Early Matched Care for High-Risk Injured Postal Workers: A Replication Study

Early Matched Care for High-Risk Injured Postal Workers: A Replication Study | 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 Early Matched Care for High-Risk Injured Postal Workers: A Replication Study Michael Nicholas, Melanie Ianssen, Daniel Costa, Manassi Mittinty, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9422704/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Purpose Psychosocial factors have been identified as risks for delayed recovery after work injuries but results from interventions for high-risk cases have been mixed. An earlier Australian study that matched care to assessed psychosocial risk factors with injured workers showed better return to work outcomes over 2 years than the usual care control, but as the study was not randomized it needed replication in another population. The present study, using the same protocol as the earlier study, but with injured workers from a different industry and different providers, attempted to replicate the earlier findings. Methods A 2-group prospective design was used, with participants being allocated to Intervention or Control groups based on their home state of Australia. High-risk cases were identified within 5-10 days of injury. Those in the Intervention states were offered a coordinated, multidisciplinary intervention that targeted both identified psychological and social/workplace risk factors. Participants from both groups received usual care as required, including surgery, from their healthcare providers. Results After 1-year, time to return to pre-injury work hours, total costs, and medical costs were significantly less in the Intervention group, relative to the Controls. Having surgery was associated with more lost time from work, but surgical cases in the Intervention group had significantly less lost time than the surgical cases in the Controls. Conclusions These results replicate those of the earlier study and indicate that long-term disability from work injuries can be prevented by matching psychosocial interventions to identified psychosocial risk factors. Having surgery was associated with higher costs and delayed recovery, but did not alter the psychosocial findings. Trial Registration . The study was registered prospectively with the Australian and New Zealand Clinical Trials Registry (ACTRN12620000041954). Psychosocial screening Workers’ compensation Work injury Early intervention Figures Figure 1 Figure 2 Figure 3 Introduction In Australia, average national return to work (RTW) rates after workplace injuries have remained at around 80% work for the last decade, with recent figures even indicating a decline in this rate [1,2]. The costs associated with the delayed RTW can be substantial, both financially and in terms of quality of life for the individuals concerned, their employers, and society at large [3]. Reducing lost time from work after injury has been a major focus for occupational researchers. Two major lines of research findings have emerged. Firstly, prospective studies have repeatedly confirmed that psychological and social/environmental factors are strong predictors of delayed recovery and RTW following workplace injuries [4,5,6,7]. Secondly, despite expectations that interventions targeting those deemed at risk due to psychosocial factors as early as possible would improve disability outcomes, the results have often been mixed [8]. Croft et al. [8] considered possible explanations for these disappointing results in studies of stratified care. Chief amongst these was the problem of implementing stratified care protocols in clinical practice, especially when it involved changes in clinician behaviour. But one explanation not canvassed was the nature of the interventions employed in the stratified care studies. Specifically, the stratified interventions were largely based on the risk category an injured person fell into (high, medium or low) according to their scores on the self-report StartBack tool (SBT) [9]. However, the interventions offered were not individually tailored to the specific concerns of each patient. An alternative approach to those identified at high risk of delayed RTW due to psychosocial factors was described by Linton et al. [10] as ‘matched care’. As in stratified care, matched care involves screening injured workers with a psychosocial risk scale, but then having a psychologist assess each high-risk worker to clarify their concerns and relevant contextual factors. This enables an intervention to be tailored to the assessed needs of the high-risk group, rather than according to their risk group. A systematic review of studies of interventions aimed at improving RTW outcomes after injury by Cullen et al. [11] concluded that improved outcomes for injured workers were more likely if interventions entailed a comprehensive, biopsychosocial approach involving treatments coordinated with the workplace. In contrast, the stratified care studies reviewed by Croft et al. [8] do not appear to have been individually tailored and nor did they address possible workplace and coordinated care issues, although Delitto et al. [12] acknowledged the problem. A coordinated and individually matched care approach was reported by Nicholas et al. [13] who found lost time from work in the following 2 years after injury was half that of a usual (stepped) care control group. However, as the experimental design was not randomized, it is possible the findings were due to chance and replication was essential to confirm the findings. The present study with recently injured workers uses the same basic protocol employed by Nicholas et al. [13] but in another industry and using different service providers. The primary outcome was time to return to pre-injury work hours over the following year. Methods Australia Post (AP) is an Australian Government-owned enterprise providing postal services for the whole country (of 6 states and 2 territories), with a total workforce of >34,000. The workforce comprises around 60% males, with 46% aged between 30 and 50 years. The nature of the work is varied, ranging from clerical, office-based roles to more physical mail sorting, and delivering parcels or letters by truck, motor bikes and on foot in a wide range of geographical locations. Commensurate with this breakdown, a consistent contributor to workplace injuries for AP is motor accidents and surgery is often required in these cases. To make the study as representative as possible of the injured workforce all those with a workplace injury, including those requiring surgery, were invited to participate. It was felt this broad inclusion approach would provide the study with what might be termed ‘ecological validity’. Before the study commenced in early 2020, the RTW outcomes for AP had been consistently around 90%, which was markedly better than the national average at the time of around 80% [1]. Importantly, AP had already adopted an in-house rehabilitation model that was focused on early intervention and the health benefits of work. However, psychosocial screening to identify those likely to have delayed recovery was not done routinely and decision-making by AP’s rehabilitation providers was often based on personal judgement and lack of expected progress. In preparation for the study and consistent with recommendations in the occupational health literature [14], a period of consultation with key stakeholder groups within AP nationally (across all states) was undertaken by the AP rehabilitation team. This preparatory phase included education about the evidence for psychosocial risk factors and their impact on recovery from injury. Consultation groups included: senior and operational management, injured workers, Work Rehabilitation Providers (WRPs), claims delegates, union representatives (CEPU), human resources members, allied health professionals and other treatment providers. The study was named EMCAP (Early Matched Care, Australia Post). The protocol was based on that used in the WISE study but tailored, by discussion with the AP rehabilitation managers, to fit within the normal business practices of the employer, AP. As in the WISE study, EMCAP entailed comparing a coordinated approach to injured AP workers who had been identified by a brief psychological screening instrument (Örebro Musculoskeletal Pain Screening Questionnaire-Short Form - ÖMPSQ-SF) [15] as at high risk for lost time from work. The intervention was intended to target both psychological and social/workplace risk factors identified at the individual level in addition to usual care by the treating doctors and physiotherapists. The psychological interventions matched to the identified risk factors for each worker were provided by psychologists and counsellors employed as Employee Assistance Providers (EAPs) by Converge, a company contracted to AP to provide Employee Assistance Provider (EAP) services. Unlike the WISE study, which was conducted within one state, EMCAP was conducted across all six Australian States and participation in the intervention or control condition depended upon state of residence as random assignment would have been too complicated for the state-based rehabilitation teams to manage the different protocols at the same worksites. To balance the distribution of cases, the two most populous states (New South Wales and Victoria) were assigned to different groups, with Victoria becoming an intervention state and NSW a control. The next most populous (Queensland) was designated as a control, and the rest (comprising the more sparsely populated states of Tasmania, South Australia, and Western Australia) were designated as intervention states. All these decisions were made by the AP management team, and the external researchers played no part. All AP workers across the country with new (non-psychological) injury claims were to be offered screening by the Workplace Rehabilitation Providers (WRPs) with the ÖMPSQ-SF after informed consent for screening was obtained. Informed consent included permitting the research team to follow their RTW progress over the following year. Screening was expected to occur within the first 5 days after the injury/incident report. To ensure the study was as ecologically valid as possible, no injury site or cause (apart from psychological injury claims) was to be excluded and even those offered surgery for their workplace injury were to be included provided they gave informed consent. 2.1 Participants and pain sites Study participants were recruited by WRPs from consecutive injured AP workers who made a work-related (non-psychological) injury claim. The severity of injury was not a selection criterion, providing the worker was able to communicate sufficiently in English with the WRP. WRPs from both Intervention and Control groups were given training by webinar by the lead researcher (MN) in ways of introducing the ÖMPSQ-SF to the injured workers. The steps taken for the consenting workers are presented graphically in Figure 1. Once a worker was identified as being in the high-risk category, and living in an intervention state, their WRP explained the nature of the study to them individually to obtain informed consent for participation in the intervention protocol. Once this was provided, each worker’s General Practitioner (GP), Case Manager (CM) and workplace supervisor (team leader) were advised of the risk status of the injured worker and the protocol for their management was activated. This entailed a meeting between the worker’s GP, the worker, the WRP, and the psychologist/counsellor (who attended by telephone). Those who declined to participate received usual care (like the Control group) but were out of the study as their risk status was no longer blinded to the WRP managing the case. For the Control group, the completed screening instrument was scored by the Research Manager (MM). The WRP, General Practitioner (GP), other treatment providers, Case Manager (CM) and workplace remained blinded to the risk status of these injured workers. All participating (high and low risk) workers from both groups received usual care under the federal Government’s workers compensation scheme (Comcare). 2.2 Specific Hypotheses tested 2.2.1 Compared to those in the Control group, time to return to pre-injury hours of work (which is when wage replacements are no longer provided) would be significantly less for the Intervention group. These differences were expected whether surgery was undertaken or not. A recent national survey of injured workers found only 53% reported working at their pre-injury hours after RTW, which is lower than the overall RTW rate of 78%, and represents a higher goal to achieve than simply RTW.[2] 2.2.2 The mean costs of claims (for lost time and medical treatments) would be less for the Intervention group. 2.2.3 Additional, secondary outcomes were time to return to pre-injury duties and time to return to work (any time), as well as incapacity and medical costs, which were expected to also favour the Intervention group. 2.2.4 Consistent with the hypothesis that improved psychological risk factors (as assessed by the self-report ÖMPSQ-SF, mood, pain, interference, and pain beliefs measures) would be associated with improved RTW outcomes, psychological risk factors were also assessed pre- and post-the psychological interventions. But these were only assessed for the Intervention group to ensure blinding of the Control group’s risk status was not inadvertently revealed. 2.3 Inclusion/exclusion criteria Participants had to be AP workers who had reported a physical, work-related injury (other than primary psychological injuries) between January 2020 and May 2021) and had made a workers compensation insurance claim. They also had to have taken, or had been approved for, medically-sanctioned time off work due to their injury. Injured workers recommended for surgical interventions were also able to participate and were included in the study if they consented. In addition, only those screened as having a high psychological risk for delayed recovery (ÖMPSQ-SF > 48/100), and being within four weeks of the injury date, were included in the study. Exclusion criteria Prospective participants were excluded if they had made a psychological injury claim, or scored <48 on the ÖMPSQ-SF. Those who declined to participate after having the study explained to them were also excluded. 2.4 Ethics approval and prospective trial registration Ethical approval for the study was obtained from the University of Sydney Human Research Ethics Committee (2019/976). The study was registered prospectively with the Australian and New Zealand Clinical Trials Registry (ACTRN12620000041954). 2.5 Study Design A controlled, non-randomised, prospective design was used (Fig 1) using an Intervention and Control group. Assignment of workers to Intervention or Control groups was based on which state they lived in. The states were chosen by the Rehabilitation team at AP in a manner aimed at balancing the workforce numbers across those states overall. The outcomes of high-risk workers from the Intervention states (Victoria, South Australia, Western Australia, and Tasmania) were compared with the outcomes of similar high-risk workers from the Control states (New South Wales and Queensland). In both the Intervention and Control states the rehabilitation teams subscribed to the value of early intervention and the same treatment options were available in all states if requested by WRPs or treatment providers. The main differences between the two conditions were the known risk status and agreed matched care management protocol for high-risk cases in the Intervention states. Data on work status, lost days, and costs were maintained by the employer (who was also the insurer) as normal for a minimum of one year from the date of injury. 2.6 Sample size The sample size estimation for this study was based upon the WISE study [13], which compared a brief psychological intervention within a workplace and medical management protocol for recently injured workers employed by NSW Health vs standard care. It reported effect sizes of d = .39 for the primary outcome of work loss time, indicating that the intervention was effective. Based on this effect size, d = 0.39, an alpha of .05 and 80% power, a sample of N = 208 workers was calculated to be adequate (n = 104 per group) in the EMCAP study. 2.7 Protocol for Intervention Group As this study involved several stakeholders working in a coordinated way, the (abbreviated) roles of each are outlined here. The steps from initial recruitment are summarised in Figure 1. Note, the intervention for the consenting workers did not start until their case had been discussed at a case conference with the treating doctor (GP), the injured worker, their WRP, and the psychologist/counsellor (who attended by telephone). At this meeting the worker’s RTW obstacles were identified and the intervention plans agreed to by all those present. The implementation of the protocol by all stakeholders was monitored as closely as possible by the Research Manager (MM) and senior AP rehabilitation staff (LM) throughout the project to promote adherence. The psychologists/counsellors and WRPs in the Intervention arm of the study received training in the background of the protocol and its implementation for this study. This was provided over 1 day for the psychologists/counsellors via webinar by the lead investigator (MN). The training included extended discussion about the protocol, guidance in scoring the ÖMPSQ-SF and using the workers’ responses to clarify the nature of psychosocial contributors to high scores with the injured workers, exploring options for addressing the psychosocial risk factors, including basic pain self-management strategies, and a review of the WISE study experiences. Additional mentoring and psychology guidance for the psychologists/counsellors was provided as needed by the external clinical psychologist (TO), who had previously participated in the WISE study as a treating psychologist, along with periodic follow-up reviews with the participating psychologists/counsellors and WRPs by the lead investigator (MN). 2.7.1 Workplaces and work rehabilitation provider (WRP) Workplace interventions depended upon the RTW obstacles for the individual workers as identified by them, their Converge counsellor/psychologist and their WRP (after consultation with the workplace team leader). The WRP group had training in the use of the intervention protocol by the lead investigator (MN) with follow-up review sessions from time to time. Additional guidance for the WRPs was provided as needed by the external psychologist (TO). The WRPs met (face to face, except when limited by COVID-19 restrictions) with all high-risk workers within a week of the initial screening to recruit for the study. This included encouraging them to see the selected psychologist/counsellor within the next week for an assessment and possible treatment. The WRP was required to be in regular contact with the workers, as well as their GP and the treating psychologists/counsellors throughout their treatment. They were also expected to work closely with each worker’s workplace supervisor to assist the RTW processes as required. In addition to modifying work tasks and conditions, examples of the practical responses to the workers’ needs included arranging for them to see a dietician (if body weight was a concern, e.g., for those with lower limb injuries), support for attending exercise programs, as well as arranging for aids and equipment where needed (not just for work but also for personal/recreational functioning). 