Are female healthcare workers at higher risk of occupational injury?
Bibliographic record
Abstract
BACKGROUND: Differential risks of occupational injuries by gender have been examined across various industries. With the number of employees in healthcare rising and an overwhelming proportion of this workforce being female, it is important to address this issue in this growing sector. AIMS: To determine whether compensated work-related injuries among females are higher than their male colleagues in the British Columbia healthcare sector. METHODS: Incidents of occupational injury resulting in compensated days lost from work over a 1-year period for all healthcare workers were extracted from a standardized operational database and the numbers of productive hours were obtained from payroll data. Injuries were grouped into all injuries and musculoskeletal injuries (MSIs). Detailed analysis was conducted using Poisson regression modelling. RESULTS: A total of 42 332 employees were included in the study of whom 11% were male and 89% female. When adjusted for age, occupation, sub-sector, employment category, health region and facility, female workers had significantly higher risk of all injuries [rate ratio (95% CI) = 1.58 (1.24-2.01)] and MSIs [1.43 (1.11-1.85)] compared to their male colleagues. CONCLUSIONS: Occupational health and safety initiatives should be gender sensitive and developed accordingly.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".