Sharp injuries and their determinants among health care workers at first-level care facilities in Sindh Province, Pakistan
Bibliographic record
Abstract
SUMMARY OBJECTIVES: To assess the rate and determinants of sharp injuries during the previous 6 months among health care workers at first-level care facilities in two districts of Pakistan. METHODS: Cross-sectional survey at public, general practitioners and non-licensed private practitioners selected through stratified random sampling. At each facility, we interviewed a prescriber and a dispenser/injection provider about knowledge of bloodborne pathogens transmission and preventive practices, risk perception, and use of precautions and sharp injuries received during the previous 6 months. Multivariable Poisson regression was used to assess the factors associated with the number of sharp injuries. RESULTS: Fifty-four percentage of the 233 workers had at least one injury during the previous 6 months. The overall rate of sharp injuries per person per year was 3.7; among non-physician prescribers (9%), it was 4.3; among dispensers (69%), it was 3.7, and among physicians (18%), it was 2.1. In the multivariable model, work experience, risk perception and type of health care worker were significantly associated with receiving sharp injuries during the previous 6 months. In the model including dispensers only, a higher knowledge score was associated with fewer sharp injuries, while perceived severity of disease and lack of professional qualification were associated with more. CONCLUSIONS: Sharp injuries are common in Pakistan. Better knowledge about modes of bloodborne pathogen transmission and professional qualification may reduce their incidence.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".