2.7.2 Psychologists/counsellors The participating psychologists/counsellors were employed by an external organisation (Converge) contracted to provide employee assistance (EAP) to AP. A specialist group within Converge was established for this project and this consisted of psychologists/counsellors who had a background or interest in occupational health. As mentioned earlier, they were provided with additional education about the health benefits of rehabilitation at work, including a discussion about the earlier WISE study and what was learnt from that. The training also included strategies for helping the injured workers to engage in managing their pain and other RTW obstacles. These strategies included the use of a case formulation approach to matching the interventions to the worker’s presenting problems and contributing factors, including activity goal setting, the use of activity pacing to reach those goals, relaxation methods, and problem management for issues like pain, stress, sleep disturbance, etc [16]. Recent evidence supporting this matched care approach was also provided as part of the training [6,13,10]. The psychologists/counsellors were required to agree to follow the intervention protocol. This included assessing the workers within a week of referral and to use the worker’s responses to items on the ÖMPSQ-SF to develop a case formulation followed by identification of likely obstacles to RTW. The psychologists/counsellors were expected to address all obstacles they considered within their scope of practice within six weekly sessions (one per week for six weeks). The psychologists/counsellors were also required to explain to the workers who agreed to participate that, unlike their normal role in Employee Assistance where the content of sessions were strictly confidential, in this study they were required, as appropriate, to share with the employer and WRP any issues raised by the injured worker that may be affecting their ability to recover and RTW. The help of the employer and WRP would be sought in resolving these issues, in collaboration with the injured workers. The injured workers were, however, assured that no confidential information concerning the worker would be passed to the employer or WRP. Accordingly, the psychologist/counsellors were expected to maintain regular contact with both the workplace (WRP) and treating doctor (GP). If a consenting high-risk worker was scheduled for surgery, the psychological treatment sessions would follow the worker’s recovery from surgery once the surgical team cleared them to proceed. 2.7.3. Nominated Treating Doctor (GP) – Primary Care General Medical Practitioners The GPs were chosen by the injured workers, as per normal for injured workers. The GPs were informed their patient was in an approved trial and details of the trial were provided by the workplace WRP. The GPs were free to pursue whichever medical treatment they judged appropriate, as per the usual Comcare Guidelines. 2.7.4. The (insurance) case manager (CM) The CM referred participating workers to nominated independent specialists (relevant to injury type) on an as needed basis as per normal practice, this was not included within the standard EMCAP protocol as it applied to the intervention and control groups alike. 2.7.5. Physiotherapists The physiotherapists providing care were selected by the workers’ GPs and reported to the WRP and GP on their progress. The physiotherapists treating Intervention workers were advised by the WRP that the worker was participating in a study aimed at facilitating early RTW. The physiotherapists were expected to have a good understanding of the importance of an activity-based approach to treatment. 2.8. Variation in Protocol during COVID-19 shutdowns The advent of COVID-19 affected both intervention and control states from about April 2020, but the impact varied according to state-based rules imposed by the different state governments. The strictest restrictions on movement were imposed in the state of Victoria (an Intervention state) and these restrictions meant that several changes in protocol for the study were required and were approved by the University of Sydney HREC. The main impact was that face-to-face counselling sessions had to be conducted online by videolinks. The extent to which these changes affected the study was unclear, but the impression from a review by the rehabilitation managers for AP did not reveal any obvious differences between face-to-face vs online sessions. However, there was no option at the time but to continue with the study to the best of the WRP and treatment providers’ abilities. Several recommendations had to be adapted during lockdowns, for example gym equipment was rented and delivered to a workers’ home and they were provided with telehealth sessions with an exercise physiologist as an alternative to a gym program. 2.9 Protocol for Control Group High and low risk workers in the two Control states received treatment as usual under the standard Comcare Guidelines that apply nationwide for AP. As noted earlier, this also meant the services of a psychologist or counsellor could have been sought by the WRP and GP if thought necessary. 3. Measures 3.1 Screening measure The ÖMPSQ-SF (Örebro Musculoskeletal Pain Screening Questionnaire – Short Form) [15] [contains 10 items, each scored on a 0-10 scale, to yield a possible score between 0 – 100. Workers who scored ≥ 48 (out of 100) were considered at high-risk of delayed recovery [6]. The score of 48 was used in this study, rather than the score of 50 in the WISE study, as 48 was found by Nicholas et al [6] as the optimal cut-off according to both Youden’s and the distance statistics. 3.2 Outcome measures 3.2.1 Days to pre-injury work hours (PIWH), operationalised as lost time from work (number of days reimbursed for missing pre-injury work hours) over 1 year from injury. The data on lost work time were obtained from the Australia Post Records. 3.2.2 Total claim costs . These data were also obtained from the Australia Post records. These encompasses all claim-related costs, including wage replacement, medical treatments, rehabilitation and matched care services. The costs of the psychologists/counsellors for the intervention were not included as they were already covered within the EAP contract with Australia Post and did not add to the costs of the intervention. It is important to note that the Control Group participants could also access the counsellors in their state if they wished or on recommendation of their WRP. The costs of medical treatments (including GP visits, specialists, allied health, independent medical assessments, surgery, hospitalisation) were also compared between the two groups. Additional cost measures included the mean Incapacity payments. These refer to the costs of wage replacement while an injured worker is not at work. 3.2.3 Supplementary data Acceptability of the intervention protocol for employer/ insurer . To evaluate the acceptability of the EMCAP protocol to key stakeholders, feedback was sought from injured workers treated within the EMCAP protocol, their GPs, WRPs, workplace managers, and psychologists/counsellors at the end of recruitment. 3.3 Psychological outcome measures The changes in psychological risk level following the psychological treatments were evaluated by the ÖMPSQ-SF administered to participants by the treating psychologists/counsellors before and after their treatments. Additional psychological outcome measures were also administered by the treating psychologists/counsellors at these times as per hypothesis 2.2.4: Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ-SF) [15]. The 10-item version of the original 24 item version comprises established psychosocial predictors of lost time from work after work injuries. It has been found to have good psychometric properties, and the total score has been found predictive of lost time from work after work injuries [6]. The Brief Pain Inventory (BPI) [17] includes two sub-scales: Pain Severity scale and Pain Interference scale. Both use a 0-10 scale, where higher scores indicate higher pain levels and higher pain interference in daily activities, respectively. The 21-item version of the Depression Anxiety Stress Scales (DASS) (Lovibond and Lovibond, 1995) [18], was used to measure severity of Depressive, Anxiety, and Stress symptoms. Each subscale score was doubled to provide a mean score between 0-42, with higher scores reflecting higher severity on each dimension. The Pain Self-Efficacy Questionnaire (PSEQ) (Nicholas, 2007) [19] measures the strength and generality of a person in pain’s beliefs about his/her ability to accomplish various activities, including work, despite their pain. Scores range from 0 to 60. Higher scores indicate stronger self-efficacy beliefs. One of the goals of the intervention was to achieve higher scores on this scale as they are associated with higher functional status. The Pain Catastrophising Scale (PCS) (Sullivan et al., 2005) [20] provides a measure of distressing thoughts about pain. Total scores range between 0-52, with higher scores indicating more frequent distressing thoughts. 4. Participants (see CONSORT Diagram, Fig 2) Of the 854 participants deemed eligible, 549 (64%) consented to screening, with 328 allocated to the Control states and 221 to the Intervention states. In the Control states, 192 (58%) of the 328 participants were classified as high risk and therefore eligible for inclusion in the study. In the Intervention states, 137 (61.9%) participants were classified as high risk, and of those, 78 (56.9%) consented to participate in the Intervention group. Finally, as three participants in each group lived in very remote areas (and not only had additional difficulties accessing health services, compared to those in more urban areas, supported and gradual return to work was more challenging and expensive to provide as they were the sole employee at these sites) they were excluded due to the expectation that they would not represent the types of rehabilitation problems and costs faced by their colleagues in more urban regions, irrespective of intervention. The final sample for analysis was, therefore, 189 in the Control group and 75 in the Intervention group, for a total sample of 264. The basic demographic features for the final sample are presented in Table 1. The proportions for the primary injury sites and/or diagnoses (where available) are provided in Table 2. Examination of Table 1 indicates the participants in both groups were roughly similar, as was their mean initial screening score on the ÖMPSQ-SF. Examination of Table 2 indicates that participating workers in the Intervention and Control groups were quite heterogeneous in their injuries and injury sites, with shoulder and lower back injury sites being the most common, with lower limb injuries next. The list for both groups was similar and included both fractures and soft tissue injuries. Table 1: Basic demographic information for both groups. Group Intervention Control Final Number 75 189 Mean age (yrs) 48.9 50.6 Gender (Males) 65% 72% Screening ÖMPSQ-SF (Mean, SD) 60.1 (8.2) 58.8 (7.5) Table 2. Primary injury sites/diagnoses for both groups Intervention (%) Control (%) Shoulder injury 20 (26.7) 56 (29.6) Low back injury 16 (21.3) 33 (17.5) Hip injury 4 (5.3) 2 (1.1) Hand/wrist injury 4 (5.3) 21 (11.1) Knee injury 6 (8) 14 (7.4) Lower limb fracture 8 (10.7) 18 (9.5) Lower limb soft tissue injury 6 (8) 20 (10.6) Elbow injury 0 2 (1.1) Neck injury 5 (6.7) 8 (4.2) Head injury/concussion 0 1 (0.5) Hernia 4 (5.3) 11 (5.8) Upper limb fracture 2 (2.7) 2 (1.1) Soft tissue upper limb injury 0 1 (0.5) 5. Primary and secondary outcome variables The primary outcome variable was number of days to return to pre-injury work hours. As those having surgery were included in the study, the days to return to pre-injury work hours were analysed for this cohort as well in both groups. Cost was a secondary outcome variable, and we analysed total cost as well as its breakdown into medical costs and incapacity payments (wage replacements). Days to pre-injury duties and days to return to work were also analysed as secondary outcomes. 6. Data analysis For the primary outcome, we used survival analysis to compare time to return to pre-injury hours between the Control (coded 0) and Intervention (coded 1) groups. We examined Kaplan-Meier curves for the two groups separately, and tested the group difference with Cox regression, using the survival (Therneau, 2023) [21] package in R. From the Cox regression we interpreted the hazard ratio, which represents the “risk” of return to pre-injury hours for the intervention group compared to the control group, such that a hazard ratio > 1 indicates earlier return to pre-injury hours for the Intervention than for the Control group. For the secondary outcomes, we also used survival analysis to compare days to pre-injury duties and days to return to work between the Intervention and Control groups. To compare costs between groups, we used linear regression with bootstrapped confidence intervals (95% bias-corrected, accelerated) to account for possible skew in cost data. In all analyses, we controlled for whether participants had surgery. The reason for this was that having surgery was expected to add to lost time from work. Participants who consented to participate in the Intervention group with Converge completed questionnaires (BPI, DASS, PSEQ, PCS) before and after the psychological component of the intervention, and we analysed these changes using paired-samples t -tests, as well as calculating standardised mean differences and their 95% confidence intervals. Results Primary Outcome : For return to pre-injury work hours, the proportional hazards assumption was satisfied for Intervention group, surgery, and the global model (Table 3). In the Cox regression (Table 4), intervention group was a significant predictor of return to pre-injury hours, with a hazard ratio of 1.45 indicating earlier return to pre-injury hours in the intervention than in the control group, while controlling for surgery. Median days to return to pre-injury hours was 79 for the intervention group (mean = 90.1) and 92 for the control group (mean = 121.9). Surgery was also a significant predictor of return to pre-injury hours, with a hazard ratio of 1.62 indicating earlier return to pre-injury hours in the no-surgery group than in the surgery group, while controlling for intervention group. Median days to return to pre-injury hours was 128 for those who had surgery (mean = 158.9) and 88 for those who did not (mean = 100.9). However, there was a difference between groups for those who had surgery (Table 5). Although the numbers available for analysis were small (12 in the Intervention and 18 in the Control), of those who had surgery in the Intervention group, median days to return to pre-injury hours was 89 (mean = 105.0) and 167 for the Control group (mean = 198.3). In the Cox regression analysis, the Intervention group was a significant predictor of return to pre-injury hours, with a hazard ratio of 2.26 indicating earlier return to pre-injury hours in the Intervention than in the Control ( p = .048). 7.1 Secondary outcomes: For the secondary return to work outcomes (return to pre-injury duties and return to work), the proportional hazards assumption was satisfied. For return to pre-injury duties, neither predictor (group - Intervention and Control = and surgery) was significant, and for return to work, group (intervention and Control) was not significant, but having surgery was a significant predictor of delayed RTW (see Table 4). 7.2 Cost outcomes: The results the linear regression analysis for total cost data are shown in Table 6. For the Intervention group, the mean total cost was $14,021.68 (median = $7,814.38) and for the Control group it was $20,601.99 (median = $13,463.89). In the regression model, predicted medical cost was significantly lower for the Intervention group than for the Control group. As might be expected, predicted total cost was also significantly lower for those who did not have surgery (mean = $11,500.36; median = $6,895.61) than for those who did (mean = $34,087.62; median = $26,897.64). The mean medical cost was $4,625.69 (median = $2,736.97) in the Intervention group and $8,226.32 (median = $4,559.80) in the Control group. In the regression model, predicted medical cost was significantly lower for the Intervention group than for the Control group. As might be expected, predicted medical cost was also significantly lower for those who did not have surgery (mean = $3,833.60, median = $2,758.67) than for those who did (mean = $14,216.97, median = $11,488.22). The mean incapacity payment was $8,772.95 (median = $4,548.86) in the Intervention group and $12,035.37 (median = $7,429.37) in the Control group. In the regression model, treatment group did not significantly predict incapacity payment. However, predicted incapacity payment was significantly lower for those who did not have surgery (mean = $7,387.59, median = $4,401.56) than for those who did (mean = $19,459.72, median = $12,176.69). 7.3 Self-report psychological scales The results for the pre-post intervention questionnaires are shown in Table 7. Pre-post changes on all outcome variables, except for DASS anxiety, were statistically significant, indicating there had been improvements on these domains following psychological treatments. The mean number of weekly psychology sessions was 5. 7.4 Acceptability of the intervention protocol for employer/ insurer . No formal evaluation was conducted, but informally the Australia Post Rehabilitation team did solicit and record feedback from injured workers who participated in the intervention, as well as WRPs, general practitioners, counsellors, psychologists, and managers. A selection of this feedback is presented in Figure 3. Overall, each stakeholder group reported a high degree of satisfaction with the intervention protocol over an extended period. This is consistent with AP deciding to implement the EMCAP protocol as ‘business as usual’ throughout Australia. This is still the case four years since the formal end of the study. Of interest here is that AP has reported their annual RTW rate has improved from 90% in 2020 to 98% over the year to March 2025. In contrast, the broader Comcare scheme (to which AP and other federal agencies belong) reported declining RTW rates through 2024 to average out at 81%. Discussion This trial has replicated the findings from the previous trial of a similar protocol in a different industry with different workers and different providers [13]. In this study, the primary RTW outcome, measured as time to return to pre-injury hours at work, was significantly less in the intervention group, relative to the control group. In addition, in this study the secondary outcome of total costs and medical costs were also significantly less for the intervention group, relative to the control group. While the other two secondary outcomes, time to return to pre-injury duties and days to RTW, favoured the intervention group, the differences were not statistically significant. In part, this probably reflects the above average RTW outcomes being achieved at the time by the usual care with this employer. Still, as the national RTW statistics indicate, return to pre-injury hours is the more challenging outcome [2]. A novel feature in this study was an evaluation of outcomes achieved by those who had surgery for their injuries. This cohort has often been excluded from intervention studies with musculoskeletal conditions. The results indicated that, not surprisingly, having surgery was associated with both significantly higher costs as well as a significantly longer time to return to pre-injury work hours, regardless of group assignment. However, between group analysis revealed that those in the Intervention group who had surgery returned to pre-injury hours significantly earlier than those in the control group. This is an important finding for both researchers and the occupational injury industry as it suggests better outcomes can be achieved after surgery in workers with high psychosocial risk levels, providing they receive help with those risk factors. Mirroring the previous study [13], the expected improvements in the psychological measures over the treatment period were also significant, apart from anxiety. However, inspection of the anxiety scores indicates these were in the normal range initially. The changes in the other psychological scales (Depressive symptoms, Pain severity and interference, catastrophizing and pain self-efficacy) align with the hypothesised basis for targeting these heightened psychological risk factors in the psychological interventions. Consistent with this hypothesis, we also found that following these interventions the mean score on the psychosocial screening questionnaire (ÖMPSQ-SF) had improved significantly and was well-below the baseline score as well as the initial cut-off score