Occupational injury among full-time, part-time and casual health care workers
Bibliographic record
Abstract
BACKGROUND: Previous epidemiological studies have conflicting suggestions on the association of occupational injury risks with employment category across industries. This specific issue has not been examined for direct patient care occupations in the health care sector. AIMS: To investigate whether work-related injury rates differ by employment category (part time, full time or casual) for registered nurses (RNs) in acute care and care aides (CAs) in long-term facilities. METHODS: Incidents of occupational injury resulting in compensated time loss from work, over a 1-year period within three health regions in British Columbia (BC), Canada, were extracted from a standardized operational database. Detailed analysis was conducted using Poisson regression modeling. RESULTS: Among 8640 RNs in acute care, 37% worked full time, 24% part time and 25% casual. The overall rates of injuries were 7.4, 5.3 and 5.5 per 100 person-years, respectively. Among the 2967 CAs in long-term care, 30% worked full time, 20% part time and 40% casual. The overall rates of injuries were 25.8, 22.9 and 18.1 per 100 person-years, respectively. In multivariate models, having adjusted for age, gender, facility and health region, full-time RNs had significantly higher risk of sustaining injuries compared to part-time and casual workers. For CAs, full-time workers had significantly higher risk of sustaining injuries compared to casual workers. CONCLUSIONS: Full-time direct patient care occupations have greater risk of injury compared to part-time and casual workers within the health care sector.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".