Knowledge, Awareness and Willingness to use HIV Pre-Exposure Prophylaxis (PrEP) Among Students at the University of Namibia
Bibliographic record
Abstract
Adequate knowledge, awareness and willingness to use HIV Pre-Exposure Prophylaxis (PrEP) is very important for HIV prevention mostly in developing countries where the burden of HIV infection continues to increase. The purpose of this study was to ascertain the level of knowledge, awareness, willingness to use HIV PrEP among tertiary students at the University of Namibia (UNAM), Rundu Campus. A quantitative, descriptive, cross-sectional design was used with a total of 232 first-year students across all three faculties at UNAM, Rundu Campus being selected using stratified random sampling. The study results showed that the majority (52% of the participants were female and 72% of the participants were aged between 18 and 24 years. It was also found that 45% of the participants reported that they had heard of PrEP and, of this number (n = 104), 88% reported that they were willing to use PrEP, although 8% only of the respondents had actually used it. Of the respondents who reported that they had heard of PrEP, both gender and the faculty at which they were studying were significantly associated (all p-values were less than 0.05) with their awareness and knowledge of, and willingness to use, PrEP. The study findings indicated a low level of awareness, knowledge and use of HIV PrEP among the respondents, although the degree of willingness to use PrEP was high among them.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".