used to identify the participants as high risk (48/100). At post-treatment the mean pain self-efficacy (PSEQ) score was significantly higher than at baseline and this is consistent with other studies that found scores over 40/60 on this scale are associated with being at work despite persisting pain [19, 23, 24]. The characteristics of the participants should also be considered. On screening, the mean ÖMPSQ-SF scores of both high-risk groups were almost identical (60.3 vs 58.8 for the Intervention and the Controls, respectively). These are slightly below those reported for the WISE study, but that study used a cut-off of 50 (vs 48 in EMCAP) which may account for the difference. Comparing the mean psychological scale scores in EMCAP (Table 7) with published normative Australasian data from over 12,000 adults attending chronic pain services [25], reveals, not surprisingly, the mean EMCAP scores soon after injury were less severe than the chronic pain cohort. Even so, the EMCAP cohort was still worse than 25%-35% of the normative sample on these scales. In comparison with a non-clinical community sample, the EMCAP cohort was substantially more distressed on the three DASS scales [26]. Despite the recency of their injuries, it would seem the high-risk EMCAP participants were in trouble functionally and emotionally around the time of their injury or soon after. This speaks to the construct validity of the ÖMPSQ-SF. At the same time, the improvements on these scales over a mean of 5 weekly sessions of psychological treatment are consistent with the case made by Linton and Nicholas [27] for early intervention in high risk injured workers lest they drift towards higher levels of disability and distress like those seen in patients attending chronic pain services. These findings are also consistent with those reported in the systematic review by Cullen et al. [11] that indicated better RTW outcomes are more likely when the psychological treatment is linked closely to workplace engagement. In this study we would point to the preparation of the psychologists/counsellors and WRPs to address identified psychosocial barriers to RTW, as well as preparation of the key workplace stakeholders to promote their support and adherence to the implementation protocol for the intervention sites. This preparation should not be overlooked as it provides the context in which interventions take place and it has long been recommended in the occupational rehabilitation literature [14, 28]. The findings also align with the recommendations of Jellema et al. [29] and Linton et al. [10] that early psychosocial intervention for recently injured people should be reserved for those with identified psychological risk factors. Similarly, the actual interventions should be matched to the individual worker’s assessed psychosocial needs, rather than according to their level of risk category as undertaken in some previous and (mostly) unsuccessful trials [8]. This is one of the key points to take away from this study. The improvements on the psychological scores and the risk scale (ÖMPSQ-SF) following treatment by the psychologists and counsellors that targeted these psychosocial factors confirms they are modifiable and that changes in them are associated with better functional (RTW) and mental health outcomes. Importantly, the matched care format allows the intervention team to address the actual concerns of the injured workers rather than just assuming they related to their injuries or pain. In fact, in many cases their concerns related more to work, physical functioning and stigma than their injuries/pain. An additional, and serendipitous outcome reported by the AP Rehabilitation team was a marked difference between the two groups in disputes concerning decisions about claims or rehabilitation processes. Relative to those in the Control group the Rehabilitation team reported those in the Intervention group initiated substantially fewer complaints or costly and acrimonious litigation. This observation was not formally assessed but is worthy of future research. It may reflect a benefit from the collaborative, biopsychosocial approach promoted by the EMCAP protocol which specifically includes the injured worker in both the identification of RTW barriers and the selection of interventions to address them. Another question raised by this study concerns the providers of the psychosocial interventions. Specifically, do these interventions require mental health professionals to be effective? Dealing with people experiencing significant levels of psychological distress may well require advanced professional skills that are out of the scope of practice for most non-mental health professionals. But there is evidence that treatment providers from a range of disciplines, such as physiotherapists, occupational therapists, or nurses, who have been trained to competence in delivering psychologically-based interventions for pain can achieve significant improvements in psychosocial variables. For example, Åsenlöf et al. [30] demonstrated benefits for early matching of patients with low back pain (in primary care) to treatment by physiotherapists based on individual behavioural assessments versus guideline-informed exercise-based treatment. More recently, Kent et al [24] demonstrated that after extended training in the delivery of a cognitive and functional (behavioural) protocol, physiotherapists were also able to achieve impressive and lasting improvements in patients presenting to primary care with low back pain. In the present trial, the psychologists and counsellors were also provided training in the protocol for delivering the psychosocial intervention to injured workers and in working with the rehabilitation staff from the employer. That the psychologists/counsellors were able to deliver the protocol within the framework of 5-6 sessions, consistent with the previous trial [13], coupled with the evidence that the psychological distress levels in the early post-injury phase were not severe, would suggest that most cases could be managed by other health professionals following relevant training in psychologically-informed treatment methods, as demonstrated by Åsenlöf et al. [30] and Kent et al [24]. While the refusal rate (to see a psychologist/counsellor) amongst the identified high-risk workers might be seen as somewhat high, it was better than found in our previous study (43.1% vs 50%, respectively) [13] and similar to previous findings [31, 32]. Clearly, more work is needed on ways of overcoming this resistance to seeing a psychologist/counsellor in the early post-injury period. It is possible that more physical treatment providers (like physiotherapists) would have been more acceptable to some workers. However, as many physiotherapists lack confidence in their ability to implement a psychological form of treatment, specific training is recommended [33]. Another possible explanation for the refusal rate in the Intervention cohort could be related to the communication skills of the WRPs tasked with encouraging the injured workers to see the psychologists/counsellors. While training in the communication skills was provided as part of the protocol, it is possible that more was needed, and this should also be considered in future implementations of the protocol. The importance of training for primary healthcare professionals in screening for psychosocial risk factors, follow-up individual assessments, and strategies for engaging injured workers in an integrated and individualized biopsychosocial intervention has been recognised for some time but is still to be widely adopted [33, 34, 35]. This study provides supporting evidence for Reneman et al’s [36] recent encouragement for researchers in occupational rehabilitation to utilize a biopsychosocial model of pain. The types of injuries experienced, their primary site, and mechanisms are typically varied, as demonstrated here. Accordingly, limiting the focus of research on injured workers to those with a specific pain site, like low back pain, seems artificial and at odds with the reality that psychosocial risk factors for delayed recovery and RTW are independent of pain site. In the present study, biological contributors to the presenting injuries were addressed by the workers’ general practitioner, surgeon, and physiotherapist. Identified psychological contributors were specifically addressed by the psychologists or counsellors, and the social or environmental contributors were addressed by the WRP, claims manager, workplace supervisor, general practitioner, and psychologist/counsellor as required. In occupational injury research, perhaps the ultimate outcome measure is whether the protocol is adopted by the employer and implemented as usual practice [39]. This was one of the outcomes from the previous trial [13] and replicated after the present trial. The employer (AP) reports the protocol was implemented nation-wide soon after the completion of the study as the benefits over usual care were evident by then. Whether the improved RTW reported by AP in recent years is related to the use of the matched care protocol cannot be determined, but similar gains have not been reported in the federal government’s workforce generally, where matched care has not been specifically used. The feedback provided by key stakeholders when asked for their views on EMCAP is particularly revealing and highlights the extent to which the protocol encompassed far more than the usual treatment providers in many intervention studies. The employer (AP) reports that 5 years post commencement of the study outcomes of their three key metrics of success: improvement in mental health outcomes, time to return to pre-injury hours and incapacity cost reduction (wage replacement) have remained consistent with those reported for the study period. Strengths and Limitations of the study. Strengths include the use of a prospective study design in which the protocolised intervention, which included usual medical care (including surgery), was compared with a usual care control group of similarly injured workers from the same industry, albeit in different states. Other strengths include the long-term follow-up of 1-year post-injury, the primary outcome of days to return to pre-injury work hours based on workplace records, rather than self-report measures by the injured workers provides a more objective outcome and one that is of relevance to employers and funders. By not excluding those offered surgery for their injuries and those with pain at any site and cause, the present study can be considered as representative of the challenges facing all injured workers, their employers, insurers, and families. These features enhance the ecological validity of the study and the generalisability of the findings. A further strength is that the control was an active treatment group and one that had already established better RTW outcomes than the Australian national average for RTW after injury. Limitations include the lack of randomised assignment to intervention or control groups. This does raise the risk of bias, but given the pragmatic decision-making required to implement a trial supported by the employer and the practical problems of using a randomised design, the fact that the results replicate the previous study with the same protocol provides a degree of confidence in the findings. The failure to reach the estimated sample size for the intervention group is also a limitation and this highlights the challenge of retaining potentially suitable participants after initial screening. Another limitation was the lack of self-report psychological measures in the control group which meant we could not evaluate the impact of the ‘usual care’ on these domains. Despite its attractions, the cost would have been the potential unblinding of the WRPs, treatment providers, and workplace supervisors in relation to the high and low risk cases, raising the risk of changes in the nature of usual care. Declarations Acknowledgements This study was supported by a grant from Australia Post. We would like to specifically acknowledge the contributions to the study by WRPs at Australia Post in each state, as well as psychologists and counsellors at CONVERGE. We also thank the injured workers who voluntarily participated, and the helpful feedback they provided. Consent to Participate: Written informed consent was obtained from all individual participants included in the study. Conflict of interest: Authors Nicholas and Shaw are current Editorial Board members for JOOR. Authors Ianssen, Morgan and Rae are employees of Australia Post. Apart from that, the authors declare that they have no conflicts of interest. Author Contributions: MN is the principal investigator of the research project and had the main responsibility for the design and conduct of the study. MI, LM and RN, represented the employer and made important contributions to the design and conduct of the study. MM was responsible for gaining the ethics approval and registering the trial, as well as data preparation and data collection, along with LM and RN, under the supervision of MI. TO contributed to the training and advice for the WRPs and psychologists/counsellors involved in the intervention arm. DC conducted the statistical analyses. MN had the main responsibility for the writing of the first draft of the manuscript with support from MI, LM, MM and DC. CM, WS, SL made contributions to the manuscript’s design, conception and interpretation of data. All authors have revised the manuscript critically, discussed the results, commented on the manuscript and approved the final manuscript. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 15 Apr, 2026 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9422704","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625116493,"identity":"ad2161eb-a877-4d6f-aa3e-3a20c7859acd","order_by":0,"name":"Michael 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09:18:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":965976,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9422704/v1/46a1686c-a139-4c33-b638-28c112eeed88.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Matched Care for High-Risk Injured Postal Workers: A Replication Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn Australia, average national return to work (RTW) rates after workplace injuries have remained at around 80% work for the last decade, with recent figures even indicating a decline in this rate [1,2]. The costs associated with the delayed RTW can be substantial, both financially and in terms of quality of life for the individuals concerned, their employers, and society at large [3]. Reducing lost time from work after injury has been a major focus for occupational researchers. Two major lines of research findings have emerged. Firstly, prospective studies have repeatedly confirmed that psychological and social/environmental factors are strong predictors of delayed recovery and RTW following workplace injuries [4,5,6,7]. Secondly, despite expectations that interventions targeting those deemed at risk due to psychosocial factors as early as possible would improve disability outcomes, the results have often been mixed [8]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCroft et al. [8] considered possible explanations for these disappointing results in studies of stratified care. Chief amongst these was the problem of implementing stratified care protocols in clinical practice, especially when it involved changes in clinician behaviour. But one explanation not canvassed was the nature of the interventions employed in the stratified care studies. Specifically, the stratified interventions were largely based on the risk category an injured person fell into (high, medium or low) according to their scores on the self-report StartBack tool (SBT) [9]. However, the interventions offered were not individually tailored to the specific concerns of each patient.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn alternative approach to those identified at high risk of delayed RTW due to psychosocial factors was described by Linton et al. [10] as \u0026lsquo;matched care\u0026rsquo;. As in stratified care, matched care involves screening injured workers with a psychosocial risk scale, but then having a psychologist assess each high-risk worker to clarify their concerns and relevant contextual factors. This enables an intervention to be tailored to the assessed needs of the high-risk group, rather than according to their risk group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA systematic review of studies of interventions aimed at improving RTW outcomes after injury by Cullen et al. [11] concluded that improved outcomes for injured workers were more likely if interventions entailed a comprehensive, biopsychosocial approach involving treatments coordinated with the workplace. In contrast, the stratified care studies reviewed by Croft et al. [8] do not appear to have been individually tailored and nor did they address possible workplace and coordinated care issues, although Delitto et al. [12] acknowledged the problem.\u003c/p\u003e\n\u003cp\u003eA coordinated and individually matched care approach was reported by Nicholas et al. [13] who found lost time from work in the following 2 years after injury was half that of a usual (stepped) care control group. However, as the experimental design was not randomized, it is possible the findings were due to chance and replication was essential to confirm the findings.\u003c/p\u003e\n\u003cp\u003eThe present study with recently injured workers uses the same basic protocol employed by Nicholas et al. [13] but in another industry and using different service providers. The primary outcome was time to return to pre-injury work hours over the following year.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAustralia Post (AP) is an Australian Government-owned enterprise providing postal services for the whole country (of 6 states and 2 territories), with a total workforce of \u0026gt;34,000. The workforce comprises around 60% males, with 46% aged between 30 and 50 years. The nature of the work is varied, ranging from clerical, office-based roles to more physical mail sorting, and delivering parcels or letters by truck, motor bikes and on foot in a wide range of geographical locations. Commensurate with this breakdown, a consistent contributor to workplace injuries for AP is motor accidents and surgery is often required in these cases. To make the study as representative as possible of the injured workforce all those with a workplace injury, including those requiring surgery, were invited to participate. It was felt this broad inclusion approach would provide the study with what might be termed \u0026lsquo;ecological validity\u0026rsquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBefore the study commenced in early 2020, the RTW outcomes for AP had been consistently around 90%, which was markedly better than the national average at the time of around 80%\u0026nbsp;[1]. Importantly, AP had already adopted an in-house rehabilitation model that was focused on early intervention and the health benefits of work.\u0026nbsp;However, psychosocial screening to identify those likely to have delayed recovery was not done routinely and decision-making by AP\u0026rsquo;s rehabilitation providers was often based on personal judgement and lack of expected progress.\u003c/p\u003e\n\u003cp\u003eIn preparation for the study and consistent with recommendations in the occupational health literature [14], a period of consultation with key stakeholder groups within AP nationally (across all states) was undertaken by the AP rehabilitation team. This preparatory phase included education about the evidence for psychosocial risk factors and their impact on recovery from injury. Consultation groups included: senior and operational management, injured workers, Work Rehabilitation Providers (WRPs), claims delegates, union representatives (CEPU), human resources members, allied health professionals and other treatment providers.