Preparing for PrEP: Perceptions and Readiness of Canadian Physicians for the Implementation of HIV Pre-Exposure Prophylaxis
Bibliographic record
Abstract
Recent evidence has demonstrated the efficacy of pre-exposure prophylaxis (PrEP) for HIV prevention, but concerns persist around its use. Little is known about Canadian physicians' knowledge of and willingness to prescribe PrEP. We disseminated an online survey to Canadian family, infectious disease, internal medicine, and public health physicians between September 2012-June 2013 to determine willingness to prescribe PrEP. Criteria for analysis were met by 86 surveys. 45.9% of participants felt "very familiar" with PrEP, 49.4% felt that PrEP should be approved by Health Canada, and 45.4% of respondents were willing to prescribe PrEP. Self-identifying as an HIV expert (odds ratio, OR = 4.1, 95% confidence interval, CI = 1.6-10.2), familiarity with PrEP (OR = 5.0, 95%CI = 1.3-19.0) and having been asked by patients about PrEP (OR = 4.0, 95%CI = 1.5-10.5) were positively associated with willingness to prescribe PrEP on univariable analysis. The latter two were the strongest predictors on multivariate analysis. Participants cited cost and efficacy as major concerns. 75.3% did not feel that information had been adequately disseminated among physicians. In summary, Canadian physicians demonstrate varying levels of support for PrEP and express concerns about its implementation. Further research on real-world effectiveness, continuing medical education, and clinical support is needed to prepare physicians for this prevention strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".