Best practices in the rehabilitation of acute musculoskeletal disorders in workers with injuries: An integrative review and analysis of evolving trends
Bibliographic record
Abstract
OBJECTIVE: Strategies for the restoration of function and job performance in workers with musculoskeletal disorders have changed considerably since industrial rehabilitation became a distinct specialization in the 1980s. A rich body of research concerning approaches to medical and rehabilitative management of these disorders exists, resulting in a large number of hybrid approaches worldwide. METHODS: This integrative review examined the evidence base for best practices in the rehabilitation of acute musculoskeletal workplace injuries, and also mapped the evolution of approaches over the past thirty years. Contextual factors that may have contributed to change were assessed through analysis of changing practice trends and review of descriptive literature over time. RESULTS: A clear movement away from simplistic, unidimensional approaches towards comprehensive workplace interventions is evident. Economic concerns and growing government and insurance regulation of workplace safety and injury management were the likely drivers of change. While the contributions of various elements of disability management in the workplace were examined, many features of onsite interventions remain to be examined. CONCLUSIONS: A strong body of research has produced notable advances in management of acute musculoskeletal workplace injury. Research concerning the delivery of workplace-based interventions, the role of workplace environment factors and a range of worker outcomes will further advance knowledge in this field.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".