Topics and trends in research on non-clinical interventions aimed at preventing prolonged work disability in workers compensated for work-related musculoskeletal disorders (WRMSDs): a systematic, comprehensive literature review
Bibliographic record
Abstract
PURPOSE: This study sought to provide an overview of the main topics and trends in contemporary research on successful non-clinical interventions for preventing prolonged work disability in workers compensated for work-related musculoskeletal disorders (WRMSDs). METHODS: A systematic electronic search (English and French) was performed in ten scientific databases using keywords and descriptors. After screening the identified titles and abstracts using specific sets of criteria, categorical and thematic analyses were performed on the retained articles. RESULTS: Five main topics appear to dominate the research: (1) risk factors and determinants; (2) effectiveness of interventions (programmes, specific components, strategies and policies); (3) viewpoints, experiences and perceptions of specific actors involved in the intervention process; (4) compensation issues; and (5) measurement issues. A currently widespread trend is early screening to identify risks factors for appropriate intervention and multidisciplinary, multimodal approaches. Morover, workplace-related psychosocial and ergonomic factors are considered vital to the success and sustainability of return-to-work (RTW) interventions. Finally, involving workplace actors, and more specifically, affected workers, in the RTW process appears to be a powerful force in improving the chances of moving workers away from disabled status. CONCLUSIONS: The findings of this literature review provide with information about the main topics and trends in research on rehabilitation interventions, revealing some successful modalities of intervention aimed at preventing prolonged work disability. IMPLICATIONS FOR REHABILITATION: Successful intervention for preventing prolonged work disability in workers compensated for WRMSDs address workplace issues: physical and psychosocial demands at work, ability of the workers to fill these demands, work organization and support of the worker, and worker' beliefs and attitudes related to work. Successful intervention promotes collaboration, coordination between all actors and stakeholders involved in the process of rehabilitation. Strategies able to mobilize the employees, employers, insurers and health care providers are still needed to be implemented.
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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 | 0.023 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".