Health belief model to predict sharps injuries among health care workers at first level care facilities in rural Pakistan
Bibliographic record
Abstract
BACKGROUND: We assessed the frequency and predictors of sharp injuries (SIs) among health care workers (HCWs) at first level care facilities (FLCF) in rural Pakistan. METHOD: HCWs working at public clinic (PC), privately owned licensed practitioners' clinic (LPC) and non-licensed practitioners' clinic(NLC) were interviewed on universal precautions (UPs) and constructs of health belief model (HBM) to assess their association with SIs through negative-binomial regression. RESULTS: From 365 clinics, 485 HCWs were interviewed. Overall annual rate of SIs was 192/100 HCWs/year; 78/100 HCWs among licensed prescribers, 191/100 HCWs among non-licensed prescribers, 248/100 HCWs among qualified assistants, and 321/100 HCWs among non-qualified assistants. Increasing knowledge score about bloodborne pathogens (BBPs) transmission (rate-ratio (RR): 0.93; 95%CI: 0.89-0.96), fewer years of work experience, being a non-licensed prescriber (RR: 2.02; 95%CI: 1.36-2.98) licensed (RR: 2.86; 9%CI: 1.81-4.51) or non-licensed assistant (RR: 2.78; 95%CI: 1.72-4.47) compared to a licensed prescriber, perceived barriers (RR: 1.06; 95%CI: 1.03-1.08), and compliance with UPs scores (RR: 0.93; 95%CI: 0.87-0.97) were significant predictors of SIs. CONCLUSION: Improved knowledge about BBPs, compliance with UPs and reduced barriers to follow UPs could reduce SIs to HCWs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".