Do Personal Factors or Types of Physical Tasks Predict Workplace Injury?
Bibliographic record
Abstract
Occupational health research has shown that certain worker and job characteristics are risk factors for workplace injuries. Workers who engage in physically demanding jobs, especially those jobs that involve repetitive motion, are at greater risk for work-related musculoskeletal disorders (WMSD). These risks are particularly prevalent in the health care sector. It is often reported that nurses are at higher risk of workplace musculoskeletal injury than other health care workers due to frequent lifting and transfer of patients and the prevalence of workplace violence. However, many analyses of the physical requirements of jobs do not consider the modifying effect of time spent on a physical task and the risk of WMSD. This study compared the risks of WMSD among workers in health care facilities based on the type of physical tasks and amount of time workers spent on such tasks. Workers who worked longer on a physical task reported more WMSD than those who spent less time on the same physical task. The risk of WMSD was twice as high (odds ratio [OR] = 2.3) among workers who sit less than 2 hours each day compared with those who sit longer. This study found that physical tasks associated with health care jobs and the amount of time spent on these tasks constitutes serious risk factors for WMSD.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".