Risk of Occupational Blood Exposure in a Cohort of 24,000 Hospital Healthcare Workers: Position and Environment Analysis Over Three Years
Bibliographic record
Abstract
Early and efficient prevention of occupational blood exposure at hospital requires knowledge of exposures and risks according to staff characteristics. Calculation of annual exposure rates and relative rates from personal and occupational data. The overall annual incidence was 3.5 per 100 workers per year; maximum for nurses and midwives (6.5); minimum for cleaners and paramedics (0.6). Exposures affected mainly nurses (57.81% of accidents, 12.12% of cohort) and occurred mostly in the surgical and the medical departments (26.34 and 25.20% of accidents). Men/women and students/physicians rate differences were not significant. Emergency and intensive care staffs had the highest relative rates (4.27 and 3.05) compared with maintenance staff. Nurses and laboratory staff were more exposed than physicians (3.76 and 2.30 times) were. Our results prompt prevention and training to be precisely focused and efficiently devised.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".