Post-exposure prophylaxis among Ugandan nurses: “Accidents do happen”
Bibliographic record
Abstract
In 2009 we conducted a study to explore Ugandan nurses’ practice of universal precautions while caring for persons living with HIV. During our interviews about universal precautions, nurses’ also shared their experience with post-exposure prophylaxis (PEP) following needle-stick injuries. We present findings related to nurses’ understanding of PEP and their experience with, and reporting of, needle stick injuries. Nurses have high rates of exposure to blood-borne pathogens. Although there is minimal risk of the transmission of blood-borne pathogens from health care workers (HCWs) to patients and vice versa, post-exposure prophylaxis, has become routine following the occupational exposure of HCWs to HIV. Focused ethnography was used to guide the data collection and in-depth interviews were used to collect the data between October and November 2009. Sixteen nurses from a variety of units in a large teaching hospital participated. Needle-stick injuries were a fairly common occurrence, but written policies were frequently inaccessible to nurses and they did not have adequate knowledge of PEP. Some nurses were reluctant to report injuries and avoided following PEP procedures due to lack of knowledge about PEP, concerns about anti-retroviral side effects and the stigma associated with PEP. Participants were aware of PEP however there was a wide variation in their understanding of the procedure to follow after a needle-stick injury. Employers have a responsibility to update PEP guidelines and to orientate HCWs to these. Educators must ensure that undergraduate nurses have a comprehensive understanding of universal precautions and current practice for PEP.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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 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".