Knowledge of and Opinions on HIV Preexposure Prophylaxis Among Front-Line Service Providers at Canadian AIDS Service Organizations
Bibliographic record
Abstract
Oral daily tenofovir/emtricitabine (Truvada) is approved in the United States for HIV preexposure prophylaxis (PrEP) but has generated controversy in the media and within HIV-affected communities. We conducted an online survey about PrEP-related knowledge, experience, opinions, and learning needs, and received 160 responses from service providers at Canadian AIDS Service Organizations. Respondents were cautiously optimistic about PrEP and 48.8% believed that PrEP warranted Health Canada approval. In multivariable logistic regression, support for PrEP approval was associated with more years working in HIV (odds ratio=1.89 per decade, 95% CI=1.10, 3.25), low baseline familiarity with PrEP (OR=3.24, 95% CI=1.01, 14.41), and knowing someone who had used PrEP (OR=4.39, 95% CI=1.28,15.08). Participants major concerns about PrEP were similar to those highlighted in other publications, and some issues specific to certain target populations were raised. Several participants (26.2%) had been asked about PrEP in the past year and 10.6% knew of one or more Canadian who had used PrEP. Despite clients' interest, most participants thought that they (60.6%) or their organization (63.1%) did not have enough current knowledge about PrEP, highlighting the need for further education on this novel HIV prevention strategy.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".