\u003c/p\u003e\n\u003cp\u003eThe study was named EMCAP (Early Matched Care, Australia Post). The protocol was based on that used in the WISE study but tailored, by discussion with the AP rehabilitation managers, to fit within the normal business practices of the employer, AP. As in the WISE study, EMCAP entailed comparing a coordinated approach to injured AP workers who had been identified by a brief psychological screening instrument (\u0026Ouml;rebro Musculoskeletal Pain Screening Questionnaire-Short Form - \u0026Ouml;MPSQ-SF) [15] as at high risk for lost time from work. The intervention was intended to target both psychological and social/workplace risk factors identified at the individual level in addition to usual care by the treating doctors and physiotherapists. The psychological interventions matched to the identified risk factors for each worker were provided by psychologists and counsellors employed as Employee Assistance Providers (EAPs) by Converge, a company contracted to AP to provide Employee Assistance Provider (EAP) services.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnlike the WISE study, which was conducted within one state, EMCAP was conducted across all six Australian States and participation in the intervention or control condition depended upon state of residence as random assignment would have been too complicated for the state-based rehabilitation teams to manage the different protocols at the same worksites. To balance the distribution of cases, the two most populous states (New South Wales and Victoria) were assigned to different groups, with Victoria becoming an intervention state and NSW a control. The next most populous (Queensland) was designated as a control, and the rest (comprising the more sparsely populated states of Tasmania, South Australia, and Western Australia) were designated as intervention states. All these decisions were made by the AP management team, and the external researchers played no part.\u003c/p\u003e\n\u003cp\u003eAll AP workers across the country with new (non-psychological) injury claims were to be offered screening by the Workplace Rehabilitation Providers (WRPs) with the \u0026Ouml;MPSQ-SF after informed consent for screening was obtained. Informed consent included permitting the research team to follow their RTW progress over the following year. Screening was expected to occur within the first 5 days after the injury/incident report. To ensure the study was as ecologically valid as possible, no injury site or cause (apart from psychological injury claims) was to be excluded and even those offered surgery for their workplace injury were to be included provided they gave informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1 Participants and pain sites\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStudy participants were recruited by WRPs from consecutive injured AP workers who made a work-related (non-psychological) injury claim. The severity of injury was not a selection criterion, providing the worker was able to communicate sufficiently in English with the WRP. \u0026nbsp;WRPs from both Intervention and Control groups were given training by webinar by the lead researcher (MN) in ways of introducing the\u0026nbsp;\u0026Ouml;MPSQ-SF to the injured workers. The steps taken for the consenting workers are presented graphically in Figure 1.\u003c/p\u003e\n\u003cp\u003eOnce a worker was identified as being in the high-risk category, and living in an intervention state, their WRP explained the nature of the study to them individually to obtain informed consent for participation in the intervention protocol. Once this was provided, each worker\u0026rsquo;s General Practitioner (GP), Case Manager (CM) and workplace supervisor (team leader) were advised of the risk status of the injured worker and the protocol for their management was activated. \u0026nbsp;This entailed a meeting between the worker\u0026rsquo;s GP, the worker, the WRP, and the psychologist/counsellor (who attended by telephone). Those who declined to participate received usual care (like the Control group) but were out of the study as their risk status was no longer blinded to the WRP managing the case.\u003c/p\u003e\n\u003cp\u003eFor the Control group, the completed screening instrument was scored by the Research Manager (MM). The WRP, General Practitioner (GP), other treatment providers, Case Manager (CM) and workplace remained blinded to the risk status of these injured workers. All participating (high and low risk) workers from both groups received usual care under the federal Government\u0026rsquo;s workers compensation scheme (Comcare).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Specific Hypotheses tested\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2.1\u003c/em\u003e Compared to those in the Control group, time to return to pre-injury hours of work (which is when wage replacements are no longer provided) would be significantly less for the Intervention group. These differences were expected whether surgery was undertaken or not. A recent national survey of injured workers found only 53% reported working at their pre-injury hours after RTW, which is lower than the overall RTW rate of 78%, and represents a higher goal to achieve than simply RTW.[2]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2.2\u003c/em\u003e The mean costs of claims (for lost time and medical treatments) would be less for the Intervention group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2.3 Additional, secondary outcomes were time to return to pre-injury duties and time to return to work (any time), as well as incapacity and medical costs, which were expected to also favour the Intervention group.\u003c/p\u003e\n\u003cp\u003e2.2.4 Consistent with the hypothesis that improved psychological risk factors (as assessed by the self-report \u0026Ouml;MPSQ-SF, mood, pain, interference, and pain beliefs measures) would be associated with improved RTW outcomes, psychological risk factors were also assessed pre- and post-the psychological interventions. But these were only assessed for the Intervention group to ensure blinding of the Control group\u0026rsquo;s risk status was not inadvertently revealed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3 Inclusion/exclusion criteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants had to be AP workers who had reported a physical, work-related injury\u0026nbsp;(other than primary psychological injuries) between January 2020 and May 2021)\u0026nbsp;and had made a workers compensation insurance claim. They also\u0026nbsp;had to have taken, or had been approved for, medically-sanctioned time off work due to their injury. Injured workers recommended for surgical interventions were also able to participate and were included in the study if they consented.\u0026nbsp;In addition, only those screened as having a high psychological risk for delayed recovery (\u0026Ouml;MPSQ-SF \u003cu\u003e\u0026gt;\u003c/u\u003e 48/100), and being within four weeks of the injury date, were included in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExclusion criteria\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eProspective participants were excluded if they had made a psychological injury claim, or scored \u0026lt;48 on the \u0026Ouml;MPSQ-SF. Those who declined to participate after having the study explained to them were also excluded.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4 Ethics approval and prospective trial registration\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from the University of Sydney Human Research Ethics Committee (2019/976). The study was registered prospectively with the Australian and New Zealand Clinical Trials Registry (ACTRN12620000041954).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5 Study Design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA controlled, non-randomised, prospective design was used (Fig 1) using an Intervention and Control group. Assignment of workers to Intervention or Control groups was based on which state they lived in. The states were chosen by the Rehabilitation team at AP in a manner aimed at balancing the workforce numbers across those states overall.\u003c/p\u003e\n\u003cp\u003eThe outcomes of high-risk workers from the Intervention states (Victoria, South Australia, Western Australia, and Tasmania) were compared with the outcomes of similar high-risk workers from the Control states (New South Wales and Queensland).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn both the Intervention and Control states the rehabilitation teams subscribed to the value of early intervention and the same treatment options were available in all states if requested by WRPs or treatment providers. The main differences between the two conditions were the known risk status and agreed matched care management protocol for high-risk cases in the Intervention states.\u003c/p\u003e\n\u003cp\u003eData on work status, lost days, and costs were maintained by the employer (who was also the insurer) as normal for a minimum of one year from the date of injury.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.6 Sample size\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size estimation for this study was based upon the WISE study [13], which compared a brief psychological intervention within a workplace and medical management protocol for recently injured workers employed by NSW Health vs standard care. It reported effect sizes of d = .39 for the primary outcome of work loss time, indicating that the intervention was effective. Based on this effect size, d = 0.39, an alpha of .05 and 80% power, a sample of N = 208 workers was calculated to be adequate (n = 104 per group) in the EMCAP study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.7 Protocol for Intervention Group\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs this study involved several stakeholders working in a coordinated way, the (abbreviated) roles of each are outlined here. The steps from initial recruitment are summarised in Figure 1. Note, the intervention for the consenting workers did not start until their case had been discussed at a case conference with the treating doctor (GP), the injured worker, their WRP, and the psychologist/counsellor (who attended by telephone). At this meeting the worker\u0026rsquo;s RTW obstacles were identified and the intervention plans agreed to by all those present. The implementation of the protocol by all stakeholders was monitored as closely as possible by the Research Manager (MM) and senior AP rehabilitation staff (LM) throughout the project to promote adherence. The psychologists/counsellors and WRPs in the Intervention arm of the study received training in the background of the protocol and its implementation for this study. This was provided over 1 day for the psychologists/counsellors via webinar by the lead investigator (MN). The training included extended discussion about the protocol, guidance in scoring the\u0026nbsp;\u0026Ouml;MPSQ-SF and using the workers\u0026rsquo; responses to clarify the nature of psychosocial contributors to high scores with the injured workers, exploring options for addressing the psychosocial risk factors, including basic pain self-management strategies, and a review of the WISE study experiences. Additional mentoring and psychology guidance for the psychologists/counsellors was provided as needed by the external clinical psychologist (TO), who had previously participated in the WISE study as a treating psychologist, along with periodic follow-up reviews with the participating psychologists/counsellors and WRPs by the lead investigator (MN).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.7.1 Workplaces and work rehabilitation provider (WRP)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWorkplace interventions depended upon the RTW obstacles for the individual workers as identified by them, their Converge counsellor/psychologist and their WRP (after consultation with the workplace team leader). The WRP group had training in the use of the intervention protocol by the lead investigator (MN) with follow-up review sessions from time to time. Additional guidance for the WRPs was provided as needed by the external psychologist (TO).\u003c/p\u003e\n\u003cp\u003eThe WRPs met (face to face, except when limited by COVID-19 restrictions) with all high-risk workers within a week of the initial screening to recruit for the study. This included encouraging them to see the selected psychologist/counsellor within the next week for an assessment and possible treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe WRP was required to be in regular contact with the workers, as well as their GP and the treating psychologists/counsellors throughout their treatment. They were also expected to work closely with each worker\u0026rsquo;s workplace supervisor to assist the RTW processes as required. In addition to modifying work tasks and conditions, examples of the practical responses to the workers\u0026rsquo; needs included arranging for them to see a dietician (if body weight was a concern, e.g., for those with lower limb injuries), support for attending exercise programs, as well as arranging for aids and equipment where needed (not just for work but also for personal/recreational functioning).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.7.2 Psychologists/counsellors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe participating psychologists/counsellors were employed by an external organisation (Converge) contracted to provide employee assistance (EAP) to AP. A specialist group within Converge was established for this project and this consisted of psychologists/counsellors who had a background or interest in occupational health. As mentioned earlier, they were provided with additional education about the health benefits of rehabilitation at work, including a discussion about the earlier WISE study and what was learnt from that. The training also included strategies for helping the injured workers to engage in managing their pain and other RTW obstacles. These strategies included the use of a case formulation approach to matching the interventions to the worker\u0026rsquo;s presenting problems and contributing factors, including activity goal setting, the use of activity pacing to reach those goals, relaxation methods, and problem management for issues like pain, stress, sleep disturbance, etc [16].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent evidence supporting this matched care approach was also provided as part of the training \u0026nbsp; [6,13,10]. The psychologists/counsellors were required to agree to follow the intervention protocol. This included assessing the workers within a week of referral and to use the worker\u0026rsquo;s responses to items on the \u0026Ouml;MPSQ-SF to develop a case formulation followed by identification of likely obstacles to RTW. The psychologists/counsellors were expected to address all obstacles they considered within their scope of practice within six weekly sessions (one per week for six weeks). The psychologists/counsellors were also required to explain to the workers who agreed to participate that, unlike their normal role in Employee Assistance where the content of sessions were strictly confidential, in this study they were required, as appropriate, to share with the employer and WRP any issues raised by the injured worker that may be affecting their ability to recover and RTW. The help of the employer and WRP would be sought in resolving these issues, in collaboration with the injured workers. The injured workers were, however, assured that no confidential information concerning the worker would be passed to the employer or WRP. Accordingly, the psychologist/counsellors were expected to maintain regular contact with both the workplace (WRP) and treating doctor (GP). If a consenting high-risk worker was scheduled for surgery, the psychological treatment sessions would follow the worker\u0026rsquo;s recovery from surgery once the surgical team cleared them to proceed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.7.3. Nominated Treating Doctor (GP) \u0026ndash; Primary Care General Medical Practitioners\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe GPs were chosen by the injured workers, as per normal for injured workers. The GPs were informed their patient was in an approved trial and details of the trial were provided by the workplace WRP. The GPs were free to pursue whichever medical treatment they judged appropriate, as per the usual Comcare Guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.7.4. The (insurance) case manager (CM)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe CM referred participating workers to nominated independent specialists (relevant to injury type) on an as needed basis as per normal practice, this was not included within the standard EMCAP protocol as it applied to the intervention and control groups alike.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.7.5. Physiotherapists\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe physiotherapists providing care were selected by the workers\u0026rsquo; GPs and reported to the WRP and GP on their progress. The physiotherapists treating Intervention workers were advised by the WRP that the worker was participating in a study aimed at facilitating early RTW. The physiotherapists were expected to have a good understanding of the importance of an activity-based approach to treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.8. Variation in Protocol during COVID-19 shutdowns\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe advent of COVID-19 affected both intervention and control states from about April 2020, but the impact varied according to state-based rules imposed by the different state governments. The strictest restrictions on movement were imposed in the state of Victoria (an Intervention state) and these restrictions meant that several changes in protocol for the study were required and were approved by the University of Sydney HREC. \u0026nbsp; The main impact was that face-to-face counselling sessions had to be conducted online by videolinks. The extent to which these changes affected the study was unclear, but the impression from a review by the rehabilitation managers for AP did not reveal any obvious differences between face-to-face vs online sessions. However, there was no option at the time but to continue with the study to the best of the WRP and treatment providers\u0026rsquo; abilities. Several recommendations had to be adapted during lockdowns, for example gym equipment was rented and delivered to a workers\u0026rsquo; home and they were provided with telehealth sessions with an exercise physiologist as an alternative to a gym program.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.9 Protocol for Control Group\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHigh and low risk workers in the two Control states received treatment as usual under the standard Comcare Guidelines that apply nationwide for AP. \u0026nbsp; As noted earlier, this also meant the services of a psychologist or counsellor could have been sought by the WRP and GP if thought necessary.\u003c/p\u003e\n\u003ch3\u003e3. Measures\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003e3.1 Screening measure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026Ouml;MPSQ-SF (\u0026Ouml;rebro Musculoskeletal Pain Screening Questionnaire \u0026ndash; Short Form) [15] [contains 10 items, each scored on a 0-10 scale, to yield a possible score between 0 \u0026ndash; 100.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWorkers who scored \u0026ge; 48 (out of 100) were considered at high-risk of delayed recovery [6]. The score of 48 was used in this study, rather than the score of 50 in the WISE study, as 48 was found by Nicholas et al [6] as the optimal cut-off according to both Youden\u0026rsquo;s and the distance statistics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2 Outcome measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.1 Days to pre-injury work hours\u0026nbsp;\u003c/em\u003e(PIWH), operationalised as lost time from work (number of days reimbursed for missing pre-injury work hours) over 1 year from injury. The data on lost work time were obtained from the Australia Post Records.