is needed to explore the long-term outcomes of engagement in mental health care among frontline healthcare staff. Furthermore, additional research exploring the relationship among psychiatric symptoms, burnout, treatment engagement, and metrics of tenure and patient care quality is needed to demonstrate the impact of mental health care on individual and system-level outcomes. Moreover, research that explores the barriers and facilitators to care access, engagement, and treatment preferences among the diverse patient population in EHWC will support the development of more fully patient-centered services.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Declaration\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.B. and K.B. extracted and collated the data and wrote the main manuscript text. N.V. assisted with data analysis. A.M. assisted with procuring archival data and preparing the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to institutional policy at Houston Methodist but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdriaenssens, J., De Gucht, V. \u0026amp; Maes, S. Determinants and prevalence of burnout in emergency nurses: A systematic review of 25 years of research. \u003cem\u003eInt. J. Nurs. Stud.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e (2), 649\u0026ndash;661 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBautista, C. L. et al. Nursing staff in a large hospital system underutilize insurance-based mental health services. In Healthcare (Vol. 12, No. 12, 1188). MDPI. (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDantas, L. F., Fleck, J. L., Oliveira, F. L. C. \u0026amp; Hamacher, S. No-shows in appointment scheduling\u0026ndash;a systematic literature review. \u003cem\u003eHealth Policy\u003c/em\u003e. \u003cb\u003e122\u003c/b\u003e (4), 412\u0026ndash;421 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChilds, A. W. et al. Showing up is half the battle: The impact of telehealth on psychiatric appointment attendance for hospital-based intensive outpatient services during COVID-19. \u003cem\u003eTelemedicine e-Health\u003c/em\u003e. \u003cb\u003e27\u003c/b\u003e (8), 835\u0026ndash;842 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCsiernik, R., Cavell, M. \u0026amp; Csiernik, B. EAP evaluation 2010\u0026ndash;2019: What do we now know? \u003cem\u003eJ. Workplace Behav. Health\u003c/em\u003e. \u003cb\u003e36\u003c/b\u003e (2), 105\u0026ndash;124 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHealth Resources and Services Administration. \u003cem\u003eState of the health workforce report 2024\u003c/em\u003e. U.S. Department of Health and Human Services. (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bhw.hrsa.gov/sites/default/files/bureau-health-workforce/state-of-the-health-workforce-report-2024.pdf\u003c/span\u003e\u003cspan address=\"https://bhw.hrsa.gov/sites/default/files/bureau-health-workforce/state-of-the-health-workforce-report-2024.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIBM Statistics for Windows. \u003cem\u003eVersion 29\u003c/em\u003e (IBM Corp, 2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJun, J., Ojemeni, M. M., Kalamani, R., Tong, J. \u0026amp; Crecelius, M. L. Relationship between nurse burnout, patient and organizational outcomes: Systematic review. \u003cem\u003eInt. J. Nurs. Stud.\u003c/em\u003e \u003cb\u003e119\u003c/b\u003e, 103933 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKroenke, K. \u0026amp; Spitzer, R. L. The PHQ-9: A new depression diagnostic and severity measure. \u003cem\u003ePsychiatric Annals\u003c/em\u003e. \u003cb\u003e32\u003c/b\u003e (9), 509\u0026ndash;515 (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaslach, C. \u003cem\u003eBurnout: The Cost of Caring\u003c/em\u003e (Malor Books, 2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMelnyk, B. M. et al. Interventions to improve mental health, well-being, physical health, and lifestyle behaviors in physicians and nurses: a systematic review. \u003cem\u003eAm. J. Health Promotion\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e (8), 929\u0026ndash;941 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMilicevic, A. S. et al. Modeling patient no-show history and predicting future appointment behavior at the veterans administration\u0026rsquo;s outpatient mental health clinics: NIRMO-2. \u003cem\u003eMil. Med.\u003c/em\u003e \u003cb\u003e185\u003c/b\u003e (7\u0026ndash;8), e988\u0026ndash;e994 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMunder, T. et al. Is psychotherapy effective? A re-analysis of treatments for depression. \u003cem\u003eEpidemiol. Psychiatric Sci.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (3), 268\u0026ndash;274 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePapa, A. Gaps in Mental Health Care\u0026ndash;Seeking Among Health Care Providers During the COVID-19 Pandemic\u0026mdash;United States, September 2022\u0026ndash;May 2023. \u003cem\u003eMMWR. Morbidity and Mortality Weekly Report\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e. (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalyers, M. P. et al. The relationship between professional burnout and quality and safety in healthcare: a meta-analysis. \u003cem\u003eJ. Gen. Intern. Med.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 475\u0026ndash;482 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpitzer, R. L., Kroenke, K., Williams, J. B. \u0026amp; L\u0026ouml;we, B. A brief measure for assessing generalized anxiety disorder: the GAD-7. \u003cem\u003eArch. Intern. Med.\u003c/em\u003e \u003cb\u003e166\u003c/b\u003e (10), 1092\u0026ndash;1097 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWard, E. J. et al. Assessing the impact of comprehensive mental health program on frontline health service workers. \u003cem\u003ePLoS ONE\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e (11), e0294414 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"frontline healthcare workers, burnout, mental health, healthcare utilization","lastPublishedDoi":"10.21203/rs.3.rs-6666192/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6666192/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHealthcare workers are at high risk for burnout. Burnout can exacerbate anxiety, depression, and other psychiatric concerns, leading to impaired functioning and negative outcomes for healthcare workers, patients, and healthcare systems. Many workplace factors can contribute to burnout including high patient volumes, personal health risks, exposure to trauma, and scheduling and staffing issues. Mental health treatments such as Acceptance and Commitment Therapy (ACT) and Cognitive Behavioral Therapy (CBT) are effective for addressing burnout, but these psychotherapies can be difficult for healthcare workers to access due to stigma, cost, and scheduling challenges. To address these barriers, a large hospital system supported the creation of an outpatient mental health clinic to offer medication management and psychotherapy services without copays for employees and their dependents. After being in operation for three years, data are now available on patterns of psychiatric distress and mental health care utilization in this population. This paper provides an overview of these patterns in the patient population, as well as discussion of lessons learned, future challenges, and recommendations for continuing to help healthcare workers who are experiencing burnout and psychiatric distress.\u003c/p\u003e","manuscriptTitle":"Outpatient Mental Health Care Utilization among Frontline Healthcare Workers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 03:43:39","doi":"10.21203/rs.3.rs-6666192/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c030972-ab19-4bd1-8f37-8a90699be649","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56022404,"name":"Biological sciences/Psychology"},{"id":56022405,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-12-19T12:08:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 03:43:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6666192","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6666192","identity":"rs-6666192","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}