Strategies to overcome barriers and enhance PrEP adoption among primary care providers in urban–rural communities outside Canada’s major metropolitan areas
Bibliographic record
Abstract
Pre-exposure prophylaxis (PrEP) in Ontario remains concentrated in large cities, leaving smaller urban and rural communities underserved. To inform targeted expansion, we interviewed 28 primary care providers (family physicians, trainees, public health nurses, clinic managers, and practice leads) working outside major metropolitan areas. Recruitment used multiple outreach methods, and interviews were transcribed and thematically analyzed. Half of participants had direct PrEP experience. Providers cited limited training, knowledge gaps, few continuing-education opportunities, staffing shortages, and lack of administrative support as barriers. Structural forces, stigma, high costs, transportation barriers, further limited access, intersecting with poverty, racism, and substance use, and affecting equity-deserving groups beyond gay and bisexual men. Participants recommended province-wide competency-based training, task-sharing through medical directives, normalization of PrEP in clinical discussions, broader awareness campaigns, and nurse-led models. Findings highlight the need to strengthen provider capacity while addressing social determinants to achieve equitable PrEP uptake outside Ontario’s major cities.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".