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.2 Total claim costs\u003c/em\u003e. These data were also obtained from the Australia Post records. These encompasses all claim-related costs, including wage replacement, medical treatments, rehabilitation and matched care services. The costs of the psychologists/counsellors for the intervention were not included as they were already covered within the EAP contract with Australia Post and did not add to the costs of the intervention. It is important to note that the Control Group participants could also access the counsellors in their state if they wished or on recommendation of their WRP. The costs of medical treatments (including GP visits, specialists, allied health, independent medical assessments, surgery, hospitalisation) were also compared between the two groups.\u003c/p\u003e\n\u003cp\u003eAdditional cost measures included the mean Incapacity payments. These refer to the costs of wage replacement while an injured worker is not at work.\u003c/p\u003e\n\u003cp\u003e3.2.3 Supplementary data\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcceptability of the intervention protocol for employer/ insurer\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate the acceptability of the EMCAP protocol to key stakeholders, feedback was sought from injured workers treated within the EMCAP protocol, their GPs, WRPs, workplace managers, and psychologists/counsellors at the end of recruitment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3 Psychological outcome measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe changes in psychological risk level following the psychological treatments were evaluated by the \u0026Ouml;MPSQ-SF administered to participants by the treating psychologists/counsellors before and after their treatments. Additional psychological outcome measures were also administered by the treating psychologists/counsellors at these times as per hypothesis 2.2.4:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003e\u0026Ouml;rebro Musculoskeletal Pain Screening Questionnaire (\u0026Ouml;MPSQ-SF) [15]. The 10-item version of the original 24 item version comprises established psychosocial predictors of lost time from work after work injuries. It has been found to have good psychometric properties, and the total score has been found predictive of lost time from work after work injuries [6].\u003c/li\u003e\n \u003cli\u003eThe Brief Pain Inventory (BPI) [17] includes two sub-scales: Pain Severity scale and Pain Interference scale. Both use a 0-10 scale, where higher scores indicate higher pain levels and higher pain interference in daily activities, respectively. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe 21-item version of the Depression Anxiety Stress\u003cem\u003e\u0026nbsp;\u003c/em\u003eScales (DASS) (Lovibond and Lovibond, 1995) [18], was used to measure severity of Depressive, Anxiety, and Stress symptoms. Each subscale score was doubled to provide a mean score between 0-42, with higher scores reflecting higher severity on each dimension.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe Pain Self-Efficacy Questionnaire\u003cem\u003e\u0026nbsp;\u003c/em\u003e(PSEQ) (Nicholas, 2007) [19] measures the strength and generality of a person in pain\u0026rsquo;s beliefs about his/her ability to accomplish various activities, including work, despite their pain. Scores range from 0 to 60. Higher scores indicate stronger self-efficacy beliefs. One of the goals of the intervention was to achieve higher scores on this scale as they are associated with higher functional status.\u003c/li\u003e\n \u003cli\u003eThe Pain Catastrophising Scale (PCS) (Sullivan et al., 2005) [20] provides a measure of distressing thoughts about pain. Total scores range between 0-52, with higher scores indicating more frequent distressing thoughts.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e4.\u003cem\u003e\u0026nbsp;Participants\u0026nbsp;\u003c/em\u003e(see CONSORT Diagram, Fig 2)\u003c/p\u003e\n\u003cp\u003eOf the 854 participants deemed eligible, 549 (64%) consented to screening, with 328 allocated to the Control states and 221 to the Intervention states. \u0026nbsp;In the Control states, 192 (58%) of the 328 participants were classified as high risk and therefore eligible for inclusion in the study. \u0026nbsp;In the Intervention states, 137 (61.9%) participants were classified as high risk, and of those, 78 (56.9%) consented to participate in the Intervention group. \u0026nbsp; Finally, as three participants in each group lived in very remote areas (and not only had additional difficulties accessing health services, compared to those in more urban areas, supported and gradual return to work was more challenging and expensive to provide as they were the sole employee at these sites) they were excluded due to the expectation that they would not represent the types of rehabilitation problems and costs faced by their colleagues in more urban regions, irrespective of intervention. \u0026nbsp;The final sample for analysis was, therefore, 189 in the Control group and 75 in the Intervention group, for a total sample of 264. The basic demographic features for the final sample are presented in Table 1. The proportions for the primary injury sites and/or diagnoses (where available) are provided in Table 2. \u0026nbsp; Examination of Table 1 indicates the participants in both groups were roughly similar, as was their mean initial screening score on the \u0026Ouml;MPSQ-SF. Examination of Table 2 indicates that participating workers in the Intervention and Control groups were quite heterogeneous in their injuries and injury sites, with shoulder and lower back injury sites being the most common, with lower limb injuries next. The list for both groups was similar and included both fractures and soft tissue injuries.\u003c/p\u003e\n\u003cp\u003eTable 1: Basic demographic information for both groups.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFinal Number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eMean age (yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e48.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e50.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGender (Males)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e72%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eScreening \u0026Ouml;MPSQ-SF (Mean, SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003e60.1 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e58.8 (7.5)\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\u003eTable 2. Primary injury sites/diagnoses for both groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"434\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eIntervention (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eControl (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eShoulder injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e20 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e56 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eLow back injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e16 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e33 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eHip injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eHand/wrist injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e21 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eKnee injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eLower limb fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e8 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e18 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eLower limb soft tissue injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e20 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eElbow injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eNeck injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e5 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eHead injury/concussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eHernia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e11 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eUpper limb fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e2 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eSoft tissue upper limb injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1 (0.5)\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\u003cem\u003e5. Primary and secondary outcome variables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome variable was number of days to return to pre-injury work hours. As those\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehaving surgery were included in the study, the days to return to pre-injury work hours were analysed for this cohort as well in both groups. Cost was a secondary outcome variable, and we analysed total cost as well as its breakdown into medical costs and incapacity payments (wage replacements). Days to pre-injury duties and days to return to work were also analysed as secondary outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e6. Data analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the primary outcome, we used survival analysis to compare time to return to pre-injury hours between the Control (coded 0) and Intervention (coded 1) groups. \u0026nbsp;We examined Kaplan-Meier curves for the two groups separately, and tested the group difference with Cox regression, using the survival (Therneau, 2023) [21] package in R. \u0026nbsp;From the Cox regression we interpreted the hazard ratio, which represents the \u0026ldquo;risk\u0026rdquo; of return to pre-injury hours for the intervention group compared to the control group, such that a hazard ratio \u0026gt; 1 indicates earlier return to pre-injury hours for the Intervention than for the Control group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the secondary outcomes, we also used survival analysis to compare days to pre-injury duties and days to return to work between the Intervention and Control groups. \u0026nbsp;To compare costs between groups, we used linear regression with bootstrapped confidence intervals (95% bias-corrected, accelerated) to account for possible skew in cost data. \u0026nbsp;In all analyses, we controlled for whether participants had surgery. The reason for this was that having surgery was expected to add to lost time from work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants who consented to participate in the Intervention group with Converge completed questionnaires (BPI, DASS, PSEQ, PCS) before and after the psychological component of the intervention, and we analysed these changes using paired-samples \u003cem\u003et\u003c/em\u003e-tests, as well as calculating standardised mean differences and their 95% confidence intervals.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePrimary Outcome\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003eFor return to pre-injury work hours, the proportional hazards assumption was satisfied for Intervention group, surgery, and the global model (Table 3). \u0026nbsp;In the Cox regression (Table 4), intervention group was a significant predictor of return to pre-injury hours, with a hazard ratio of 1.45 indicating earlier return to pre-injury hours in the intervention than in the control group, while controlling for surgery. \u0026nbsp;Median days to return to pre-injury hours was 79 for the intervention group (mean = 90.1) and 92 for the control group (mean = 121.9). \u0026nbsp;Surgery was also a significant predictor of return to pre-injury hours, with a hazard ratio of 1.62 indicating earlier return to pre-injury hours in the no-surgery group than in the surgery group, while controlling for intervention group. \u0026nbsp;Median days to return to pre-injury hours was 128 for those who had surgery (mean = 158.9) and 88 for those who did not (mean = 100.9).\u003c/p\u003e\n\u003cp\u003eHowever, there was a difference between groups for those who had surgery (Table 5). Although the numbers available for analysis were small (12 in the Intervention and 18 in the Control), of those who had surgery in the Intervention group, median days to return to pre-injury hours was 89 (mean = 105.0) and 167 for the Control group (mean = 198.3). \u0026nbsp;In the Cox regression analysis, the Intervention group was a significant predictor of return to pre-injury hours, with a hazard ratio of 2.26 indicating earlier return to pre-injury hours in the Intervention than in the Control (\u003cem\u003ep\u003c/em\u003e = .048).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"602\" height=\"124\" 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\" alt=\"A table with numbers and symbols AI-generated content may be incorrect.\" v:shapes=\"_x0000_i1029\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"479\" height=\"269\" 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\" alt=\"A table with numbers and text AI-generated content may be incorrect.\" v:shapes=\"_x0000_i1028\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"602\" height=\"363\" 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\" v:shapes=\"_x0000_i1027\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e7.1 Secondary outcomes:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the secondary return to work outcomes (return to pre-injury duties and return to work), the proportional hazards assumption was satisfied. \u0026nbsp;For return to pre-injury duties, neither predictor (group - Intervention and Control = and surgery) was significant, and for return to work, group (intervention and Control) was not significant, but having surgery was a significant predictor of delayed RTW (see Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e7.2 Cost outcomes:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results the linear regression analysis for total cost data are shown in Table 6. \u0026nbsp;For the Intervention group, the mean total cost was $14,021.68 (median = $7,814.38) and for the Control group it was $20,601.99 (median = $13,463.89). \u0026nbsp;In the regression model, predicted medical cost was significantly lower for the Intervention group than for the Control group. As might be expected, predicted total cost was also significantly lower for those who did not have surgery (mean = $11,500.36; median = $6,895.61) than for those who did (mean = $34,087.62; median = $26,897.64).\u003c/p\u003e\n\u003cp\u003eThe mean medical cost was $4,625.69 (median = $2,736.97) in the Intervention group and $8,226.32 (median = $4,559.80) in the Control group. \u0026nbsp;In the regression model, predicted medical cost was significantly lower for the Intervention group than for the Control group. \u0026nbsp;As might be expected, predicted medical cost was also significantly lower for those who did not have surgery (mean = $3,833.60, median = $2,758.67) than for those who did (mean = $14,216.97, median = $11,488.22).\u003c/p\u003e\n\u003cp\u003eThe mean incapacity payment was $8,772.95 (median = $4,548.86) in the Intervention group and $12,035.37 (median = $7,429.37) in the Control group. \u0026nbsp;In the regression model, treatment group did not significantly predict incapacity payment. However, predicted incapacity payment was significantly lower for those who did not have surgery (mean = $7,387.59, median = $4,401.56) than for those who did (mean = $19,459.72, median = $12,176.69).\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"502\" height=\"204\" 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+X8VvGYVCiMlq3AwC205P1qLxjcQ6L8Q/D+u6rE7aTFBNB54QstvK2MMwHTI4zVDxvqNn4i8N2Oq6Rby3mmWOrxTXyxwEedGvVgMfOBkflTtT1fT/GfjDwsfDe64fT7hri5u0iKrDFtwUJIHJ6YqhBrVvofiSK08Jam2oQ3upH7Vo8tuxMJY/PIr4BUDrzxWxo2pWuj/ABY8T2d+Whm1JrZ7PMZxMBHg4OOxrpfHOhnxD4P1CxjH+kbPNtyOolT5lP5iuL8H6leeJ7zUvGUljJJJY6ctla2zqRvlC7pcA+rYGa4+/wBf1PX7XQpLnWYZZBqMDyaTbaaU+yHdjDSHkY6e9elWduknxs1OZ4QzJpMQRyv3csc4NZcl3d6b4q+It9aWP2yeK0tWjgZCVkOxuMd/pXLwaxf614p8IXE+vx6ntvQWgttOMKWpKHKl/wBMGugsfEGkeG/iZ4wn1kPDHM0KxXLQMynEfKbgOvtWRBaTw/Dm2ma1lghvPEqXVtAyEFIWl+Xjt6/jVnUlbR/iZ4huNW1++0SK+WFrS4igDxzIFwV3FTgg9quf2V4WsfAdkZ9b1G2gOotdWeoTQFXSbJ+bbtxtPPUYrqfh7rupa5p9+18yXEUFyY7a/SExC7TH39p/L0rsaKKKKKKKKKKKKKKKKKKKKKKKKKRlDKVYAg9QRRgYxgY9KakMUYASNFA5GFAxQYYmxmJDg5GVHBpxVWIJUEjoSOlNeGKRgzxoxHQsoOKfTGijdlZ41Zl6EjJFOZVbG5QcHIyOlBAIIIyD2NJsXZs2jbjG3HFI8UbpsdFZf7pGRQYozH5ZjUp/dI4/KlRFjUKihVHQAYFIIYlztjQZOThR19acFUMWCgMepx1pohiG7ESfP975Rz9aVI0iXbGiovoowKVkV1KuoZT1BGQaRESNQqKqqOwGBSqqou1VCj0AxRtXdu2jd0zjmk8tNu3Yu3OcY4zSlVLAlQSOhx0prwxSMGeNGZehKgkUrxpKu2RFdfRhkUeWmCNi4PUY604DAwOlJtXdu2jdjGcc0kkccq7ZEVx6MM1z/izwt/wkllaJBevY3VlOtxbSooZVceqngiqGl+DtQPiS217xBrI1G7s42jtUigEKRhvvE45JrsFVUGFUAegFN8mLzPM8tN/97aM/nQ0MTuHaJGcdGKgkUPDFIwZ40Zh0LKCRTiqlgxUEjoSOlNaGN3DtGjMOhKgkU4qrEEqCRyMjpRtXdu2jdjGcc0MiupV1DKeoIyKRY0UYVFAAxwO3pR5abNmxdv8AdxxSPFHIAHjVgORuGcUpjQggopBOSMdawfEXhn/hIdQ0aSa52Wen3P2p7fbnznA+XJ7AHmt2SKOVdsiK49GGaXy02hdi7R0GOBSGGIyCQxoXHRtoz+dPpnkx+Z5nlpv/AL20Z/OnFVLBio3Docc0BVDFgoBPU460josilXUMp6gjIoVFRNiqAo7AcUkcUcQIjjVAeTtGM0LDErl1iQOerBRk0pjRnDsill6MRyKdSKioMIoUdcAYpohiBYiJAW5JCjmnbV3Fto3HjOOaAqhiwUAnqcdaasMSfdiRec8KBz60NDE4IeNGBOTlQeacUVgAVBA6AimyRRygCSNXA6Bhmh4o5E2OisvoRkU5VVFCqoVR0AGAKWiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiionureJtsk8SN6M4Bpv260/5+oP+/go+3Wn/AD9Qf9/BR9utP+fqD/v4KPt1p/z9Qf8AfwUfbrT/AJ+oP+/go+3Wn/P1B/38FH260/5+oP8Av4KPt1p/z9Qf9/BR9utP+fqD/v4KPt1p/wA/UH/fwUfbrT/n6g/7+Cj7daf8/UH/AH8FH260/wCfqD/v4KPt1p/z9Qf9/BR9utP+fqD/AL+Cj7daf8/UH/fwUfbrT/n6g/7+Cj7daf8AP1B/38FH260/5+oP+/go+3Wn/P1B/wB/BR9utP8An6g/7+Cj7daf8/UH/fwUfbrT/n6g/wC/go+3Wn/P1B/38FH260/5+oP+/go+3Wn/AD9Qf9/BR9utP+fqD/v4KPt1p/z9Qf8AfwUfbrT/AJ+oP+/go+3Wn/P1B/38FH260/5+oP8Av4KPt1p/z9Qf9/BR9utP+fqD/v4KPt1p/wA/UH/fwUfbbT/n6h/7+Cj7daf8/UH/AH8FH260/wCfqD/v4KPt1p/z9Qf9/BR9utP+fqD/AL+Cj7daf8/UH/fwUfbrT/n6g/7+Cj7daf8AP1B/38FH260/5+oP+/go+3Wn/P1B/wB/BR9utP8An6g/7+Cj7daf8/UH/fwUfbrT/n6g/wC/go+3Wn/P1B/38FH260/5+oP+/go+3Wn/AD9Qf9/BR9ttD0uof+/go+3Wn/P1B/38FH260/5+oP8Av4KPt1p/z9Qf9/BR9utP+fqD/v4KPt1p/wA/UH/fwUfbrT/n6g/7+Cj7daf8/UH/AH8FH260/wCfqD/v4KPt1p/z9Qf9/BR9utP+fqD/AL+Cj7daf8/UH/fwUfbrT/n6g/7+Cj7daf8AP1B/38FH260/5+oP+/go+3Wn/P1B/wB/BR9utP8An6g/7+Cj7daf8/UH/fwUfbrT/n6g/wC/go+3Wn/P1B/38FH220PS6h/7+Cj7daf8/UH/AH8FH260/wCfqD/v4KPt1p/z9Qf9/BR9utP+fqD/AL+Cj7daf8/UH/fwUfbrT/n6g/7+Cj7daf8AP1B/38FH260/5+oP+/go+3Wn/P1B/wB/BR9utP8An6g/7+Cj7daf8/UH/fwUfbrT/n6g/wC/go+3Wn/P1B/38FH260/5+oP+/go+3Wn/AD9Qf9/BR9utP+fqD/v4KPt1p/z9Qf8AfwUfbrT/AJ+oP+/go+3Wn/P1B/38FOS6t5W2xzxO3orgmpaKK4bSNA0jWPEniabUdOt7qRL1VVpk3EDy14Ge1bf/AAhPhj/oBWH/AH5FH/CE+GP+gFYf9+RR/wAIT4Y/6AVh/wB+RR/whPhj/oBWH/fkUf8ACE+GP+gFYf8AfkUf8IT4Y/6AVh/35FH/AAhPhj/oBWH/AH5FH/CE+GP+gFYf9+RR/wAIT4Y/6AVh/wB+RR/whPhj/oBWH/fkUf8ACE+GP+gFYf8AfkUf8IT4Y/6AVh/35FH/AAhPhj/oBWH/AH5FH/CE+GP+gFYf9+RR/wAIT4Y/6AVh/wB+RR/whPhj/oBWH/fkUf8ACE+GP+gFYf8AfkUf8IT4Y/6AVh/35FH/AAhPhj/oBWH/AH5FH/CE+GP+gFYf9+RR/wAIT4Y/6AVh/wB+RR/whPhj/oBWH/fkUf8ACE+GP+gFYf8AfkUf8IT4Y/6AVh/35FH/AAhPhj/oBWH/AH5FH/CE+GP+gFYf9+RR/wAIT4Y/6AVh/wB+RR/whPhj/oBWH/fkUf8ACE+GP+gFYf8AfkUf8IT4Y/6AVh/35FH/AAhPhj/oBWH/AH5FH/CE+GP+gFYf9+RR/wAIT4Y/6AVh/wB+RR/whPhj/oBWH/fkVga/4S8PQav4dji0azRJr5kkCxgBh5MhwfXkA/hW/wD8IT4Y/wCgFYf9+RR/whPhj/oBWH/fkUf8IT4Y/wCgFYf9+RR/whPhj/oBWH/fkUf8IT4Y/wCgFYf9+RR/whPhj/oBWH/fkUf8IT4Y/wCgFYf9+RR/whPhj/oBWH/fkUf8IT4Y/wCgFYf9+RR/whPhj/oBWH/fkUf8IT4Y/wCgFYf9+RR/whPhj/oBWH/fkUf8IT4Y/wCgFYf9+RR/whPhj/oBWH/fkUf8IT4Y/wCgFYf9+RWJ4w8IeHbXwhqs8GjWccqW7FXWIAqfUGtOx8F+GXsLZm0OxLNEpJMQ54FWP+EJ8Mf9AKw/78ij/hCfDH/QCsP+/Io/4Qnwx/0ArD/vyKP+EJ8Mf9AKw/78ij/hCfDH/QCsP+/Io/4Qnwx/0ArD/vyKP+EJ8Mf9AKw/78ij/hCfDH/QCsP+/Io/4Qnwx/0ArD/vyKP+EJ8Mf9AKw/78ij/hCfDH/QCsP+/Io/4Qnwx/0ArD/vyKP+EJ8Mf9AKw/78ij/hCfDH/QCsP+/Io/4Qnwx/0ArD/vyKP+EJ8Mf9AKw/78ij/hCfDH/QCsP+/Io/4Qnwx/0ArD/vyKwfF3hLw9a6RBJBo1nG5vrVCVjAJBmUEfiMit7/hCfDH/AEArD/vyKP8AhCfDH/QCsP8AvyKP+EJ8Mf8AQCsP+/Io/wCEJ8Mf9AKw/wC/Io/4Qnwx/wBAKw/78ij/AIQnwx/0ArD/AL8ij/hCfDH/AEArD/vyKP8AhCfDH/QCsP8AvyKP+EJ8Mf8AQCsP+/Io/wCEJ8Mf9AKw/wC/Io/4Qnwx/wBAKw/78ij/AIQnwx/0ArD/AL8ij/hCfDH/AEArD/vyKP8AhCfDH/QCsP8AvyKP+EJ8Mf8AQCsP+/Io/wCEJ8Mf9AKw/wC/Io/4Qnwx/wBAKw/78ij/AIQnwx/0ArD/AL8ij/hCfDH/AEArD/vyKw9X0DSNH8SeGZtO063tZHvWVmhTaSPLbg47V3VFFcv4X/5D3ij/AK/1/wDRa11FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFc54l/5Dfhj/ALCDf+iZK6OiiiiiiiiiiiiiisDxv/yJGsf9ezVraf8A8g21/wCuKfyFWaKKKK5nxr4mn8O6dbJYW63OqahOtrZxOcLvP8TewHJrOXSPFsEkcsnjW3a6yDJbPaRiI+oH8X45rW1jxtoOgXv2HUr4R3nlrKIVRmZlJIyABzyDS6x420HQzbrfXhEtwnmRwxxs8hT+9tAJApX8aeH08NN4h/tGNtMQhWmUE7WJC4I6g5I4q/fa1YadNYxXU/lvfS+TbjBO9sZx+VZOpeP/AA3pN1dWt3qGLm1YLLCkbO65Gc4A6Y71t6ZqdlrOnQ3+n3CXFrMNySIcg1boooornPGv/IEt/wDsIWn/AKOSujoooorl/iD4gvPDPg+51KxVPOV0TzHUssQZgC5A6gZpPDVrqkX/ABMLnxWus6fJDuAECKA3XcrL2xnir8HivRrnw0/iGK7DaWiszTbTwFODx161DqnjXQNGS2a+v1i+1QefANhJkXj7oA5PI4pmo+OvD+l2lncXV4yi8j82GNYmaRk9doGQPrS6b458ParolxrNvfhdPtztknlRkUH2yOfwp+h+M9D8RXUlrp92WuY13mKWNo2K/wB4BgMj3FVl+IfhmS6e2ivzLMnmeYscTNs2Z3BsDg8H61oSeKdHi8Mr4ie8UaW0YkE+DyCcDjrnPataORZYkkT7rqGGRjg06iiiuX8Uf8h7wv8A9f7f+i2rqKKK5fwv/wAh7xR/1/r/AOi1rqKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK5zxL/wAhvwx/2EG/9EyV0dFFFFFFFFFFFFFFYHjf/kSNY/69mrW0/wD5Btr/ANcU/kKs0UUUVw/xI06/aLRtd061a7l0a8FxJbp96SMjDbR3IHNcZ461Twf4o043OjxS3HiSaWFYlSKQTLh1yCOgwua6uK3ST43tJJCGMehJtZlztPmHOD61nrqdj4S+KWv3niINBDfwQ/Ybx4yybFBDRggcHPOK57UtJvNY+H/jq/02wmjstRvY7mzgMZVnRGUvIF7bsE/hWzrPizS/Emu+C4tJae4WO/V5JRCwSP5D8rEj73tTbDxJonh34oeMpNYPleYYNk5hLLgR8pkDgn0710PwqtJ7fwvc3EltJawXl/Pc20Ei7SkTN8vHb1/Gu5oooornPGv/ACBLf/sIWn/o5K6OiiiisjxJq1lo+lNPqVrLPYu3lzbIvMCqe7L/AHa848OQ6d/wsHb4Hec6HNZynUY1DC2SQj5NmejZ6gVi2niCz0n4N6n4XuI7n+2oVnhktFgYsuXJ3dMbcHOa6xLZJvHXgbzYRIItJlYFkyFbaoz7Gs7xnrtxoHxS+02a24mbSlixcxPIrrvJ+XYCVx39ay9T01J/hZp50nU21CK21ZbvUZLOH5o8sWfbGf7uehroPDUWkax4z0/UYfFWpazd2kUnlhoAqRqwwQ5Cj249q1fhhaQjQ9XY26q02q3W8lMF/nxz68VxtjYXLeJYPhu8cn2Kx1JtSLn7rWv3kT/vs4r26iiiiuX8Uf8AIe8L/wDX+3/otq6iiiuU8NTRR6/4oDyIp+3rwzAf8s1rpvtVv/z3i/77FH2q3/57xf8AfYo+1W//AD3i/wC+xR9qt/8AnvF/32KPtVv/AM94v++xR9qt/wDnvF/32KPtVv8A894v++xR9qt/+e8X/fYo+1W//PeL/vsUfarf/nvF/wB9ij7Vb/8APeL/AL7FH2q3/wCe8X/fYo+1W/8Az3i/77FH2q3/AOe8X/fYo+1W/wDz3i/77FH2q3/57xf99ij7Vb/894v++xR9qt/+e8X/AH2KPtVv/wA94v8AvsUfarf/AJ7xf99ij7Vb/wDPeL/vsUfarf8A57xf99ij7Vb/APPeL/vsUfarf/nvF/32KPtVv/z3i/77FH2q3/57xf8AfYo+1W//AD3i/wC+xR9qt/8AnvF/32KPtVv/AM94v++xR9qt/wDnvF/32KPtVv8A894v++xR9qt/+e8X/fYo+1W//PeL/vsUfarf/nvF/wB9iuc8R3EB1rwyRNGQNQbPzj/njJXR/arf/nvF/wB9ij7Vb/8APeL/AL7FH2q3/wCe8X/fYo+1W/8Az3i/77FH2q3/AOe8X/fYo+1W/wDz3i/77FH2q3/57xf99ij7Vb/894v++xR9qt/+e8X/AH2KPtVv/wA94v8AvsUfarf/AJ7xf99ij7Vb/wDPeL/vsUfarf8A57xf99ij7Vb/APPeL/vsUfarf/nvF/32KwPG1xA3grWAs0ZP2ZuA4rWsLm3GnWoM8X+pT+Megqz9qt/+e8X/AH2KPtVv/wA94v8AvsUfarf/AJ7xf99ij7Vb/wDPeL/vsUfarf8A57xf99ij7Vb/APPeL/vsVGslkrl1e3Dn+IFc077Ra7t3nQ7sYzuGaR5rOUYkkgcejMDTvtNsBjz4sem8U1ZLNBhXgAzngjr61g6VoNpp3iPXNWe+hnGqPG/lMBiPYu3rnmug+1W4/wCW8X/fYpftVv8A894v++xR9qt/+e8X/fYo+1W//PeL/vsUfarf/nvF/wB9ij7Vb/8APeL/AL7Fc740uIG0W3AmjP8AxMLT+Mf89krovtVv/wA94v8AvsUfarf/AJ7xf99ij7Vb/wDPeL/vsUfarf8A57xf99ij7Vb/APPeL/vsUhubYjBniIPbeKak1pEMRyQIPRWAo82zLFvMg3EYJyMkUvn2uQfNhyBgHcOK5HVPDV23iWfXdD8RxWFxdRLFcRzQrOjBehGSCvWr/hTQLTw1a3YfUheXl9Obi6ncqu9zxwo4A9q3kls4s+XJAmeu0gZpy3Fqows0IHXhhWBpuiW9l4s1bX59RjuJ71UiiU4AgjUfdBzzk810H2q3/wCe8X/fYo+1W/8Az3i/77FH2q3/AOe8X/fYo+1W/wDz3i/77FH2q3/57xf99iuZ8SzRSa/4XCSIx+3twrA/8s2rq6KK4bSPD+kav4k8TTajptrdSreqoeaMMQPLXjmtv/hCfDH/AEAdP/78LR/whPhj/oA6f/34Wj/hCfDH/QB0/wD78LR/whPhj/oA6f8A9+Fo/wCEJ8Mf9AHT/wDvwtH/AAhPhj/oA6f/AN+Fo/4Qnwx/0AdP/wC/C0f8IT4Y/wCgDp//AH4Wj/hCfDH/AEAdP/78LR/whPhj/oA6f/34Wj/hCfDH/QB0/wD78LR/whPhj/oA6f8A9+Fo/wCEJ8Mf9AHT/wDvwtH/AAhPhj/oA6f/AN+Fo/4Qnwx/0AdP/wC/C0f8IT4Y/wCgDp//AH4Wj/hCfDH/AEAdP/78LR/whPhj/oA6f/34Wj/hCfDH/QB0/wD78LR/whPhj/oA6f8A9+Fo/wCEJ8Mf9AHT/wDvwtH/AAhPhj/oA6f/AN+Fo/4Qnwx/0AdP/wC/C0f8IT4Y/wCgDp//AH4Wj/hCfDH/AEAdP/78LR/whPhj/oA6f/34Wj/hCfDH/QB0/wD78LR/whPhj/oA6f8A9+Fo/wCEJ8Mf9AHT/wDvwtH/AAhPhj/oA6f/AN+Fo/4Qnwx/0AdP/wC/C0f8IT4Y/wCgDp//AH4Wj/hCfDH/AEAdP/78LR/whPhj/oA6f/34WsDxB4R8Ow6x4cSPRLFVlvmVwIR8w8mQ4P4gflW//wAIT4Y/6AOn/wDfhaP+EJ8Mf9AHT/8AvwtH/CE+GP8AoA6f/wB+Fo/4Qnwx/wBAHT/+/C0f8IT4Y/6AOn/9+Fo/4Qnwx/0AdP8A+/C0f8IT4Y/6AOn/APfhaP8AhCfDH/QB0/8A78LR/wAIT4Y/6AOn/wDfhaP+EJ8Mf9AHT/8AvwtH/CE+GP8AoA6f/wB+Fo/4Qnwx/wBAHT/+/C0f8IT4Y/6AOn/9+Fo/4Qnwx/0AdP8A+/C0f8IT4Y/6AOn/APfhaxPGHhDw7beD9Vmh0SxjkS3YqywgEGtSx8GeGX0+2ZtCsCxiUk+QvoKn/wCEJ8Mf9AHT/wDvwtH/AAhPhj/oA6f/AN+Fo/4Qnwx/0AdP/wC/C0f8IT4Y/wCgDp//AH4Wj/hCfDH/AEAdP/78LR/whPhj/oA6f/34Wj/hCfDH/QB0/wD78LR/whPhj/oA6f8A9+Fo/wCEJ8Mf9AHT/wDvwtH/AAhPhj/oA6f/AN+Fo/4Qnwx/0AdP/wC/C0f8IT4Y/wCgDp//AH4Wj/hCfDH/AEAdP/78LR/whPhj/oA6f/34Wj/hCfDH/QB0/wD78LR/whPhj/oA6f8A9+Fo/wCEJ8Mf9AHT/wDvwtH/AAhPhj/oA6f/AN+FrB8X+EfDtto8Dw6JYoxvrVSVhA4Mygj8RxW9/wAIV4Y/6AOn/wDfhaP+EJ8Mf9AHT/8AvwtH/CE+GP8AoA6f/wB+Fo/4Qnwx/wBAHT/+/C0f8IT4Y/6AOn/9+Fo/4Qnwx/0AdP8A+/C0f8IT4Y/6AOn/APfhaP8AhCfDH/QB0/8A78LR/wAIT4Y/6AOn/wDfhaP+EJ8Mf9AHT/8AvwtH/CE+GP8AoA6f/wB+Fo/4Qnwx/wBAHT/+/C0f8IT4Y/6AOn/9+Fo/4Qnwx/0AdP8A+/C0f8IT4Y/6AOn/APfhaP8AhCfDH/QB0/8A78LR/wAIT4Y/6AOn/wDfhaP+EJ8Mf9AHT/8AvwtH/CE+GP8AoA6f/wB+FrD1fw/pGkeJPDM2naba2srXrKzwxhSR5bccV3VFFcv4X/5D3ij/AK/1/wDRa1b8Z6jc6T4L1i/s5BHc29q7xuRnawHBxWB8L/E2pa3pNxY67KsmrWZR3cLt8yKRdyNj6HH4Vjp4y1u++Kllb21wq+HpbmWyWPYP3skaZds+mePwrsNW8eeHNEv3sr2/xNHjzRHGziLPTeVBC/jV3VfFGjaLpcOpXt9GltPgQsmXMpPICgcn8K43TPE6a58Trv8As3VHfTxoofZziKTeeSh6MBW9pPifTrDwTa6tqevR3kDZX7aYvL85txGAnXPbA9KuWHjTQNQ0y71GG+CW9p/x8GVGQx+mQRnmk0TxroPiG9ey0+8LXKJv8qSNo2Zf7wDAZH0rK+HN7dXttr5uriSYxaxcRxmRs7VBGAPau1ooooooooooooooooooooooornPEv8AyG/DH/YQb/0TJXR1594y8RLpvj7w5ay6i1rZwxzXd0ik/vRjai4HLcnpXXaF4h0zxJYG80q5E8KuY3+UqyMOoIPINadFFFFFFFFFFFYHjf8A5EjWP+vZq1tP/wCQba/9cU/kKs0UUUjZ2nbjOOM15j4pvPGHhWzTxBNr9vct9pRDpKW4CSKzAbUb727Bzmt3x7qniSx0C4n0KOC3EVpJcz3c3zeXtGdir3Y+vQVu+H72S68LadfXcgMklpHLK54GSoJNcLovi/W9Y+I1kRMsfh6/in+yQbBukWPjzCevJzj2rW8WW/iqGHU9Wt/EttpttaRmS2t/IDK4UZPmMeeTxxXQ+FdVuNc8K6Zql1B5M91brI8Y6AkdvasjxLY+Jp5Lu7tPEkGkWdvHuhUQByxAyTIW7ewpvh7xJret/Dmz1mHTY5dWnTaIWbYhO7bvyei4+b6Uz4darrOp2+tx65dR3N1Z6nJbhok2qAApwB6ZJrtKKKK5zxr/AMgS3/7CFp/6OSujoooorzzxnrninT9c0v7L5FlpDapb2rMcPLdBz82P7qjketddr8+rw6eF0S2hmvJXCB52xHEO7t3OPQVi/DrVdT1jwf8AadTuVub5biaIyBNobaxA4Hauf8Q3vjDwpHZazc69bXjzXkcMmlJbhUdXbGIz97IHc1ratqWt6740l8N6NqK6ZBZWqXF5crGJJCzn5UUHgDHJNYsni/XrLwf4utLy6iOtaCwRbxIwBIjYKuV6A4PNdB4TtdQlmgvZfGbatEYgZLYRx4yR3K8jFdXffavsM/2Exi62HyjLnaGxxnHavPbi+8UeFvFWgQXuuR6xHq0xgntBAqGI4zvTHO0e9aXxE8Salp1l/ZmgSImrSQSXLysNwt4IxlnI9/uj610HhO9uNS8IaPfXT+ZcXFnFJI+MbmKgk4rYoorl/FH/ACHvC/8A1/t/6Lauooorl/C//Ie8Uf8AX+v/AKLWl+IoJ+HWvgAk/YpOAM9q8+1ufUPCmn+G/EmmWsk0t9pKaXNGg58woDE5Hsc1pNoX/CPeIfhzpigs0JuDM+M5kaPLEn3JNYFjFHpOpeJLDXvEuoaNPcX0svkrbh1uYm6MpKndxxite+tLTwvd+AtRkN3J4esIpo2nuIiWhZ1+RnXHHp04qfSNRstY+LerX+mwSLatooXzzCUWc7j8wyOR2zWHpebDwf4B1i9t5JNKsLudrsCMt5ZYsEkI9Aa7bX/GtrN4S1XU/DNtHqM1uY1Z2t2MfJ+90y20c8VyGi393qfxS8OXMmvprMYgnBlgsDBHCSo+Td/F9O1dj8MVZbbxFlSM63c4yMZ5Fd3RRRRRRRRRRRRRRRRRRRRRRRXOeJf+Q34Y/wCwg3/omSujryW513Q7P4wavqmvboo9PtI7WzlaFmQufmcAgEbuRx710nw5s7r7Lq+tXVs9qdYv3uooHXayxYAUsOxIGfxrtqKKKKKKKKKKKwPG/wDyJGsf9ezVraf/AMg21/64p/IVZooornbfw5PYas+ptr+r3KAu/wBjeRTGc9gNueO3NeYeK7rwrqKHVtBbUh4vWcNa2pWRpFlzgho2yoGM813Pj7X7PTvAN7Z6tcxwajeabIqRYJ3ybMEDHuazbaeXxh8KoNJ8L6hbG8W2ggufNLKFXaN6kgZBIBFZLw+KrP4keF7Z9P0eHyLOWOJIJXKCIYDZJXg46Vb8b3HgXWLm+h199QttSt18tIx5qlyOVKKPlfmtfQPFV9ofgXw5L4otbg6hfTLa7Y4huUsTsLDt8oGapeN7nwPqt9dWHidr62uLdNqcyosoPIKbflfn1rS8D6xc6Z8OYL/xNI1rFCzLG86bGEO7EZZQODjFZHwy8TaPPq3iKzjvkNxe6vNPbx4IMibV+YcdODXqFFFFc541/wCQJb/9hC0/9HJXR0UUVz9/4ZnvdYF+niDVbZAVP2WGRREcexXPP1rj/ij4n0aG40Wwe+QXVprFtNPHtJKIDkseOmK7WfxZoUOhR6xJqMS6fKdqT4OGPIx09jXG/CfxHpcnhuexhufNvY7i5uDbqp3lN5IIz61zXie48M3rJq3hZtQPjD7SHt7ZhIzK5YbgyPlVGM88V0z6jD4P+Jt/qWt77ey1ewh23W0mNZU4ZCR0PcVH4eksJpfFvirWLZ10TVJ444lngY+ZEg27yuM7SfaqUUXh/UPH3h+bwLbBHt5HOoT2kZjhEG37r8AEk9B1ruk8X2Nx/bsaWl6x0h/LnCxZL5Gfkx14rzVv7AXxVod94ClvJ9XkuVS7jbzJFW2J+ff5n3PwrX13RfGFgPF2qeVpFzHfQyKsryyCSO2VTtQDbjPU+5rq/hsdRPgHRxqMdvGRaxiEQsTmPYNpbI4b1FdXRRXL+KP+Q94X/wCv9v8A0W1dRRRXJeHLm3g8QeKBNPFGTfqQHcA/6ta6Q39iwIN3bkHsZFpDfWBABurbA6DzF4oN9Ykgm6tiR0PmLxSNeac5Be5tWI6EupxStfWDqVa6tmB6gyKaBe6euMXVsMDHEi9KPtths2/arbb6eYuKRb3T0XatzbKvoJFAoW805AAtzarjph1FKL6xXOLu2GeeJFpf7Qsv+fy3/wC/q/40f2hZf8/lv/39X/Gj+0LL/n8t/wDv6v8AjR/aFl/z+W//AH9X/Gj+0LL/AJ/Lf/v6v+NH9oWX/P5b/wDf1f8AGj+0LL/n8t/+/q/40f2hZf8AP5b/APf1f8aP7Qsv+fy3/wC/q/40f2hZf8/lv/39X/Gj+0LL/n8t/wDv6v8AjR/aFl/z+W//AH9X/Gj+0LL/AJ/Lf/v6v+NH9oWX/P5b/wDf1f8AGj+0LL/n8t/+/q/40f2hZf8AP5b/APf1f8aP7Qsv+fy3/wC/q/40f2hZf8/lv/39X/Gj+0LL/n8t/wDv6v8AjR/aFl/z+W//AH9X/Gj+0LL/AJ/Lf/v6v+NH9oWX/P5b/wDf1f8AGj+0LL/n8t/+/q/40f2hZf8AP5b/APf1f8a57xFeWr614aK3UBC6gxYiReP3MnvXQ/2hZf8AP5b/APf1f8aa15pzfeuLU855detO/tCy/wCfy3/7+r/jR/aFl/z+W/8A39X/ABo/tCy/5/Lf/v6v+NH9oWX/AD+W/wD39X/Gj+0LL/n8t/8Av6v+NH9oWX/P5b/9/V/xo/tCy/5/Lf8A7+r/AI0f2hZf8/lv/wB/V/xo/tCy/wCfy3/7+r/jR/aFl/z+W/8A39X/ABo/tCy/5/Lf/v6v+NH9oWX/AD+W/wD39X/Gj+0LL/n8t/8Av6v+NYXjW+tH8F6uqXUDMbZsASDn9a1bC/sxp1qDd2+REn/LVfQe9WP7Qsv+fy3/AO/q/wCNH9oWX/P5b/8Af1f8aP7Qsv8An8t/+/q/40f2hZf8/lv/AN/V/wAaP7Qsv+fy3/7+r/jTftmnB9/2i13/AN7euaHu9OkIL3Fq2Om51NCXmnx52XNqueu11FL9usSwY3VtkdD5i0jXmnOwZri1YjoS6nFKb6wbGbq2OORmRaR7zTnIL3NqxHTLqcUrX1g67WurZl9DIppq3OmIwZZ7QEdCHWn/ANoWX/P5b/8Af1f8aP7Qsv8An8t/+/q/40f2hZf8/lv/AN/V/wAaP7Qsv+fy3/7+r/jXPeMry1k0W3CXUDH+0LQ4Ei/89k966H+0LL/n8t/+/q/40f2hZf8AP5b/APf1f8aP7Qsv+fy3/wC/q/40f2hZf8/lv/39X/Gj+0LL/n8t/wDv6v8AjTGudMdizT2hJ6kutBu9NKBDcWuwdF3rihbrTUbclxaKfUOopReacH3i4tQ397euaV73T5Bh7m2YehkU0fbrDbt+1W2308xaRLzT4wQlzaqD/ddRSi+sASRdWwJ64kXmkW805CSlxaqT1IdRmnG/sSMG7tyP+ui/40C/sQABd24A7CRf8aP7Qsv+fy3/AO/q/wCNH9oWX/P5b/8Af1f8aP7Qsv8An8t/+/q/41zfiO5t5/EHhcQzxSEXzEhHBP8Aq2rraKK4XSPD+kax4k8TTajp0FzKt6qh5FyQPLXitv8A4Qjwx/0BLP8A790f8IR4Y/6Aln/37o/4Qjwx/wBASz/790f8IR4Y/wCgJZ/9+6P+EI8Mf9ASz/790f8ACEeGP+gJZ/8Afuj/AIQjwx/0BLP/AL90f8IR4Y/6Aln/AN+6P+EI8Mf9ASz/AO/dH/CEeGP+gJZ/9+6P+EI8Mf8AQEs/+/dH/CEeGP8AoCWf/fuj/hCPDH/QEs/+/dH/AAhHhj/oCWf/AH7o/wCEI8Mf9ASz/wC/dH/CEeGP+gJZ/wDfuj/hCPDH/QEs/wDv3R/whHhj/oCWf/fuj/hCPDH/AEBLP/v3R/whHhj/AKAln/37o/4Qjwx/0BLP/v3R/wAIR4Y/6Aln/wB+6P8AhCPDH/QEs/8Av3R/whHhj/oCWf8A37o/4Qjwx/0BLP8A790f8IR4Y/6Aln/37o/4Qjwx/wBASz/790f8IR4Y/wCgJZ/9+6P+EI8Mf9ASz/790f8ACEeGP+gJZ/8Afuj/AIQjwx/0BLP/AL90f8IR4Y/6Aln/AN+6P+EI8Mf9ASz/AO/dH/CEeGP+gJZ/9+6wdf8ACPh2DV/DscejWirLfMrjZ94eTIcfmAfwre/4Qjwx/wBASz/790f8IR4Y/wCgJZ/9+6P+EI8Mf9ASz/790f8ACEeGP+gJZ/8Afuj/AIQjwx/0BLP/AL90f8IR4Y/6Aln/AN+6P+EI8Mf9ASz/AO/dH/CEeGP+gJZ/9+6P+EI8Mf8AQEs/+/dH/CEeGP8AoCWf/fuj/hCPDH/QEs/+/dH/AAhHhj/oCWf/AH7o/wCEI8Mf9ASz/wC/dH/CEeGP+gJZ/wDfuj/hCPDH/QEs/wDv3WJ4w8H+HbXwhqs8Oj2iSJbsVYJyDWpZeCvDT2FszaJZkmJST5fsKn/4Qjwx/wBASz/790f8IR4Y/wCgJZ/9+6P+EI8Mf9ASz/790f8ACEeGP+gJZ/8Afuj/AIQjwx/0BLP/AL90f8IR4Y/6Aln/AN+6P+EI8Mf9ASz/AO/dH/CEeGP+gJZ/9+6P+EI8Mf8AQEs/+/dH/CEeGP8AoCWf/fuj/hCPDH/QEs/+/dH/AAhHhj/oCWf/AH7o/wCEI8Mf9ASz/wC/dH/CEeGP+gJZ/wDfuj/hCPDH/QEs/wDv3R/whHhj/oCWf/fuj/hCPDH/AEBLP/v3R/whHhj/AKAln/37rB8XeEfDtrpEDw6NaKxvrVSQnYzICPxHFb3/AAhHhj/oCWf/AH7o/wCEI8Mf9ASz/wC/dH/CEeGP+gJZ/wDfuj/hCPDH/QEs/wDv3R/whHhj/oCWf/fuj/hCPDH/AEBLP/v3R/whHhj/AKAln/37o/4Qjwx/0BLP/v3R/wAIR4Y/6Aln/wB+6P8AhCPDH/QEs/8Av3R/whHhj/oCWf8A37o/4Qjwx/0BLP8A790f8IR4Y/6Aln/37o/4Qjwx/wBASz/790f8IR4Y/wCgJZ/9+6P+EI8Mf9ASz/790f8ACEeGP+gJZ/8Afuj/AIQjwx/0BLP/AL90f8IR4Y/6Aln/AN+6xNX8P6Ro/iTwzNp2nQW0rXrKXjXBI8tuK7qiiuX8L/8AIe8Uf9f6/wDota6iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiuc8S/8AIb8Mf9hBv/RMldHXAXN/4g8TeMtX0fStWXSLTSUjDSLCskk0jjPO7gKBWPe+ONbPwt1e8a5jtda0u/GnzXCINu4SKpcA8cq1dX4ettQheW8k8WtrcKxH9wI4gA2M5ynNcxoHjDVJNS0a8vdWint9TNybqzKKosVjyQcjnjGDn1rrvC+oanrs91rMzmHSZsJp9sUAZkH/AC1Y9fm7D0pfG/iWTwv4fN1bQLcX08qW1pCxwHlc4GfbvVPTtD8YwTWt1d+KY53LBrm1Nmoi291QjkexNL4Q1u/1TxF4qtbubzILG+EVuu0DYm3OOOtdfRRRRRWB43/5EjWP+vZq1tP/AOQba/8AXFP5CrNFUtYuJLTRb64hOJYoHdDjoQpIrhfAz6xrdhpeqXHjc3MksazT2KQw9+qnAyKsT3/iDxV4r1fTNH1YaTY6TsiaZIVkeaZhk/e4CistPiBrR8JC2KW//CRnVv7H8zb+68zP+tx6becetakF94i8L+MNI0zVtWGrWGrh41laBY3gmVd38PVTVbxjN4x8O6Pe+Jf+EgtgtrJvXTxbDyni3YClj827B616BYXJvNPtrlkKGaJZCp7ZGcVYrN1ufVIdPP8AY9rFcXrsETzn2omerN3IHoKwPhzq2q6v4cupdWukuryG9mgMioEU7WwMAdqwvFd74w8KWKa/Lr1vct9qRDpKW4CSKzAbUb724A5z7V1muWPiDUpohp2tR6TZrFud1hEkrP6fNwFFY/gzXtf8Q+Eb/dJatqdrcy2kV4UIim2nAkwP6VUi1DxH4d8eaLpF9rSazbaqknmIYFje3KDO4bf4T05r0Wiuc8a/8gS3/wCwhaf+jkro6KK8+1XXPFFv490OCTyLPR7q7kt1gGHknCqTvY/wj0ApfHWr6lb+LfD2k2muDSLa9WdppyiN90AgfNwK6nw9a3Vpp7G71xtX3tuSdkRQF9Bt4rhk8Y61qXxJ0lbO4WPw3czT2sabATcNGhLSZxnG7gY9Ku/bfE3izxBrsGlawmkWmkyi3jVYFkaaXbkl93Re2BVzQNa17xb4JhmtZ7aw1RZ2truYpvCFCQzIvqeMZ9ah8M6rrdp491DwvqWpLq8Edot0l35So8RLY8t9vGe4rvaK8uvvEGtalrviOP8A4SWLQI9HOIbd4FYzLtz5jFuoJ44q5ceONWj8D6DcywQWetau6wqbjiOLqTIQe2BnHvWv4SkuZpbiQeMYNfjCYMaRxjy39cp29jXP+I77xh4UisdYuNetrxpryOCTS0twqMrtjEZ+9kDua6jxlr11pemwWmmBTrOov5Fmjc7WxlnI9FHNQfDbVtQ1vwRaXuqXAuLwySpJKFC7trleg47V1tcv4o/5D3hf/r/b/wBFtXUUUVy/hf8A5D3ij/r/AF/9FrXUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUVzniX/kN+GP+wg3/omSujrzLxY3hOy8UXN5LrmpaRqzRKtwlluBuV/hGNpDHtkU3wpp2m+H/AV3N4ntZFstUv3umgvEMrKrEbPM684UE+9VNKi0a++J2lXfgi28qzhhkGqTW8ZjgZSPkXBABbPpWvf6TY+IfFdzpmnafBBZQEPq9zHEEa5fqsG7uO7flWL4Y1G+k8RaQsd/fPqclzPHqtg5PlW8K7tmF6KBhcY610/xO0q+v9As77ToGubnSr2K9EC9ZFU/MB74JqxYfEnw3qT2sNtczPdXDhBbi3fejHj5hjjHc1n+AkZfFvjfcrAHUlwSMZ+TtXfUUUUUVgeN/wDkSNY/69mrW0//AJBtr/1xT+QqzTZFLxsgYqWBG5eo9xXM2mhf8I/HeX2oa7qmo2vkt5kN0RIoXuQAuTxXAa0PCeqXmjxeBrNBriXsUiyWUDRCKMHLmQ4Axjsa2bHWbLwN458Tx64ZLa31GVLu0uDGzJL8uGUEDqD2rmjpV/H4dj8WPYXAjPiT+1Wg2HzBbH5dxXrnHOK6i61iz8ceOvDY0Qvc2emPJd3dz5bKifLhVycfMSelY+seMdD8W+JWstX1FbHw9pdxloJEYPfTKe4x/qwfzNelt4m0eKXSoPtS7tUyLIBTiTAzx6cVY1jTZNVsDaxahdWLFgfOtWAcY7cg1QjktPBuhl9X1m5nhEnNzeHc2T0HyiuQ+FPiPSpdP1Gwiud9419c3CQhCGaMtkEZHeub8VXnhXUkOq6C2pr4vWcPa2xWRnWXOCDG2VA6812Pi2/8NXAtNL8ZfaraT7MJfNQyJEzEYZdydSCOhrO8Dak+k+DNYkntr+Xw5b3Jj079wRMbY8FsDkgZJz1xWRp/9iQ+PNFuPAUt3czzuV1PfvkjW3xn5mflTnoBXtNFc541/wCQJb/9hC0/9HJXR0Vz8fhm4TXv7SPiHVWj8wv9jMi+T/u4xnH41xnjbxfoMfjbwyragoOnXshu8Ix8r5COePWui8Q6z4InWyl19bWeOWLzbaW4ty67T6HHFch4e0nUr3S/Ga+EM2OlXzxppf2kMqA4xMyA8gHPFVr6z8W6Z4p8E2Q03R4hamaO1SKVymBHhtxxwcc+5rT8SP4U0/xHqNwuuatp2rShRd2en7s3TY4wNpye2RU2i21p4S+Glta+Jbe9jg1C4d5/K3s0G8ll3svI4ABPrVbwOthbfEK4i8HvczeH5rYyX8koZkE+fl2u3JOOor0jSdc0/W/tf2Cfzfsk7W83BG2Reo5rPu/DNxda4NSXxDqsMYdW+yRyKIuO2MZwe/NeYal/wjmra9r7+PpLi31SCd4tOQI6iOAcxtGVHzEnnmpr/wDtO78G+Dtc8T2El1DY3pa9V4ct5ByqSOnsME1s6RNpup/Ewaz4YtCNMtNNdLyeCEpHO5OVRRgbmGK53xLdeGb949V8LPqJ8X/aQ9vbssjMrlvnDI/yqMZ5rsNR0bxiPGE2vW0OlXKC0WC2S5ldTb5GZMADGSe/pTPgwdU/4QhFvoYI7cTS+QUJ3k+Y27cCOOelejVy/ij/AJD3hf8A6/2/9FtXUUUVy/hf/kPeKP8Ar/X/ANFrXUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUVzniQE634Y4/5iDf+iZK6OmPDE7h3iRmHRioJFOZVdSrKGU9QRkUkcUcS7Y0VF9FGBShVXO1QMnJwOppoijWRpFRQ7dWA5P40+o1giSQyLEiuerBQCaeEVSSqgE8kgdaWiiiiisDxv/yJOsf9ezVraf8A8g21/wCuKfyFWaKOtRxwRREmOJEJ6lVAzSyRRygCSNHA5G4ZxT8DGMcUyOKOIERxqgPJCjFMaztWJLW0JJ7lBWPL4ajuPF1trlxKHSztzFaW4TAiZvvP7kjArepskaSrtkRXX0YZFMS2gibdHDGjeqqAacIYhL5oiTzP720Z/OiSKOUYkjVx6MM04ABdoAA6YpscMUWfLjRM9dqgZp9Fc541BOiW+B/zELT/ANHpXR0UVC1pbOxZreIk9SUHNOa3hdAjxRsq9FKggU8AKAAAAOgFBRSwYqCR0JHSmmGJpBIYkLjoxUZH405lDKVYAg9QRUTw7baSO22QsVO0heFOODiszwx4eh8NaKljHIZpWdpZ52GDNIxyzGtmo3gilYNJEjkdCyg4p5AYEEAg9QaRI0iXbGiovoowKQQxCQyCNBIerBRn86fSKqou1VCj0AxS1y/ij/kPeF/+v9v/AEW1dRRRXBaX4es9X8S+JprmW8R1vVUCC7kiGPLXspAz71s/8IRpX/Pxqv8A4Mp//iqP+EI0r/n41X/wZT//ABVH/CEaV/z8ar/4Mp//AIqj/hCNK/5+NV/8GU//AMVR/wAIRpX/AD8ar/4Mp/8A4qj/AIQjSv8An41X/wAGU/8A8VR/whGlf8/Gq/8Agyn/APiqP+EI0r/n41X/AMGU/wD8VR/whGlf8/Gq/wDgyn/+Ko/4QjSv+fjVf/BlP/8AFUf8IRpX/Pxqv/gyn/8AiqP+EI0r/n41X/wZT/8AxVH/AAhGlf8APxqv/gyn/wDiqP8AhCNK/wCfjVf/AAZT/wDxVH/CEaV/z8ar/wCDKf8A+Ko/4QjSv+fjVf8AwZT/APxVH/CEaV/z8ar/AODKf/4qj/hCNK/5+NV/8GU//wAVR/whGlf8/Gq/+DKf/wCKo/4QjSv+fjVf/BlP/wDFUf8ACEaV/wA/Gq/+DKf/AOKo/wCEI0r/AJ+NV/8ABlP/APFUf8IRpX/Pxqv/AIMp/wD4qj/hCNK/5+NV/wDBlP8A/FUf8IRpX/Pxqv8A4Mp//iqP+EI0r/n41X/wZT//ABVH/CEaV/z8ar/4Mp//AIqj/hCNK/5+NV/8GU//AMVR/wAIRpX/AD8ar/4Mp/8A4qj/AIQjSv8An41X/wAGU/8A8VR/whGlf8/Gq/8Agyn/APiqP+EI0r/n41X/AMGU/wD8VR/whGlf8/Gq/wDgyn/+Ko/4QjSv+fjVf/BlP/8AFVg6/wCENNh1fw6iz6kRLfMrbtQmJA8mQ8Zbjp2re/4QjSv+fjVf/BlP/wDFUf8ACEaV/wA/Gq/+DKf/AOKo/wCEI0r/AJ+NV/8ABlP/APFUf8IRpX/Pxqv/AIMp/wD4qj/hCNK/5+NV/wDBlP8A/FUf8IRpX/Pxqv8A4Mp//iqP+EI0r/n41X/wZT//ABVH/CEaV/z8ar/4Mp//AIqj/hCNK/5+NV/8GU//AMVR/wAIRpX/AD8ar/4Mp/8A4qj/AIQjSv8An41X/wAGU/8A8VR/whGlf8/Gq/8Agyn/APiqP+EI0r/n41X/AMGU/wD8VR/whGlf8/Gq/wDgyn/+Ko/4QjSv+fjVf/BlP/8AFVi+L/B+m23hDVZkn1IsluxAfUJmH4gtg1p2XgrS3sLZjcapkxKeNRm9B/tVP/whGlf8/Gq/+DKf/wCKo/4QjSv+fjVf/BlP/wDFUf8ACEaV/wA/Gq/+DKf/AOKo/wCEI0r/AJ+NV/8ABlP/APFUf8IRpX/Pxqv/AIMp/wD4qj/hCNK/5+NV/wDBlP8A/FUf8IRpX/Pxqv8A4Mp//iqP+EI0r/n41X/wZT//ABVH/CEaV/z8ar/4Mp//AIqj/hCNK/5+NV/8GU//AMVR/wAIRpX/AD8ar/4Mp/8A4qj/AIQjSv8An41X/wAGU/8A8VR/whGlf8/Gq/8Agyn/APiqP+EI0r/n41X/AMGU/wD8VR/whGlf8/Gq/wDgyn/+Ko/4QjSv+fjVf/BlP/8AFUf8IRpX/Pxqv/gyn/8AiqP+EI0r/n41X/wZT/8AxVYXi3whpttpEDpPqRJvrVfm1CZhgzKD1at3/hCNK/5+NV/8GU//AMVR/wAIRpX/AD8ar/4Mp/8A4qj/AIQjSv8An41X/wAGU/8A8VR/whGlf8/Gq/8Agyn/APiqP+EI0r/n41X/AMGU/wD8VR/whGlf8/Gq/wDgyn/+Ko/4QjSv+fjVf/BlP/8AFUf8IRpX/Pxqv/gyn/8AiqP+EI0r/n41X/wZT/8AxVH/AAhGlf8APxqv/gyn/wDiqP8AhCNK/wCfjVf/AAZT/wDxVH/CEaV/z8ar/wCDKf8A+Ko/4QjSv+fjVf8AwZT/APxVH/CEaV/z8ar/AODKf/4qj/hCNK/5+NV/8GU//wAVR/whGlf8/Gq/+DKf/wCKo/4QjSv+fjVf/BlP/wDFUf8ACEaV/wA/Gq/+DKf/AOKo/wCEI0r/AJ+NV/8ABlP/APFVjap4fs9H8S+GZraW8d2vWUie7klGPLbsxIz713tFFcv4X/5D3ij/AK/1/wDRa1f8WatPoXhPVNVtkR57W3aVFf7pIHek03xBbSaFpl7qNzb2015bLNtZwoPyhmxnsM1e03VtP1i3M+m3sF3EDtLwuGAPpxUEPiLRbjU202HVLSS9UkGBZQXyO2KSbxJolvqK6fNq1nHeMdohaZQ2fTHrVm71SwsX2Xd7BA3lmTEkgX5R1PPaksNW07U7M3dje29xbjOZYpAyjHXJ7VBY+ItG1S6e1sdUtLm4Tlo4pQzD8KW/8Q6NpVzHbX+p2ltPJ9yOWUKT+BqrJq01v4huUuL3Tk0yKyE+0ufOU55c9tmO9ZXhr4jaHr2mS3ct7aWrxSOGiacEhFbaHPsePzrqLm/s7OGOa5uYoo5GVEZ2ADM3QD1JqzRRRRRRRRRRRRRRRRRRRRRXOeJf+Q34Y/7CDf8AomSujrjfE3iK507xr4f06O8itrJ0mub9pMAeWq4HJ6fMa6mw1Gy1S0W6sLqG5t24EkThlP4irNFFFFFFFFFFFYHjf/kSNY/69mrW0/8A5Btr/wBcU/kKs0UUUjEhSQMkDp61534g8U+MfDNn/b2o2GmDSEmVJrRHYzojNgNu6E89K6LV08WT3UcmhXelxWbRglbuJ2fd9QelY/g/xXrWo2Gv3mqxW91b6dO0MD2EbZuCg+baCeeeBU3g/wAV6xrniXW9O1XT4rEWaQyRQq251VwT856Z9h0qHxf4y1nRtRtoNO0kGzF3DBc3lycKfMIG2MD7xwevQV3dcLqHibX9R8XajoHh5dPt202FJZ5b/cfMLDICqO3qas6H40udT8EtrTaVLNeJM1uba1O4SyB9uUP90nnPYVJ4D8R6p4ittWOr21vbXNlfvbeVASQoABwT3PPWutooornPGv8AyBLf/sIWn/o5K6OiiiiuD8U+M9Z0rXbC0sdJAsH1CCzuLy54DGQ9IwOuB36V02vza3HbRR6FbW0lzK+1pbliEhX+8QOW+lYXhbxPq1xr+r+HvEENqt9p6JMLi0J8uSNuhIPKn2qnZ+I/FPiVjfaEuk2umGRlt1vSxluVU4LgD7oODjrV/wAdeKdX8PaXK+k6V9qnjgM8s8pxDCo65PUn0ArbTVnTwxHq0lvJPIbZZjDAuWclQcKPxrC8GeKNY13WtdsdXsIbJ7F4vLhjbcyq67sMehPTpXRa7fyaXoGoX8Sq0ltbvKqt0JVSea5jwpeeNNXtNM1S+utGFhdRLM8UMTiQKwzgEnGagn1zxZqfjXWdG0WXSre301IW3XUbszlwT2PtXa2C3i2EI1B4Xuwv71oQQhPsDzirNFFcv4o/5D3hf/r/AG/9FtXUUUVy/hf/AJD3ij/r/X/0WtHxG4+HPiD/AK8pP5VxV9YWmp3nwvtL2FJ7d4HZo36NiFSM+vNTXVo2leM/GdroMIt5JdDWZIYBtBl5AYAd8VoeBB4L/wCEY8PNF/Z/9obFKltvn+fj5s/xbs5zXCeK9YXVtB8QXFnpHhyxtEuJEdrqX/TXkVvvqByGzyK6bXJNIk8Z+BJPEDQm3fTnw1x90yFVxu7fnxUcF9pGl+IfHd/pdot1ocNhH9pgt/8AVSz4O4Ljjp1xWPbXk8njHwZObXw3ZRyXI8qPSpd0yxshyrkcY/rXQeGf+EdbVPF3/CViyOo/2i/mfbtufI48vbu/hx6VqW0drN8YJI4kje1fw8iopGVZPMOBz2xWb4D0nQtY8E6tojx2a30s93BKgVRKg3nGe+Bwaz/DN3ceLtX8N+H71SzeG98mpKwyDLGdkX14+avZqKKKKKKKKKKKKKKKKKKKKK5zxL/yG/DH/YQb/wBEyV0deT3knh/VfjNqUuu3NskWj2EccUVy4CMzZZmweDj0ra+F0CNaa5qdrB9n03UNSkmsogu1TGABvA7BiCa76iiiiiiiiiiisDxv/wAiRrH/AF7NWtp//INtf+uKfyFWaKKK5yz07xNBrRubzX7abTAzH7MLQKwXsN+e1cfqWsaf8Q9ejsjqFtB4Z0y5D3DyTKpvpl6IoJ+4D1Peuo8e+IToXg+WWxYPe3m21sVUj55ZOFx9Ov4VoeGdGt/CnhSy03eqpawjzZWOAW6sxPucmuR8L6pp7fFjxay31sRNHaiMiVfnO08DnmrHxT1Gxj0nTrd7y3WZdUtWaNpAGA39SPSuqv8AxPo2mwWc9zfxCG8nW3gdDvV5D0GR9K811u003xj8QNZtdX1YaGdMiEEBhlEM1wrrkuzH7yDpiun+G2uef4Pl+2vbR2unXL2cN2gEcVxGhAWQDoM5/Oq3w11Gxkv/ABTEl5btJLrczxosgJddq8gdxXodFFFc541/5Alv/wBhC0/9HJXR0UUVzuoab4nm1kT2OvW9vp+VJtmtA7EDr82e9c98T9SsUXw9A97brLHrdqzoZACoz1I7Cug8RappU2nQWsviD+zRetiC7glAyVPIDdK4fwin2Dxj4j0LTr0a1BcWf2iXUXO+VZSCojZxwfUDtXL2dh4Vh+Er3bytB4ltg8cY+0MLhLkOQqqucgZxxjpXo/jK/wDJ+E95FqtzDFqEumAujuAzPtGcDvzXQ+HNRspPC1jLHeQPHFaxiR1kBCfKOp7VyvhDU9Pf4k+Mgl9bMZpbbysSg7/3fb1rqJNb0HWINZ06W7jaKzzBfq5KiPcOhJ9q851bTtG8M6l4fPgrVJjfS3scRsobszJLAfvblycADvXSeI9A8DXmo6hqV/qa2eobQs80N8YnQqOOAeo+laPwyvNSvvAljPqkss0xLiOaZcPJEGIRj7kYrrqKK5fxR/yHvC//AF/t/wCi2rqKKK5fwv8A8h7xR/1/r/6LWujuLaC7t5Le5iSaGRSrxuuVYehHeov7Nsd9q/2SDdaAi3OwZiGMfL6cccU9bK1W9e8W3iFy6BGmCjcyjoCfSqcHh3RbbUG1CDSrKO8Y5M6QKHJ9c4pJfDWhz3kt3NpFjJcyqVklaBSzg9cnHNY+reERqfi/Sb+SK0fS7S0lt5LeRc7t2NuFxjAxXQ2elafp1kbOzsre3tjnMMUYVTnrwKq2vhnQrFw9ro9jAyyeaGjgVSH9Rx15p994e0bU7pLq+0uzubhOFklhVmH4kVaWxtEvPti20S3PliLzQg3bAchc+ntUMGjaZa6jLqEGn20V5MMSTpEA7/U9TWJ4S8M3Ojajrup6g0D3uqXhlJh6LGBhF6dcda6miiiiiiiiiiiiiiiiiiiiiuc8SAnW/DHH/MQb/wBEyV0dZd34b0O/Z2vNIsp2d/MYyQKxZumTkda0o40ijWONFRFGFVRgAe1OooooooooooorA8b/APIk6x/17NWvp/8AyDbX/rin8hViiiikIDKVIBBGCDWCfA/hViSfDumZJyf9GT/CtSTTLCVbZZLOBxasGtw0YPlEDAK+hqeeCK6gkgnjWWGRSro4yGB7EVj2/g3wza3Ec9voOnRTRsGR0t1DKR0IOKlvvC2gapdNdX+jWNzcMADJNArMce5FTR6BpEVpBaJplotvbyebDEIl2xv/AHlHY+9JqPh/RtXkWTUdLs7t04VpoVcj8SKmm0nTrjTf7Omsbd7LAH2doxswOR8vSqdl4U8P6bdpdWOi2FtcJnbLFAqsM9eQK2KKKK5zxqCdEt8D/mIWn/o9K6Oiiiise98KeHtSu3u77RbC5uHxullgVmOOnJFTnQNIbTF0xtMtDYp923MKlB9BjFTafpWn6TAYNOsre0iJyUhjCAn8KgPh/Rjqf9pHSrM32c/aPJXfn1zjNGpeH9H1mRH1PTLS8dBhTPErlR7ZotfD2jWVlNZWul2kNrP/AK2GOIKr/Ud6r2vg/wAN2VzHc2uhafDPGdySR26qyn1BAq+ul2C/attlbj7Wc3H7sfvT/tev41Bp/h7RtJnabT9Ks7WVhhnhhVSfxApl34a0O/vPtd3pFjPcZz5skCs35kVqKoVQqgBQMADoKWiiuX8Uf8h7wv8A9f7f+i2rqKKK4LTPD1nrHibxNNcTXqOt6qgQXTxDHlr2UgZrY/4QfTP+frVf/BjN/wDFUf8ACD6Z/wA/Wq/+DGb/AOKo/wCEH0z/AJ+tV/8ABjN/8VR/wg+mf8/Wq/8Agxm/+Ko/4QfTP+frVf8AwYzf/FUf8IPpn/P1qv8A4MZv/iqP+EH0z/n61X/wYzf/ABVH/CD6Z/z9ar/4MZv/AIqj/hB9M/5+tV/8GM3/AMVR/wAIPpn/AD9ar/4MZv8A4qj/AIQfTP8An61X/wAGM3/xVH/CD6Z/z9ar/wCDGb/4qj/hB9M/5+tV/wDBjN/8VR/wg+mf8/Wq/wDgxm/+Ko/4QfTP+frVf/BjN/8AFUf8IPpn/P1qv/gxm/8AiqP+EH0z/n61X/wYzf8AxVH/AAg+mf8AP1qv/gxm/wDiqP8AhB9M/wCfrVf/AAYzf/FUf8IPpn/P1qv/AIMZv/iqP+EH0z/n61X/AMGM3/xVH/CD6Z/z9ar/AODGb/4qj/hB9M/5+tV/8GM3/wAVR/wg+mf8/Wq/+DGb/wCKo/4QfTP+frVf/BjN/wDFUf8ACD6Z/wA/Wq/+DGb/AOKo/wCEH0z/AJ+tV/8ABjN/8VR/wg+mf8/Wq/8Agxm/+Ko/4QfTP+frVf8AwYzf/FUf8IPpn/P1qv8A4MZv/iqP+EH0z/n61X/wYzf/ABVH/CD6Z/z9ar/4MZv/AIqj/hB9M/5+tV/8GM3/AMVR/wAIPpn/AD9ar/4MZv8A4qsLX/CGmw6v4eRbjUyJb5lJa/lJA8mQ8fNx07Vu/wDCD6Z/z9ar/wCDGb/4qj/hB9M/5+tV/wDBjN/8VR/wg+mf8/Wq/wDgxm/+Ko/4QfTP+frVf/BjN/8AFUf8IPpn/P1qv/gxm/8AiqP+EH0z/n61X/wYzf8AxVH/AAg+mf8AP1qv/gxm/wDiqP8AhB9M/wCfrVf/AAYzf/FUf8IPpn/P1qv/AIMZv/iqP+EH0z/n61X/AMGM3/xVH/CD6Z/z9ar/AODGb/4qj/hB9M/5+tV/8GM3/wAVR/wg+mf8/Wq/+DGb/wCKo/4QfTP+frVf/BjN/wDFUf8ACD6Z/wA/Wq/+DGb/AOKrF8X+D9OtvCOqzJcamzJbsQHv5WH4gtzWnZeCtMewtmNzqoJiU8ajN6D/AGqn/wCEH0z/AJ+tV/8ABjN/8VR/wg+mf8/Wq/8Agxm/+Ko/4QfTP+frVf8AwYzf/FUf8IPpn/P1qv8A4MZv/iqP+EH0z/n61X/wYzf/ABVH/CD6Z/z9ar/4MZv/AIqj/hB9M/5+tV/8GM3/AMVR/wAIPpn/AD9ar/4MZv8A4qj/AIQfTP8An61X/wAGM3/xVH/CD6Z/z9ar/wCDGb/4qj/hB9M/5+tV/wDBjN/8VR/wg+mf8/Wq/wDgxm/+Ko/4QfTP+frVf/BjN/8AFUf8IPpn/P1qv/gxm/8AiqP+EH0z/n61X/wYzf8AxVH/AAg+mf8AP1qv/gxm/wDiqP8AhB9M/wCfrVf/AAYzf/FUf8IPpn/P1qv/AIMZv/iqwvFvg/TbbSIHW41Nib62XDX8pGDMoPVutbv/AAg+mf8AP1qv/gxm/wDiqP8AhB9M/wCfrVf/AAYzf/FUf8IPpn/P1qv/AIMZv/iqP+EH0z/n61X/AMGM3/xVH/CD6Z/z9ar/AODGb/4qj/hB9M/5+tV/8GM3/wAVR/wg+mf8/Wq/+DGb/wCKo/4QfTP+frVf/BjN/wDFUf8ACD6Z/wA/Wq/+DGb/AOKo/wCEH0z/AJ+tV/8ABjN/8VR/wg+mf8/Wq/8Agxm/+Ko/4QfTP+frVf8AwYzf/FUf8IPpn/P1qv8A4MZv/iqP+EH0z/n61X/wYzf/ABVH/CD6Z/z9ar/4MZv/AIqj/hB9M/5+tV/8GM3/AMVR/wAIPpn/AD9ar/4MZv8A4qj/AIQfTP8An61X/wAGM3/xVH/CD6Z/z9ar/wCDGb/4qsbU/D1no/ibwzNbzXru16ykT3TyjHlt2YkZrvqKK5fwv/yHvFH/AF/r/wCi1rqKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK5zxL/yG/DH/AGEG/wDRMldHXD3/AIj8Rar4o1DRPDENjGNMRDc3N7uIZ2GQihfbqaoXPxD1Bvhtf63DaQQ6xY3QsriCQlo0lEio3TnGGzXRaN/wlcMzz65daXNZrEWC2kTq+7r1JxisLQ/G2r3mo6PPexWY0zWmmW2SLIkg8vJy5PByBz6V0Hh7XLvxBf3t3DFGmiRt5NrKQd9wwPzOP9jPA9cVP4s8SQeFdAl1KaJ53DLHDAn3pZGOFUfjWTp0vj9p7a5voNG+zyuPOtY2cSQoeuGPDEVa8MeI7vWtd8R2VxHEsWm3gghKZyy7c8+9dPRRRRRWB43/AORI1j/r2atbT/8AkG2v/XFP5CrNFFITgE1534c1jxx4lt21C3uNFgsxdSRCJ4nL7Ucr1z14ro9ah8XPeKdFu9Kitdg3C6idm3d+h6VgeGfGupvo3iLVdfNo9lpUzRRT2ilRMUHzYB98AVoaLe+M9Qu7a8u00i3sZsO9kGZp40IyMt03e2KivfEeu6rrl9pvhtdPhg09hFcXt8SVaUjOxFHoOprrNON6dPh/tHyfte3975GdmfbPOKkupJYrSWSCHzplQlI843HsM9q4K78U+LPDmraQ3iC1019P1O5W12WjN5lu7fdyTww9a09Z8R6vceIJdC8Ox2azWsSy3d5eMfLi3fdQAcljjNXnTxbJoEIiuNKTVg/7xyjtCyew6g9K5+x17xdB8QLHw7qEulXUclu9zdG2jdWhQcL1PUmvQqKK5zxr/wAgS3/7CFp/6OSujoorhb/xlrEXjbStMi0kQaVdXL2zXNwcPKyqSSi9l46nrVrxX4l1Oy1/SvD2jRWovtQV5BcXZPloqdQAOWY+lR+HfFGtXsviDS76ytptW0jaA1q5EVwWUsoBP3TxgjtVKTxP4q0DX9Hg8Q22nPY6tN9nT7GW328hGQDn7w96nu/EXiXWdf1PTfC0FgkOlkJPcXu4iWUjOxQOmO5qqvxHu7nwrYT2mmL/AG5eXx037K7/ALuKdSdxZv7oAzV/R/EevWXi6Pw54mismlu7dp7S6s8hX2/eQqeQR1rtaK88k8UeK9audYn8NWmnrYaVO9uRdlt91In3wuOFHYE1q2niXVfEnhLTNU8OWVv597xJ9qchLfHDZA5bBGMCofC/ifV7jxBq/h3Xre2GoWEazLPak+XLG3Tg8g1neFdZ8b+J9Pg1ZLjRYbKSZ1MJicuEVyp5zjPFdP4t8QN4e0bzbeEXGoXDiCzt/wDnrKeg+g6n2FQeAdfvfE3hC11PUI4o7p3kSRYs7QVcrxn6V0tcv4o/5D3hf/r/AG/9FtXUUUVy/hf/AJD3ij/r/X/0WtdRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRXOeJf8AkN+GP+wg3/omSujrzzWtPttL8UajrOm+MLTR7i4jT+0ILhUkB2j5WAJBU4rM8G6Lpl34C1T/AISaffp+s6nJcrJct5JmGRtY4xjJTOKWwis9H+Juk6f4V1Oe6sriCU6ja/aTcRRKB8jZJO0k8dan1Lw5pN14nfRdBtjFJtMmp3CSMRbRNyY4xnCtJ3xjioPC/iHUlvtAWO+he1v55rV9JSJR9jjj3BSMfNxtGc+ta/xYtp/+Ef07VIonmj0vUYbueNBkmNT8xx7ZzXSW/i/w9dQW0sOs2TrdMqwhZgWdj0GOua5rwB/yNvjgdxqS5/74rv6KKKKKwPG//Ikax/17NWtp/wDyDbX/AK4p/IVZoornrXT/ABHBrDXN7r9tNpoLH7MLMIwXnA37u30rz7xZpXhPRdAudU8M6xJbav5vmWq2d8zmaUt93y8kEE9eK7Dxj4iu9E8BLMVxrF7HHbQRDqZ5ABx9CSfwrI8T+F20f4H3eiWiGSSC1VpNo5dgwZ2+vU1gX8Phy2k8J3vhC4zrFzeQg+TOZHkhI/eeYMngDrml0zSfCja/40t/FLRx3CXzzxiecoPKZcq6DI59xzXXfDjU5Lf4e6ZNrV4U8yRo7aS7fa8iFyIxz1JGMV2lzcw2dtLc3EixQxKXd2OAqjkk15pYanY+PPFFnrt5fW1vomnSN/ZtrLMoe5l6ecy54A/hFUm0rw7N8VPFMfikRqs8UE9qLiYpG6BNrEcgEgitv4ZXptfCWqXE9xI2j219cGwmnbP+iqeDk9R1xUvw1gl1NNT8YXaET6zNugDdUt14Qfj1rvaKK5zxr/yBLf8A7CFp/wCjkro6K5+PTfEi679pfXrdtN8wn7ILMBtvpv3friub8a6rpyeNfB4a/tgYb6TzQZV+T92evPFU/Hk1j4g8XaJoF9dxWGnNCb1NSVgsjuDgJFJ0X1PrS+AdRtfD+o+I9HW/ivNG01RdHUyRuy2SyyOOGYY60aRqNn438V2viS+vba30uxLLpNpJMoklc8GZlzx6KKk8H6lYeHPFHjKw1a9itZWvvtiGdggeJl+8ueuMVjaHotrrHhKfU7nVTo73OvS6hpd1IQuDnC8NjIIB471d0iG91f4qadPJr0esjSrWUzzW8KpDEz4CoME5Y4yee1en2t/aX3m/ZLmKfyXMcnluG2MOqnHQ+1Y93pviWTXBcWuvW8Onb1P2VrMM20dRv3d/pXGapbW3hmbXLzTPG1vYWVzNJPc2TRpNIsxHzBOcgk9sGpvD2rw/Dn4T6W+rq32ubPk2x4eSR2LKvseRn0rT8D2NvbPqWr6nqdnc65qf767EMysIIwPljGOyjv61ynizS/C/h7QDqHhLV5bfVxOptIrO9aTznZxlfLyQQcnPFbeqDxN/wmw1Wfw1NqFrZ2YSy8q4jQLIy/vHIY5z2FSfBa7urjwMkU1g8EUc0vlzGRWEuZGJwByMHjmvRq5fxR/yHvC//X+3/otq6iiiuX8L/wDIe8Uf9f6/+i1rqKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK5zxICdb8MYB/5CDf+iZK6Osy98OaLqV4t3e6VZ3FwuMSywqzce5FWruws7+1Nrd2sM9uf+WUiBl/KotN0XTNHRk03T7a0V+WEMYXP1xU8FnbWsk0kEEcbzvvlZFALt6n1NQQaNplrqEt/b2FtFeS/6ydIwHb6mrpAZSrAEHgg96yrXwxoNlefbLXR7GG5znzUgUNn1zir8Fja2s08tvbxRSTtvlZFAMjep9TU9FFFFFYHjcZ8E6xj/n2atfT/APkG2v8A1xT+QqxRRQQCMHkVk2/hjQrS++22+j2MV1nPmpAoYH64q9c2FpeSQPc20UzwP5kRkQMUb1GehqcgMCCAQeCDWdY+H9H0y6kubHS7S2nk+/JFCqsfxFLf6Bo+qXEdxf6ZaXM0f3JJolZl/E1l+IvDcmv6poqSmJdKsJ/tUkf8TyKP3YHbaCST9K6GeCK6gkgnjSWGRSro4yGB7EVip4I8LRurp4f05WU5Ui3UYP5Ve1HQtK1fy/7R061u/K+550Qbb9M1ZeytZLJrJ7eI2rJ5ZhKDYV9MdMU+CCK2gjggjSKKNQqIgwFA6ACpKKK53xqCdEt8An/iYWnT/ruldFRRWJc+DvDV7cyXFzoWnzTSHc8jwKWY+pOKtXOg6ReWEVjc6bazWsQAjhkiBVMeg7U6HRNKttOk0+DTrWOzkBDwLEAjA9cjvVCPwV4XhlSWPQNOSRCGVlt1BUjoRxV3UdB0jV5I31HTLS7eP7jTRBiv0zUt7pOn6jZizvbK3uLYYxFJGGUY6YFJbabbaVp72+k2dvbAAlI40CKWxxnFZfg3w43hvRWhndZL66me6u5U6NK5ycew6V0NZb+GtDk1H+0X0mya8zu88wqXz65x1qbUtG0zWY0j1Kwt7tEO5FnjDhT6jNV9P8MaFpU7T2GkWdtKylGeKEKSp6jjtS2vhnQrG9+2WukWUNz181IFDfnitUgEYPQ1BZ2Vrp9sLezt4reFSSI4lCqCTk8D3qeuX8Uf8h7wv/1/t/6LauooorgNN8OWWteJvEs1zLeI6XqqBBcvGMeWvYHrWx/wgelf8/Oqf+B8n+NH/CB6V/z86p/4Hyf40f8ACB6V/wA/Oqf+B8n+NH/CB6V/z86p/wCB8n+NH/CB6V/z86p/4Hyf40f8IHpX/Pzqn/gfJ/jR/wAIHpX/AD86p/4Hyf40f8IHpX/Pzqn/AIHyf40f8IHpX/Pzqn/gfJ/jR/wgelf8/Oqf+B8n+NH/AAgelf8APzqn/gfJ/jR/wgelf8/Oqf8AgfJ/jR/wgelf8/Oqf+B8n+NH/CB6V/z86p/4Hyf40f8ACB6V/wA/Oqf+B8n+NH/CB6V/z86p/wCB8n+NH/CB6V/z86p/4Hyf40f8IHpX/Pzqn/gfJ/jR/wAIHpX/AD86p/4Hyf40f8IHpX/Pzqn/AIHyf40f8IHpX/Pzqn/gfJ/jR/wgelf8/Oqf+B8n+NH/AAgelf8APzqn/gfJ/jR/wgelf8/Oqf8AgfJ/jR/wgelf8/Oqf+B8n+NH/CB6V/z86p/4Hyf40f8ACB6V/wA/Oqf+B8n+NH/CB6V/z86p/wCB8n+NH/CB6V/z86p/4Hyf40f8IHpX/Pzqn/gfJ/jR/wAIHpX/AD86p/4Hyf40f8IHpX/Pzqn/AIHyf40f8IHpX/Pzqn/gfJ/jR/wgelf8/Oqf+B8n+NYWveDdMg1bw9Gs+pETXzIxN7ISB5Mh4546Vu/8IHpX/Pzqn/gfJ/jSHwLpAIBu9TBPTN/Jz+tL/wAIHpX/AD86p/4Hyf40f8IHpX/Pzqn/AIHyf40f8IHpX/Pzqn/gfJ/jR/wgelf8/Oqf+B8n+NH/AAgelf8APzqn/gfJ/jR/wgelf8/Oqf8AgfJ/jR/wgelf8/Oqf+B8n+NH/CB6V/z86p/4Hyf40f8ACB6V/wA/Oqf+B8n+NH/CB6V/z86p/wCB8n+NH/CB6V/z86p/4Hyf40f8IHpX/Pzqn/gfJ/jR/wAIHpX/AD86p/4Hyf41i+LvBemWvhHVJ0uNSLJbsQGvZCPxBNadl4G0p7C3c3OpgtEp4vpPQe9T/wDCB6V/z86p/wCB8n+NH/CB6V/z86p/4Hyf40f8IHpX/Pzqn/gfJ/jR/wAIHpX/AD86p/4Hyf40f8IHpX/Pzqn/AIHyf40f8IHpX/Pzqn/gfJ/jR/wgelf8/Oqf+B8n+NH/AAgelf8APzqn/gfJ/jR/wgelf8/Oqf8AgfJ/jR/wgelf8/Oqf+B8n+NH/CB6V/z86p/4Hyf40f8ACB6V/wA/Oqf+B8n+NH/CB6V/z86p/wCB8n+NH/CB6V/z86p/4Hyf40f8IHpX/Pzqn/gfJ/jR/wAIHpX/AD86p/4Hyf40f8IHpX/Pzqn/AIHyf40f8IHpX/Pzqn/gfJ/jWF4s8GaZa6TBIk+pMTfWyYa9kIwZlB71u/8ACB6V/wA/Oqf+B8n+NH/CB6V/z86p/wCB8n+NH/CB6V/z86p/4Hyf40f8IHpX/Pzqn/gfJ/jR/wAIHpX/AD86p/4Hyf40f8IHpX/Pzqn/AIHyf40f8IHpX/Pzqn/gfJ/jR/wgelf8/Oqf+B8n+NH/AAgelf8APzqn/gfJ/jR/wgelf8/Oqf8AgfJ/jR/wgelf8/Oqf+B8n+NH/CB6V/z86p/4Hyf40f8ACB6V/wA/Oqf+B8n+NH/CB6V/z86p/wCB8n+NH/CB6V/z86p/4Hyf40f8IHpX/Pzqn/gfJ/jR/wAIHpX/AD86p/4Hyf40f8IHpX/Pzqn/AIHyf40f8IHpX/Pzqn/gfJ/jWPqXhyy0XxN4amtpbx3e9ZSJ7l5Bjy27E9a7+iiuX8L/APIe8Uf9f6/+i1rX1lNXeyA0WW0iutwy10jMm3vwpBzXFeHtZ8d6zqmpWzz6Gkem3gtpiIJMyDAJI+bjg12l/wCIdG0udLfUNVs7WZx8qTTKpP4E1k+LfG+n+FI9MaeSBzfXUcIDTBdsbHBk9wK1m8QaOmmpqT6pZrZP9y4MyhG+hzg1n+INej/4QbVdX0W+hlMNrJJDPCwkUMB+VXvDd3Pf+GdLvLl9889rHJI2MZYqCTWpRRRRRRRRRRRRRRRRRRRRRRRRRRXOeJf+Q34Y/wCwg3/omSujrzTxrrMNv8TvDVu4uZvskE10La3Qs8rkbVAH5nmuw8NeJ7PxPaXE1tDcW8ttMYLi3uU2yROOxFbdFFFFFFFFFFFYHjf/AJEjWP8Ar2atbT/+Qba/9cU/kKs0UUUhOFJ9q888N61428TWzahbz6JBaC6kiETwyF9qNjruxniuk1yTxS9zHb6DDp8cYj3SXN4WYbv7oVefxrnbP4kSL4D1PW9RsAL/AEy5aymghbKSTBgo2n+6SRTh4o8UaFq2jr4lttOaw1WUQK9nuDW0rDKq2fvA9MjFXdR8R63qGv3mk+HEsIlsNoury+LFd7DIRVGMnHU5711GmG/OnQ/2n9n+2Y/eG3zsJ9s81yHj3xlq/hyFjpWlCWOExG4u7jiJQ7Bdqgcs3P4V0+rXGqx6WG0i1hnvXKhRM+1Ez1Y98D0Fc7oXiXXI/GkvhfxDDZPcNafa4Lmy3BSmcEMrEkHNdrRRRXOeNf8AkCW//YQtP/RyV0dFFFFcT4l1bxpp0Go6nYWelrp9ipkEVw7GW4RRliCOF4zjOar65421FvC/hnUtDito5tbuYYQLsFljDqTzgjpiug0qTX7KC7uPEd1pskMab0NnG64AGTncTXIP418XN4dbxfFpun/2EpMn2Rmb7Q0AON+77ucc4xW5q3i28nvbDS/DkVtJd3dqL1p7tiIoIT0Ygckk9BW5obawLBzrkli84c7ZLPcEKepDdD+NcnoXjzUNc+IDabHawJoUsMr2s5z5kxjYKWHONucgfSuy1zVE0TQr7VJImlS0gaYonVtozgVwMfjbxRZ2uh6xqMGkzaZq88cSW9qzedF5n3TknD474FanjbxlrGgSxppmkiS3SaFLi8uciMB2A2oByzc/QV3AOQDS0UVy/ij/AJD3hf8A6/2/9FtXUUUVy/hf/kPeKP8Ar/X/ANFrXUVw/gIg694zwRxqx/8AQFrm/CreGi/itvFRs/7U+3zC4F/t3eT/AAbd38OOmKxPs6L8MPC1zqMP+iQ67GYpLpeUtTKcbiei4x+GK6/xnrWnW1zoOn6ZpejXb3Pmta3F46rawbfvYxwSfSuR0G4lfQPiZE7WAVYtxTTzm3DGM5Kf1969b8H/APIl6L/15Rf+gCtuiiiiiiiiiiiiiiiiiiiiiiiiiiuc8S/8hvwx/wBhBv8A0TJXR15bPqdzo/xP1vW73w9qd3brbx2dlPbQb+QNzAfUkc9K6TwDpF/Y2Oo6nq0Qg1DVrx7uSDOfJU8IhPcgDn6111FFFFFFFFFFFYHjf/kSNY/69mrW0/8A5Btr/wBcU/kKs0UUVzlrp/iWDWGub3XrabTAWJthaBWC9hvz2rz7xXpfhHRfD9zqnhnWZLXVzJ5lqlnes5llLfd8vJyCevFdbrlzZ6rDaaTqPiefQ9TFqs00cEyxFgwweWHOCD0rl/Ctppus/D/X/D+oXUSaNZ3hhg1WP5POwQwkJPBYNjJ71X1K21HVfEPhnRn8VRa5LBfR3LR20CqscUYzvkYE89h65qW20rwrN418ZQ+KzHHN9pFxELicxqYmX76jIBIx1rb8BeI4NH8C6a+tXkqwXV69tp0lwCXkQsfLB79B37VN8X7+zi8DXVtJ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v:shapes=\"_x0000_i1026\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e7.3 Self-report psychological scales\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results for the pre-post intervention questionnaires are shown in Table 7. \u0026nbsp;Pre-post changes on all outcome variables, except for DASS anxiety, were statistically significant, indicating there had been improvements on these domains following psychological treatments. The mean number of weekly psychology sessions was 5.\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"602\" height=\"279\" 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\" alt=\"A table with numbers and letters AI-generated content may be incorrect.\" v:shapes=\"Picture_x0020_1\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e7.4 Acceptability of the intervention protocol for employer/ insurer\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo formal evaluation was conducted, but informally the Australia Post Rehabilitation team did solicit and record feedback from injured workers who participated in the intervention, as well as WRPs, general practitioners, counsellors, psychologists, and managers. A selection of this feedback is presented in Figure 3. Overall, each stakeholder group reported a high degree of satisfaction with the intervention protocol over an extended period. This is consistent with AP deciding to implement the EMCAP protocol as \u0026lsquo;business as usual\u0026rsquo; throughout Australia. This is still the case four years since the formal end of the study. \u0026nbsp;Of interest here is that AP has reported their annual RTW rate has improved from 90% in 2020 to 98% over the year to March 2025. In contrast, the broader Comcare scheme (to which AP and other federal agencies belong) reported declining RTW rates through 2024 to average out at 81%.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis trial has replicated the findings from the previous trial of a similar protocol in a different industry with different workers and different providers [13]. In this study, the primary RTW outcome, measured as time to return to pre-injury hours at work, was significantly less in the intervention group, relative to the control group. In addition, in this study the secondary outcome of total costs and medical costs were also significantly less for the intervention group, relative to the control group. While the other two secondary outcomes, time to return to pre-injury duties and days to RTW, favoured the intervention group, the differences were not statistically significant. \u0026nbsp;In part, this probably reflects the above average RTW outcomes being achieved at the time by the usual care with this employer. Still, as the national RTW statistics indicate, return to pre-injury hours is the more challenging outcome [2]. A novel feature in this study was an evaluation of outcomes achieved by those who had surgery for their injuries. This cohort has often been excluded from intervention studies with musculoskeletal conditions. The results indicated that, not surprisingly, having surgery was associated with both significantly higher costs as well as a significantly longer time to return to pre-injury work hours, regardless of group assignment. However, between group analysis revealed that those in the Intervention group who had surgery returned to pre-injury hours significantly earlier than those in the control group. This is an important finding for both researchers and the occupational injury industry as it suggests better outcomes can be achieved after surgery in workers with high psychosocial risk levels, providing they receive help with those risk factors.\u003c/p\u003e\n\u003cp\u003eMirroring the previous study [13], the expected improvements in the psychological measures over the treatment period were also significant, apart from anxiety. However, inspection of the anxiety scores indicates these were in the normal range initially. The changes in the other psychological scales (Depressive symptoms, Pain severity and interference, catastrophizing and pain self-efficacy) align with the hypothesised basis for targeting these heightened psychological risk factors in the psychological interventions. Consistent with this hypothesis, we also found that following these interventions the mean score on the psychosocial screening questionnaire (\u0026Ouml;MPSQ-SF) had improved significantly and was well-below the baseline score as well as the initial cut-off score used to identify the participants as high risk (48/100). At post-treatment the mean pain self-efficacy (PSEQ) score was significantly higher than at baseline and this is consistent with other studies that found scores over 40/60 on this scale are associated with being at work despite persisting pain [19, 23, 24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe characteristics of the participants should also be considered. On screening, the mean \u0026Ouml;MPSQ-SF scores of both high-risk groups were almost identical (60.3 vs 58.8 for the Intervention and the Controls, respectively). These are slightly below those reported for the WISE study, but that study used a cut-off of 50 (vs 48 in EMCAP) which may account for the difference. Comparing the mean psychological scale scores in EMCAP (Table 7) with published normative Australasian data from over 12,000 adults attending chronic pain services [25], reveals, not surprisingly, the mean EMCAP scores soon after injury were less severe than the chronic pain cohort. Even so, the EMCAP cohort was still worse than 25%-35% of the normative sample on these scales. \u0026nbsp;In comparison with a non-clinical community sample, the EMCAP cohort was substantially more distressed on the three DASS scales [26]. Despite the recency of their injuries, it would seem the high-risk EMCAP participants were in trouble functionally and emotionally around the time of their injury or soon after. This speaks to the construct validity of the \u0026Ouml;MPSQ-SF. At the same time, the improvements on these scales over a mean of 5 weekly sessions of psychological treatment are consistent with the case made by Linton and Nicholas [27] for early intervention in high risk injured workers lest they drift towards higher levels of disability and distress like those seen in patients attending chronic pain services.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings are also consistent with those reported in the systematic review by Cullen et al. [11] that indicated better RTW outcomes are more likely when the psychological treatment is linked closely to workplace engagement. In this study we would point to the preparation of the psychologists/counsellors and WRPs to address identified psychosocial barriers to RTW, as well as preparation of the key workplace stakeholders to promote their support and adherence to the implementation protocol for the intervention sites. \u0026nbsp;This preparation should not be overlooked as it provides the context in which interventions take place and it has long been recommended in the occupational rehabilitation literature [14, 28].\u003c/p\u003e\n\u003cp\u003eThe findings also align with the recommendations of Jellema et al. [29] and Linton et al. [10] that early psychosocial intervention for recently injured people should be reserved for those with identified psychological risk factors. Similarly, the actual interventions should be matched to the individual worker\u0026rsquo;s assessed psychosocial needs, rather than according to their level of risk category as undertaken in some previous and (mostly) unsuccessful trials [8]. This is one of the key points to take away from this study. The improvements on the psychological scores and the risk scale (\u0026Ouml;MPSQ-SF) following treatment by the psychologists and counsellors that targeted these psychosocial factors confirms they are modifiable and that changes in them are associated with better functional (RTW) and mental health outcomes. Importantly, the matched care format allows the intervention team to address the actual concerns of the injured workers rather than just assuming they related to their injuries or pain. In fact, in many cases their concerns related more to work, physical functioning and stigma than their injuries/pain.\u003c/p\u003e\n\u003cp\u003eAn additional, and serendipitous outcome reported by the AP Rehabilitation team was a marked difference between the two groups in disputes concerning decisions about claims or rehabilitation processes. Relative to those in the Control group the Rehabilitation team reported those in the Intervention group initiated substantially fewer complaints or costly and acrimonious litigation. This observation was not formally assessed but is worthy of future research. It may reflect a benefit from the collaborative, biopsychosocial approach promoted by the EMCAP protocol which specifically includes the injured worker in both the identification of RTW barriers and the selection of interventions to address them.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother question raised by this study concerns the providers of the\u0026nbsp;psychosocial interventions. \u0026nbsp;Specifically, do these interventions require mental health professionals to be effective? Dealing with people experiencing significant levels of psychological distress may well require advanced professional skills that are out of the scope of practice for most non-mental health professionals. But there is evidence that treatment providers from a range of disciplines, such as physiotherapists, occupational therapists, or nurses, who have been trained to competence in delivering psychologically-based interventions for pain can achieve significant improvements in psychosocial variables. \u0026nbsp;For example, \u0026Aring;senl\u0026ouml;f\u0026nbsp;et al. [30] demonstrated benefits for early matching of patients with low back pain (in primary care) to treatment by physiotherapists based on individual behavioural assessments versus guideline-informed exercise-based treatment. \u0026nbsp;More recently, Kent et al [24] demonstrated that after extended training in the delivery of a cognitive and functional (behavioural) protocol, physiotherapists were also able to achieve impressive and lasting improvements in patients presenting to primary care with low back pain. In the present trial, the psychologists and counsellors were also provided training in the protocol for delivering the psychosocial intervention to injured workers and in working with the rehabilitation staff from the employer. That the psychologists/counsellors were able to deliver the protocol within the framework of 5-6 sessions, consistent with the previous trial [13], coupled with the evidence that the psychological distress levels in the early post-injury phase were not severe, would suggest that most cases could be managed by other health professionals following relevant training in psychologically-informed treatment methods, as demonstrated by\u0026nbsp;\u0026Aring;senl\u0026ouml;f\u0026nbsp;et al. [30] and Kent et al [24].