Occupational Post-Exposure Prophylaxis (PEP) against Human Immunodeficiency Virus (HIV) Infection in a Health District in Cameroon: Assessment of the Knowledge and Practices of Nurses
Bibliographic record
Abstract
BACKGROUND: Health care providers are at risk of acquiring human immunodeficiency virus (HIV) infection from occupational exposure, with nurses being the most vulnerable. There is no data on the awareness of post-exposure prophylaxis (PEP) among nurses in Cameroon. This study aimed to assess the knowledge, practices of nurses regarding PEP for HIV and their determinants in Cameroon. METHODS: A cross-sectional study was conducted between April and July 2013, and involved 80 nurses in a rural health district in the North West Region of Cameroon. Data was collected using a structured questionnaire and analysed using the SPSS software version 20. RESULTS: In all, 73.7% of the participants had poor knowledge about PEP for HIV. Though many (83.8%) had heard about PEP, just 10 (12.5%) had received formal training on PEP for HIV. Only 24 (30%) and 20 (25%) knew the correct drug regimen and duration of treatment respectively. The majority (85%) considered themselves to be at risk of acquiring HIV at work, with 54 (67.5%) having experienced an exposure in the past, mainly while setting up intravenous lines (57.4%), recapping needles (37.0%) and during delivery (24.1%). Of those exposed, ten (18.9%) received PEP, which was started after 24 hours in 50%. In multivariable regression analyses, awareness of hospital policy [OR: 0.043 (0.005-0.404), p-value = 0.006] was associated with Good knowledge on PEP for HIV. CONCLUSIONS: The knowledge and practice of nurses on PEP for HIV in Cameroon is low. There is urgent need for training programmes and workshops to increase awareness, improve practice, and reduce the risk of HIV acquisition from work related activities among health care providers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".