The association of socioeconomic status and psychosocial and physical workplace factors with musculoskeletal injury in hospital workers
Bibliographic record
Abstract
BACKGROUND: The combined effect of socioeconomic, organizational, psychosocial, and physical factors on work-related musculoskeletal disorders (WRMSDs) were studied in a heterogeneous, socioeconomically diverse sample (cases and their matched referents) of hospital workers. METHODS: Cases were defined by a new acute or cumulative work-related musculoskeletal injury; referents were matched by job group, shift length, or at random. Information was obtained through telephone interviews and on-site ergonomics observation. Questionnaire items included sociodemographic variables, lost work time, work effectiveness, health status, pain/disability, and psychosocial working conditions using Effort Reward Imbalance (ERI) and Demand-Control (DC) models. Two multivariate models were tested: Model 1 included occupation as a predictor; Model 2 included education-income as a predictor. RESULTS: Cases reported greater pain, disability, lost time, and decreased work effectiveness than the referents. Model 1 was statistically significant for neck/upper extremity injury (Chi-square = 19.3, P = 0.01), back/lower extremity injury (Chi-square = 14.0, P = 0.05), and all injuries combined (Chi-square = 25.4, P = 0.001). "Other Clinical" occupations (34% mental health workers) had the highest risk of injury (OR 4.5: 95%CI, 1.7-12.1) for all injuries. The ERI ratio was a significant predictor for neck and upper extremity (OR 1.5: 95%CI, 1.1-1.9) and all injuries (OR 1.3; 95%CI, 1.04-1.5), per SD change in score. CONCLUSIONS: In this study, the risk of WRMSDs was more strongly influenced by specific psychosocial and physical job-related exposures than by broad socioeconomic factors such as education and income.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".