Return to Work for Mental Ill-Health: A Scoping Review Exploring the Impact and Role of Return-to-Work Coordinators
Bibliographic record
Abstract
Purpose This scoping review was completed to explore the role and impact of having a return-to-work (RTW) coordinator when dealing with individuals with common mental ill-health conditions. Methods Peer reviewed articles published in English between 2000 and 2018 were considered. Our research team reviewed all articles to determine if an analytic focus on RTW coordinator and mental ill-health was present; consensus on inclusion was reached for all articles. Data were extracted for all relevant articles and synthesized for outcomes of interest. Results Our search of six databases yielded 1798 unique articles; 5 articles were found to be relevant. The searched yielded only quantitative studies. Of those, we found that studies grouped mental ill-health conditions together, did not consider quality of life, and used different titles to describe RTW coordinators. Included articles described roles of RTW coordinators but did not include information on their strategies and actions. Included articles suggest that RTW interventions for mental ill-health that utilize a RTW coordinator may result in delayed time to RTW. Conclusions Our limited findings suggest that interventions for mental ill-health that employ RTW coordinators may be more time consuming than conventional approaches and may not increase RTW rate or worker's self-efficacy for RTW. Research on this topic with long-term outcomes and varied research designs (including qualitative) is needed, as well as studies that clearly define RTW coordinator roles and strategies, delineate results by mental health condition, and address the impact of RTW coordinators on workers' quality of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".