Feedback for the prevention and rehabilitation of work-related musculoskeletal disorders: A systematic review
Bibliographic record
Abstract
BACKGROUND: Work-related musculoskeletal disorders (WRMSDs) remain a challenge despite research aimed at improving their prevention and treatment. Extrinsic feedback has been suggested for the prevention and rehabilitation of WRMSDs to improve sensorimotor control, and ultimately to reduce pain and disability. However, there are few systematic reviews on the effectiveness of extrinsic feedback for WRMSDs. OBJECTIVE: To perform a systematic review investigating the effect of extrinsic feedback for the prevention and rehabilitation of WRMSDs. METHODS: Five databases (CINAHL, Embase, Ergonomics Abstract, PsycInfo, PubMed) were searched. Studies of various designs assessing the effects of extrinsic feedback during work tasks on three outcomes (function, symptoms, sensorimotor control) in the context of prevention and rehabilitation of WRMSDs were included. RESULTS: Forty-nine studies were included, for a total sample of 3387 participants (including 925 injured) who performed work-related tasks in the workplace (27 studies) or in controlled environments (22 studies). The use of extrinsic feedback was shown to be effective in controlled environments for short-term prevention of functional limitations and sensorimotor alterations (very limited to moderate evidence) and for improving, in injured participants, function, symptoms and sensorimotor control (moderate evidence). In the workplace, it was shown to be effective for short-term prevention of functional limitations (limited evidence). There was conflicting evidence regarding its effect for WRMSD rehabilitation in the workplace. CONCLUSION: Extrinsic feedback is an interesting complementary tool for the prevention and rehabilitation of WRMSDs in controlled environments. More evidence is needed regarding its effect for the prevention and rehabilitation of WRMSDs in the workplace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".