High incidence of occupational exposures among healthcare workers in Erbil, Iraq
Bibliographic record
Abstract
INTRODUCTION: The current status of percutaneous injury and mucous exposures (PMEs) of hospital workers and factors associated with the injuries have not been studied in Iraq. This study aimed to evaluate the epidemiology of PMEs with blood or body fluids that leads serious risks for healthcare workers (HCWs). METHODOLOGY: An analytic, cross-sectional survey study was conducted among HCWs in Erbil city center, Iraq. The study was performed at sevenhospitals, and 177 participants were included. The dependent variable was the occurrence of PMEs in the last year, and the independent variables were age, sex, occupation of HCWs, working site, and work duration. RESULTS: A total of 177 HCW participants included 57 nurses/midwives (32.2%), 59 doctors (33.3%), 27 laboratory workers (15.3%), and 34 paramedics/multipurpose workers (19.2%) from seven hospitals. The study concluded that 67.8% of the participants reported at least one occupational PME in the last year. In all, 13.3/person/year PME incidents were reported for nurses, 9.74/person/year for paramedics/multipurpose workers, 6.71/person/year for doctors, and 3.37/person/year laboratory workers. The mean number of PME incidents was 8.91/person/year. HCWs showed 85.0% compliance with wearing mask in risky situations. The most dangerous action for occupational exposure was blood taking (39.0%). In the univariate analysis, none of the investigated variables were found to be significantly related to PME. CONCLUSIONS: Occupational injuries and exposures in Iraqi HCWs are extremely common; awareness about protection is not sufficient. Nurses were found to be the highest risk group among HCWs. Preventive actions should be taken to avoid infection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".