\u003c/p\u003e\n\u003cp\u003eWhile the refusal rate (to see a psychologist/counsellor) amongst the identified high-risk workers might be seen as somewhat high, it was better than found in our previous study (43.1% vs 50%, respectively) [13] and similar to previous findings [31, 32]. Clearly, more work is needed on ways of overcoming this resistance to seeing a psychologist/counsellor in the early post-injury period. It is possible that more physical treatment providers (like physiotherapists) would have been more acceptable to some workers. However, as many physiotherapists lack confidence in their ability to implement a psychological form of treatment, specific training is recommended [33]. \u0026nbsp;Another possible explanation for the refusal rate in the Intervention cohort could be related to the communication skills of the WRPs tasked with encouraging the injured workers to see the psychologists/counsellors. While training in the communication skills was provided as part of the protocol, it is possible that more was needed, and this should also be considered in future implementations of the protocol. The importance of training for primary healthcare professionals in screening for psychosocial risk factors, follow-up individual assessments, and strategies for engaging injured workers in an integrated and individualized biopsychosocial intervention has been recognised for some time but is still to be widely adopted [33, 34, 35].\u003c/p\u003e\n\u003cp\u003eThis study provides supporting evidence for Reneman et al\u0026rsquo;s [36] recent encouragement for researchers in occupational rehabilitation to utilize a biopsychosocial model of pain. The types of injuries experienced, their primary site, and mechanisms are typically varied, as demonstrated here. Accordingly, limiting the focus of research on injured workers to those with a specific pain site, like low back pain, seems artificial and at odds with the reality that psychosocial risk factors for delayed recovery and RTW are independent of pain site. In the present study, biological contributors to the presenting injuries were addressed by the workers\u0026rsquo; general practitioner, surgeon, and physiotherapist. Identified psychological contributors were specifically addressed by the psychologists or counsellors, and the social or environmental contributors were addressed by the WRP, claims manager, workplace supervisor, general practitioner, and psychologist/counsellor as required.\u003c/p\u003e\n\u003cp\u003eIn occupational injury research, perhaps the ultimate outcome measure is whether the protocol is adopted by the employer and implemented as usual practice [39]. This was one of the outcomes from the previous trial [13] and replicated after the present trial. The employer (AP) reports the protocol was implemented nation-wide soon after the completion of the study as the benefits over usual care were evident by then. Whether the improved RTW reported by AP in recent years is related to the use of the matched care protocol cannot be determined, but similar gains have not been reported in the federal government\u0026rsquo;s workforce generally, where matched care has not been specifically used. The feedback provided by key stakeholders when asked for their views on EMCAP is particularly revealing and highlights the extent to which the protocol encompassed far more than the usual treatment providers in many intervention studies.\u003c/p\u003e\n\u003cp\u003eThe employer (AP) reports that 5 years post commencement of the study outcomes of their three key metrics of success: improvement in mental health outcomes, time to return to pre-injury hours and incapacity cost reduction (wage replacement) have remained consistent with those reported for the study period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStrengths and Limitations of the study.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStrengths include the use of a prospective study design in which the protocolised intervention, which included usual medical care (including surgery), was compared with a usual care control group of similarly injured workers from the same industry, albeit in different states. Other strengths include the long-term follow-up of 1-year post-injury, the primary outcome of days to return to pre-injury work hours based on workplace records, rather than self-report measures by the injured workers provides a more objective outcome and one that is of relevance to employers and funders. By not excluding those offered surgery for their injuries and those with pain at any site and cause, the present study can be considered as representative of the challenges facing all injured workers, their employers, insurers, and families. These features enhance the ecological validity of the study and the generalisability of the findings. A further strength is that the control was an active treatment group and one that had already established better RTW outcomes than the Australian national average for RTW after injury.\u003c/p\u003e\n\u003cp\u003eLimitations include the lack of randomised assignment to intervention or control groups. This does raise the risk of bias, but given the pragmatic decision-making required to implement a trial supported by the employer and the practical problems of using a randomised design, the fact that the results replicate the previous study with the same protocol provides a degree of confidence in the findings. The failure to reach the estimated sample size for the intervention group is also a limitation and this highlights the challenge of retaining potentially suitable participants after initial screening. Another limitation was the lack of self-report psychological measures in the control group which meant we could not evaluate the impact of the \u0026lsquo;usual care\u0026rsquo; on these domains. Despite its attractions, the cost would have been the potential unblinding of the WRPs, treatment providers, and workplace supervisors in relation to the high and low risk cases, raising the risk of changes in the nature of usual care.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e This study was supported by a grant from Australia Post. We would like to specifically acknowledge the contributions to the study by WRPs at Australia Post in each state, as well as psychologists and counsellors at CONVERGE. We also thank the injured workers who voluntarily participated, and the helpful feedback they provided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e Written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eAuthors Nicholas and Shaw are current Editorial Board members for JOOR.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAuthors Ianssen, Morgan and Rae are employees of Australia Post.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eApart from that, the authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eMN is the principal investigator of the research project and had the main responsibility for the design and conduct of the study. MI, LM and RN, represented the employer and made important contributions to the design and conduct of the study. MM was responsible for gaining the ethics approval and registering the trial, as well as data preparation and data collection, along with LM and RN, under the supervision of MI. TO contributed to the training and advice for the WRPs and psychologists/counsellors involved in the intervention arm. DC conducted the statistical analyses. MN had the main responsibility for the writing of the first draft of the manuscript with support from MI, LM, MM and DC. CM, WS, SL made contributions to the manuscript\u0026rsquo;s design, conception and interpretation of data. All authors have revised the manuscript critically, discussed the results, commented on the manuscript and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The data are not publicly available due to containing information that could compromise the privacy of the study participants. Reasonable inquiries about access may be sent to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNational Return to Work Survey, Ref 5059, Safe Work Australia 2021. https://www.safeworkaustralia.gov.au/sites/default/files/2022-02/2021 (accessed 14/12/25)\u003c/li\u003e\n\u003cli\u003eNational Return to Work Survey. Ref. 5059, Safe Work Australia 2025. https://data.safeworkaustralia.gov.au/sites/default/files/2025-11/NRTW_Survey_Analysis_Report_Nov2025.pdf (accessed 14/12/25)\u003c/li\u003e\n\u003cli\u003eSchofield DJ, Shrestha RN, Percival R, Callander EJ, Kelly SJ, Passey ME. Early retirement and the financial assets of individuals with back problems.\u003cem\u003e \u003c/em\u003eEurop Spine J 2011; 20(5): p. 731-736.\u003c/li\u003e\n\u003cli\u003eCampbell P, Wynne-Jones G, Muller S, Dunn KM. The influence of employment social support for risk and prognosis in nonspecific back pain: a systematic review and critical synthesis. Int Arch Occ Environ Health 2013; 86:119\u0026ndash;137.\u003c/li\u003e\n\u003cli\u003eHeikkala E, Oura P, Ruokolainen O, Ala-Mursula L, Linton SJ, Karppinen J. Identification and characterisation of trajectories of sickness absence due to musculoskeletal pain: A 1‑Year population‑based study. Euro J Pub Hlth 2023, 33: 442\u0026ndash;447.\u003c/li\u003e\n\u003cli\u003eNicholas MK, Costa DSJ, Linton SJ, et al. Predicting return to work in a heterogeneous sample of recently injured workers using the brief \u0026Ouml;MPSQ-SF. J Occ Rehab 2018; https://doi.org/10.1007/s10926-018-9784-8\u003c/li\u003e\n\u003cli\u003eRysstad T, Grotle M, Aasdahl L, Dunn KM, Tveter AT. Identification and Characterisation of Trajectories of Sickness Absence Due to Musculoskeletal Pain: A 1‑Year Population‑based Study. J Occ Rehab 2023; 33:277\u0026ndash;287.\u003c/li\u003e\n\u003cli\u003eCroft P, Hill J, Foster NE, Dunn KM, van der Windt DA. Stratified health care for low back pain using the STarT Back approach: Holy Grail or doomed to fail? PAIN 2024; 165(12):2679-2692\u003c/li\u003e\n\u003cli\u003eHill JC, Dunn KM, Lewis M, Mullis R, Main CJ, Foster NE, Hay EM. A primary care back pain screening tool: identifying patient subgroups for initial treatment. Arthritis Rheum 2008; 59:632\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eLinton SJ, Nicholas MK, Shaw W. Why wait to address high-risk cases of acute low back pain? A comparison of stepped, stratified, and matched care. PAIN 2018; 159: 2437-2441. doi: 10.1097/j.pain.0000000000001308\u003c/li\u003e\n\u003cli\u003eCullen K L, Irvin E, Collie A, Clay F, Gensby U, Jennings PA, Hogg‑Johnson S, Kristman V, Laberge M, McKenzie D, Newnam S, Palagyi A, Ruseckaite R, Sheppard DM, Shourie S, Steenstra I, Van Eerd D, Amick III BC. Effectiveness of Workplace Interventions in Return-to-Work for Musculoskeletal, Pain-Related and Mental Health Conditions: An Update of the Evidence and Messages for Practitioners. J Occ Rehab 2018; 28: 1-15.\u003c/li\u003e\n\u003cli\u003eDelitto A, Patterson CG, Stevans JM, Freburger JK, Khoja SS, Schneider MJ, Greco CM, Freel JA, Sowa GA, Wasan AD, Brennan GP, Hunter SJ, Minick KI, Wegener ST, Ephraim PL, Beneciuk JM, George SZ, Saper RB. Stratified care to prevent chronic low back pain in high-risk patients: The TARGET trial. A multi-site pragmatic cluster randomized trial. EClinical Medicine 2021; 34, 100795. https//www.journals.elsevier.com/eclinicalmedicine\u003c/li\u003e\n\u003cli\u003eNicholas MK, Costa DSJ, Linton CJ, Main CJ, Shaw WS, Pearce G, Gleeson M, Pinto RZ, Blyth FM, McAuley JH, Smeets RJEM, McGarity A. Implementation of early intervention protocol in Australia for \u0026ldquo;high risk\u0026rdquo; injured workers is associated with fewer lost work days over 2 years than usual (stepped) care. J Occ Rehab 2020; 30:93\u0026ndash;104.\u003c/li\u003e\n\u003cli\u003eWilliams-Whitt K, Bultmann U, Amick III B, Munir F, Tveito TH, Anema JR, and the Hopkinton Conference Working Group on Workplace Disability Prevention. Workplace Interventions to Prevent Disability from Both the Scientific and Practice Perspectives: A Comparison of Scientific Literature, Grey Literature and Stakeholder Observations. J Occ Rehab 2016; 26:417\u0026ndash;433.\u003c/li\u003e\n\u003cli\u003eLinton SL, Nicholas MK, MacDonald S. Development of a Short Form of the \u0026Ouml;rebro Musculoskeletal Pain Screening Questionnaire. Spine 2012; 36: 1891-95.\u003c/li\u003e\n\u003cli\u003eNicholas MK, George SZ. Psychologically Informed Interventions for Low Back Pain: An Update for Physical Therapists. Phys Ther\u003cem\u003e \u003c/em\u003e2011; 91:765\u0026ndash;776.\u003c/li\u003e\n\u003cli\u003eCleeland CS, Ryan KM. Pain assessment: global use of the Brief Pain Inventory. An Acad Med 1994; 23(2):129-38.\u003c/li\u003e\n\u003cli\u003eLovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther 1995; 33:335\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eNicholas MK. The pain self-efficacy questionnaire: taking pain into account. Eur J Pain 2007; 11:153-63.\u003c/li\u003e\n\u003cli\u003eSullivan MJL, Bishop SR, Pivik J. The pain catastrophizing scale: development and validation. Psychol Assess 1995; 7:524\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eTherneau T (2023). A Package for Survival Analysis in R. R package version 3.5-5, https://CRAN.R-project.org/package=survival\u003c/li\u003e\n\u003cli\u003eDe Vries HJ, Reneman MF, Groothoff JW, Geertzen JHB, Brouwer S. Self-reported Work Ability and Work Performance in Workers with Chronic Nonspecific Musculoskeletal Pain. J Occ Rehab 2013; 23:1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eKent P. Cognitive functional therapy with or without movement sensor biofeedback versus usual care for chronic, disabling low back pain (RESTORE): a randomised, controlled, three-arm, parallel group, phase 3, clinical trial. Lancet 2023; 401: 1866\u0026ndash;77\u003c/li\u003e\n\u003cli\u003eNicholas MK, Costa DSJ, Blanchard M, Tardif H, Asghari A, Blyth FM. Normative data for common pain measures in chronic pain clinic populations: closing a gap for clinicians and researchers. PAIN 2019; 160: 1156\u0026ndash;1165.\u003c/li\u003e\n\u003cli\u003eTaylor R, Lovibond PF, Nicholas MK, Cayley C, Wilson PH. The utility of somatic items in the assessment of depression in chronic pain patients: A comparison of the Zung Self-rating Depression Scale (SDS) and the Depression Anxiety Stress Scales (DASS) in chronic pain, clinical and community samples. Clin J Pain\u003cem\u003e \u003c/em\u003e2005;\u003cem\u003e \u003c/em\u003e21, 91\u0026ndash;100.\u003c/li\u003e\n\u003cli\u003eLinton SJ, Nicholas MK. Understanding the individual\u0026rsquo;s transition from acute to chronic disabling pain: Opportunities for improved care. Curr Opin in Psychology 2025, 62:101989\u003c/li\u003e\n\u003cli\u003eMain CJ, Shaw WS, Nicholas MK, Linton SJ. System-level efforts to address pain-related workplace challenges. PAIN 2022; 163: 1425\u0026ndash;1431.\u003c/li\u003e\n\u003cli\u003eJellema P, van der Windt DA, van der Horst HE, et al. Why is a treatment aimed at psychosocial factors not effective in patients with (sub)acute low back pain? PAIN\u003cem\u003e \u003c/em\u003e2005; 118: 350\u0026ndash;359.\u003c/li\u003e\n\u003cli\u003e\u0026Aring;senl\u0026ouml;f P, Denison E, Lindberg P. Individually Tailored Treatment Targeting Activity, Motor Behavior, and Cognition Reduces Pain\u0026ndash;Related Disability: A Randomized Controlled Trial in Patients With Musculoskeletal Pain. J Pain 2005; 6(9):588-603.\u003c/li\u003e\n\u003cli\u003eMyhre K, Marchand GH, Leivseth G, Keller A, Bautz-Holter E, Sandvik L, et al. The effect of work-focused rehabilitation among patients with neck and back pain: a randomized controlled trial. Spine 2014; 39:1999\u0026ndash;2006.\u003c/li\u003e\n\u003cli\u003evan Oostrom SH, van Oostrom W, van Mechelen BT, de Vet HCW, Knol DL, Anema JR. A workplace intervention for sick-listed employees with distress: results of a randomised controlled trial. Occ Environ Med. 2010. https ://doi.org/10.1136/oem.2009.05084 9.\u003c/li\u003e\n\u003cli\u003eNg W, Slater H, Starcevich C, Wright A, Mitchell T, Beales D. Barriers and enablers influencing healthcare professionals\u0026rsquo; adoption of a biopsychosocial approach to musculoskeletal pain: a systematic review and qualitative evidence synthesis. PAIN 2021; 162: 2154\u0026ndash;2185.\u003c/li\u003e\n\u003cli\u003eOvermeer T, Boersma K, Denison E, Linton SJ. Does teaching physical therapists to deliver a biopsychosocial treatment program result in better patient outcomes? A randomized controlled trial. Phys Ther. 2011;91:804\u0026ndash;819.\u003c/li\u003e\n\u003cli\u003eKeefe FJ, Main CJ, George SZ. Advancing psychologically informed practice for patients with persistent musculoskeletal pain: Promise, pitfalls, and solutions. Phys Ther 2018; 98: 398-407.\u003c/li\u003e\n\u003cli\u003eReneman MF, O\u0026rsquo;Keeffe M, Belton J, Falcon M, Main CJ, Moore P, Rohde I, Shaw WS, Smith BH, Aasdahl L, Hoegh M. Advancing Work‑Related Musculoskeletal Pain Science in the Journal of Occupational Rehabilitation: Time to Fully Adopt Biopsychosocial Approaches? J Occ Rehab 2025; 35:139\u0026ndash;142\u003c/li\u003e\n\u003cli\u003ePransky G, Fassie J-B, Besen E, Blanck P, Ekberg K, Feuerstein M, Munir F, the Hopkinton Conference Working Group on Workplace Disability Prevention. Sustaining work participation across the life course. J Occ Rehab 2016; 26: 465\u0026ndash;479.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-occupational-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joor","sideBox":"Learn more about [Journal of Occupational Rehabilitation](https://www.springer.com/journal/10926)","snPcode":"10926","submissionUrl":"https://submission.nature.com/new-submission/10926/3","title":"Journal of Occupational Rehabilitation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Psychosocial screening, Workers’ compensation, Work injury, Early intervention","lastPublishedDoi":"10.21203/rs.3.rs-9422704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9422704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003ePsychosocial factors have been identified as risks for delayed recovery after work injuries but results from interventions for high-risk cases have been mixed. An earlier Australian study that matched care to assessed psychosocial risk factors with injured workers showed better return to work outcomes over 2 years than the usual care control, but as the study was not randomized it needed replication in another population. The present study, using the same protocol as the earlier study, but with injured workers from a different industry and different providers, attempted to replicate the earlier findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA 2-group prospective design was used, with participants being allocated to Intervention or Control groups based on their home state of Australia. High-risk cases were identified within 5-10 days of injury. Those in the Intervention states were offered a coordinated, multidisciplinary intervention that targeted both identified psychological and social/workplace risk factors. Participants from both groups received usual care as required, including surgery, from their healthcare providers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eAfter 1-year, time to return to pre-injury work hours, total costs, and medical costs were significantly less in the Intervention group, relative to the Controls. Having surgery was associated with more lost time from work, but surgical cases in the Intervention group had significantly less lost time than the surgical cases in the Controls.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eThese results replicate those of the earlier study and indicate that long-term disability from work injuries can be prevented by matching psychosocial interventions to identified psychosocial risk factors. Having surgery was associated with higher costs and delayed recovery, but did not alter the psychosocial findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration\u003c/strong\u003e. 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