Employer Best Practice Guidelines for the Return to Work of Workers on Mental Disorder–Related Disability Leave
Bibliographic record
Abstract
OBJECTIVE: There has been an increasing number of employer best practice guidelines (BPGs) for the return to work (RTW) from mental disorder-related disability leave. This systematic review addresses 2 questions: 1) What is the quality of the development and recommendations of these BPGs? and 2) What are the areas of agreement and discrepancy among the identified guidelines related to the RTW from mental illness-related disability leave? METHOD: A systematic literature search was performed using publically available grey literature and best practice portals. It focused on the RTW of workers with medically certified disability leave related to mental disorders. The Appraisal of Guidelines for Research and Evaluation II (AGREE II) was used to assess the quality of the development and recommendations of these BPGs. RESULTS: A total of 58 unique documents were identified for screening. After screening, 5 BPGs were appraised using AGREE II; 3 BPGs were included in the final set. There were no discrepancies among the 3, although they were from different countries. They all agreed there should be: 1) well-described organizational policies and procedures for the roles and responsibilities of all stakeholders, 2) a disability leave plan, and 3) work accommodations. In addition, one guideline suggested supervisor training and mental health literacy training for all staff. CONCLUSION: Although there were no discrepancies among the 3 BPGs, they emphasized different aspects of RTW and could be considered to be complementary. Together, they provide important guidance for those seeking to understand employer best practices for mental illness-related disability.
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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 | 0.107 | 0.313 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| 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".