Work organization and musculoskeletal injuries among a cohort of health care workers
Bibliographic record
Abstract
OBJECTIVES: This study investigated the relationship between work-organization factors (job control, job demands, and workload measures) and the risk of lower-body musculoskeletal injury among health care workers. METHODS: A four-year, retrospective cohort study of 3769 health care workers was carried out in one acute care hospital in the Canadian province of British Columbia. A job-exposure matrix was constructed for the work-organization factors from survey and administrative data and assigned to workers on the basis of their occupation and department of employment. Musculoskeletal injuries resulting in workers' compensation claims were ascertained from the injury database of the hospital's Occupational Health and Safety Department. RESULTS: In the final Poisson models adjusted for demographic and biomechanical factors, an increased risk for compensated musculoskeletal injuries of the lower back and lower limb was related to low job control [relative risk (RR) 1.64, 95% confidence interval (95% CI) 1.08-2.49] and workload defined by working during periods of high absenteeism within a department (RR 2.10, 95% CI 1.61-2.98). The risk also increased with more biomechanical demands in an occupation and with a recent previous injury. CONCLUSIONS: The results indicate that work-organization characteristics (job control and workload) were associated with an increased risk of musculoskeletal injuries resulting in a compensation claim. These associations remained after the effect of demographic and biomechanical factors was taken into consideration. The association with workload measured by departmental levels of absenteeism should be explored further in future studies as reverse causality (musculoskeletal symptoms resulting in absenteeism) could not be fully ruled out in the current study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".