Assessing the educational impact of a new HIV PrEP training module among primary care providers in Southeast Ontario: results from immediate and 3-months post-training evaluation surveys
Bibliographic record
Abstract
Primary care providers (PCPs) in Canada frequently report limited knowledge and confidence in prescribing HIV pre-exposure prophylaxis (PrEP). To address this gap, we developed and evaluated an online educational module designed to enhance PCPs’ knowledge and PrEP-related clinical skills. Pre- and post-training surveys (n = 38 and n = 20, respectively) showed substantial improvements: understanding of PrEP eligibility increased by 46%; knowledge of medications and monitoring by 55–180%; skills in medication management by 57–68%; skills in client monitoring by 47–84%; and knowledge regarding PrEP discontinuation by 84%. All participants (100%) agreed that the module met their expectations and was highly valuable, applicable, and useful to their clinical practice. Qualitative feedback highlighted the need for audio narration, more downloadable materials, and more inclusive, patient-centered content. Overall, these findings indicate that the online module effectively enhances PCPs’ readiness to prescribe oral PrEP and addresses key gaps in HIV prevention training.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".