Occupational injury among full-time, part-time and casual health care workers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.006 | 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 it