Stretching to reduce work-related musculoskeletal disorders: A systematic review
Bibliographic record
Abstract
OBJECTIVE: This article reviewed the literature to clarify the physiological effects and benefits of, and misconceptions about, stretches used to reduce musculoskeletal disorders. METHODS: Nine databases were reviewed to identify studies exploring the effectiveness of stretching to prevent work-related musculoskeletal disorders. Included studies were reviewed and their methodological quality was assessed using the PEDro scale. RESULTS: The physiological effects of stretches may contribute to reducing discomfort and pain. However, if other measures are not in place to remediate their causes, stretches may suppress awareness of risks, resulting in more debilitating injuries. If inadequately performed, stretches may also cause or aggravate injuries. Careful analysis and stretching program design are required before implementing stretches. Seven studies evaluating the effectiveness of stretching to prevent musculoskeletal disorders in different occupations were identified and reviewed. CONCLUSION: The studies provided mixed findings, but demonstrated some beneficial effect of stretching in preventing work-related musculoskeletal disorders. However, due to the relatively low methodological quality of the studies available in the literature, future studies are necessary for a definite response. Future studies should minimize threats to internal and external validity, have control groups, use appropriate follow-up periods, and present a more detailed description of the interventions and worker population.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".