Occupational injury among cooks and food service workers in the healthcare sector
Bibliographic record
Abstract
BACKGROUND: Incidence of occupational injury is anticipated to be high among cooks and food service workers (CFSWs) because of the nature of their work and the types of raw and finished materials that they handle. METHOD: Incidents of occupational injury, resulting in lost time or medical care over a period of 1 year in two health regions were extracted from a standardized operational database and with person years obtained from payroll data, detailed analysis was conducted using Poisson regression modeling. RESULTS: Among the CFSWs the annual injury rate was 38.1 per 100 person years. The risk of contusions [RR, 95% CI 9.66 (1.04, 89.72)], burns [1.79 (1.39, 2.31)], and irritations or allergies [3.84 (2.05, 7.18)] was found to be significantly higher in acute care facilities compared to long-term care facilities. Lower risk was found among older workers for irritations or allergies. Female CFSWs, compared to their male counterparts, were respectively 8 and 20 times more likely to report irritations or allergies and contusions. In respect to outcome, almost all irritations or allergies required medical visits. For MSI incidents, about 67.4% resulted in time-loss from work. CONCLUSIONS: Prevention policies should be developed to reduce the hazards present in the workplace to promote safer work practices for cooks and food service workers.
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.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".