Functional recovery following musculoskeletal injury in hospital workers
Bibliographic record
Abstract
BACKGROUND: Hospital workers are at high risk of work-related musculoskeletal disorders (WRMSDs), but outcomes following such injuries have not been well studied longitudinally. AIMS: To ascertain functional recovery in hospital workers following incident WRMSDs and identify predictors of functional status. METHODS: Cases (incident WRMSD) and matched referents from two hospitals were studied at baseline and at 2 year follow-up for health status [SF-12 physical component summary (PCS)], lost workdays, self-rated work effectiveness and work status change (job change or work cessation). Predictors included WRMSD and baseline demographics, socio-economic status (SES), job-related strain and effort-reward imbalance. Logistic regression analysis tested longitudinal predictors of adverse functional status. RESULTS: The WRMSD-associated risk of poor (lowest quartile) PCS was attenuated from a baseline odds ratio (OR) of 5.2 [95% confidence interval (CI) 3.5-7.5] to a follow-up OR of 1.5 (95% CI 1.0-2.3) and was reduced further in multivariate modelling (OR = 1.4; 95% CI 0.9-2.2). At follow-up, WRMSD status did not predict significantly increased likelihood of lost workdays, decreased effectiveness or work status change. In multivariate modelling, lowest quintile SES predicted poor PCS (OR = 2.0; 95% CI 1.0-4.0) and work status change (OR = 2.5; 95% CI 1.1-5.8). High combined baseline job strain/effort-reward imbalance predicted poor PCS (OR = 1.7; 95% CI 1.1-2.7) and reduced work effectiveness (OR = 2.6; 95% CI 1.6-4.2) at follow-up. CONCLUSIONS: Baseline functional deficits associated with incident WRMSDs were largely resolved by 2 year follow-up. Nonetheless, lower SES and higher combined job strain/effort-reward imbalance predicted adverse outcomes, controlling for WRMSDs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".