Targeting prevention programs for young and new healthcare workers: what is the association of age and job tenure with occupational injury in healthcare?
Bibliographic record
Abstract
BACKGROUND: previous evidence suggests young and new workers experience an increased risk of occupational injury. We sought to confirm this observation for healthcare workers. METHODS: a retrospective cohort of 42,771 healthcare workers (88.2% women) was constructed from an active injury surveillance database. Over 2 years, incidence rates and crude and adjusted relative risks for occupational injury were compared between age groups and job tenures. RESULTS: there were opposite trends in the two main types of injuries which cancelled each other: new workers and young workers had a decreased (not increased) risk of musculoskeletal sprain and strain injuries (adjusted RR [95% CI] for new hires was 0.60 [0.48, 0.73], and 0.85 [0.73, 0.98] for workers <25 years old); but an increased risk of cut and puncture injuries (1.25 [1.07, 1.45] for new hires, 1.28 [0.99, 1.67] for workers <25 years old). CONCLUSIONS: contrary to studies of other sectors, younger age and shorter tenure were not universal risk factors for occupational injuries in the female dominated healthcare sector. Young and new workers had increased risk of cuts and punctures, but a decreased risk of musculoskeletal injuries.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".