Enhancing HIV Pre-exposure Prophylaxis Practices via an Educational Intervention
Bibliographic record
Abstract
BACKGROUND: Pre-exposure prophylaxis (PrEP) for HIV involves using antiretroviral drugs to prevent individuals at high risk from acquiring HIV infection. Most practicing primary care providers believe PrEP to be safe and effective, but less than half have prescribed or referred for PrEP. Attitudes and prescribing patterns among house officers have not been well described previously. STUDY QUESTION: Can an educational intervention enhance HIV PrEP practices among internal medicine house officers? STUDY DESIGN: This study relied on a pretest/posttest design. All categorical trainees at a medium-sized internal medicine program were offered a baseline survey to assess their knowledge on PrEP. This was followed by a PrEP-focused educational intervention and a postintervention survey. MEASURES AND OUTCOMES: Likert scales captured perceptions regarding safety, effectiveness, barriers, factors that would promote PrEP use, potential side effects, impact on risk-taking behavior, and provider comfort level in assessing behavioral risks and in PrEP prescribing. Data were analyzed using descriptive statistics, Wilcoxon signed rank test, and the Kruskal-Wallis test. Significance was accepted for P < 0.05. RESULTS: Forty-eight (100%) trainees participated in the educational session, 45 (94%) in a preintervention survey, and 36 (75%) in a postintervention survey. Before PrEP training, 22% of respondents were unaware of PrEP, 78% believed PrEP was effective, 66% believed PrEP was safe, 62% had fair or poor awareness of side effects; 18% of residents had referred for or prescribed PrEP, and 31% believed they were likely to prescribe PrEP in the next 6 months. After the intervention, 94% of trainees believed PrEP was effective (P < 0.001), 92% believed PrEP was safe (P < 0.001), and two-thirds believed they were likely to prescribe PrEP in the next 6 months. CONCLUSIONS: Brief, focused training on HIV prevention promotes awareness, acceptance, and likelihood of prescribing PrEP by internal medicine trainees.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".