Interactive Online Educational Modules Increase HIV and STI Prevention Competency Among Medical Students
Bibliographic record
Abstract
Abstract Background: The rate of new HIV diagnoses in the United States continues to slowly decline, yet, men who have sex with men and transgender women remain disproportionately affected. Key to improving quality of prevention services are providers who are comfortable addressing concepts of HIV prevention and sexual health across the spectrum of gender identities and sexual orientations. It is important to begin HIV prevention and sexual health education before providers’ practice habits are established. To address this need, we developed interactive educational modules and tested their impact on HIV prevention knowledge and awareness in future providers. Methods: Sexual health providers at an academic hospital, research clinicians, community engagement professionals, and New York City community members were consulted to develop seven educational modules, which were piloted among medical students. Knowledge of HIV and sexually transmitted infection (STI) prevention and comfort assessing prevention needs of various patients were assessed via online questionnaires administered before and after our educational intervention. Results: Pre- and post-module surveys were completed by 125 and 89 students, respectively, from all four years of training. Before the intervention, the majority of students had heard of HIV pre-exposure prophylaxis ( PrEP) (99.2%) and post-exposure prophylaxis (PEP) (92.7%). About 31% (38/123) of students agreed that they could confidently identify a patient who is a candidate for PrEP or PEP – this increased to 91% (81/89) following intervention. Conclusions: Our findings highlight a need for increased HIV and STI prevention training in medical school curricula to enable future providers to care for diverse, at-risk patient populations. Interactive modules offer an effective way to teach these concepts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".