Work-related injuries of rehabilitation therapists and measures for prevention
Bibliographic record
Abstract
Background: Rehabilitation therapists often perform physically demanding treatments that may result in work-related injuries, yet related studies are scarce. This study aimed to investigate the work-related injuries of rehabilitation therapists and provide feasible preventive measures. Methods: A cross-sectional study was carried out in 34 regions across China using an online questionnaire. The survey gathered responses primarily from 1,198 rehabilitative therapists working in 120 health institutes. Following data collection, descriptive analysis, chi-square tests, logistic regression, and receiver operating characteristic (ROC) curves were employed to analyze the data. Results: = 1,041). The top three musculoskeletal issues reported were low back pain (12%), neck pain (10%), and shoulder pain (9%). Logistic regression and ROC curve analysis identified that working as a physiotherapist and years of work experience (OR [95% CI]: 1.03 [0.99-1.07]) were significant contributors to the incidence of work-related injuries. Specifically, neuro-physiotherapists (OR [95% CI]: 3.04 [1.56-5.92]), musculoskeletal physiotherapists (OR [95% CI]: 2.46 [1.16-5.18]), and intensive care physiotherapists (OR [95% CI]: 4.70 [1.24-17.88]) were at higher risk. Furthermore, five factors were proven to be associated with injury prevention as reported by therapists: patient engagement (OR [95% CI]: 0.38 [0.23-0.62]), improving techniques (OR [95% CI]: 0.59 [0.39-0.90]), maintaining exercise habits (OR [95% CI]: 0.59 [0.40-0.86]), utilizing instruments (OR [95% CI]: 0.80 [0.53-1.19]), and strengthening education (OR [95% CI]: 0.43 [0.21-0.90]). Conclusion: The present study investigated the factors contributing to work-related injuries among rehabilitation therapists, with a focus on identifying both risk and preventive measures. These findings offer new perspectives on decreasing injury risk.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".