Blood and Body Fluid Exposure Related Knowledge, Attitude and Practices of Hospital Based Health Care Providers in United Arab Emirates
Bibliographic record
Abstract
OBJECTIVES: Knowledge, attitudes, and practices of healthcare providers related to occupational exposure to bloodborne pathogens were assessed in a tertiary-care hospital in Middle East. METHODS: A cross-sectional study was undertaken using a self-administered questionnaire based on 3 paired (infectivity known vs. not known-suspected) case studies. Only 17 out of 230 respondents had an exposure in the 12 months prior to the survey and of these, only 2 had complied fully with the hospital's exposure reporting policy. RESULTS: In the paired case studies, the theoretical responses of participating health professionals showed a greater preference for initiating self-directed treatment with antivirals or immunisation rather than complying with the hospital protocol, when the patient was known to be infected. The differences in practice when exposed to a patient with suspected blood pathogens compared to patient known to be infected was statistically significant (p < 0.001) in all 3 paired cases. Failure to test an infected patient's blood meant that an adequate risk assessment and appropriate secondary prevention could not be performed, and reflected the unwillingness to report the occupational exposure. CONCLUSION: Therefore, the study demonstrated that healthcare providers opted to treat themselves when exposed to patient with infectious disease, rather than comply with the hospital reporting and assessment protocol.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".