A review of best work-absence management and return-to-work practices for workers with musculoskeletal or common mental disorders
Bibliographic record
Abstract
BACKGROUND: Workplace absenteeism is still a curse for developed countries, and more systematic practices need to be adopted to address this issue. OBJECTIVE: To review the literature on best practices for managing work absences related to musculoskeletal or common mental disorders. METHODS: A review was conducted by performing a search in bibliographic databases and on work-disability research institute websites. Recommendations regarding work-absence management and return-to-work practices were extracted from all the retained documents and organized within a chronological framework. RESULTS: In total, 17 documents were analyzed, leading to identification of common work-absence management and return-to-work practices, the importance of a worker support approach, and recommended roles and responsibilities for stakeholders. These practices were then integrated into a six-step process: (1) time off and recovery period; (2) initial contact with the worker; (3) evaluation of the worker and his job tasks; (4) development of a return-to-work plan with accommodations; (5) work resumption, and (6) follow-up of the return-to-work process. CONCLUSIONS: Based on this review, we constructed a comprehensive work-absence management and return-to-work process designed to assist organizations. Our results indicate that such a process must be included within a broader policy of health promotion and job retention. Adaptations will be required for implementation in the workplace.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".