HIV pre-exposure prophylaxis (PrEP) should be free across Canada to those meeting evidence-based guidelines
Bibliographic record
Abstract
HIV pre-exposure prophylaxis (PrEP) should be free across Canada for all those who meet evidence-based guidelines. PrEP is a highly effective tool for preventing HIV acquisition that has been approved for use in Canada since 2016. However, without public drug plans or private insurance, generic PrEP costs approximately $200 to $250 CAD monthly. Current PrEP programs across Canada are a confusing patchwork system with variability in coverage and prohibitive co-payments, making PrEP too expensive for many equity-deserving groups. However, publicly funded PrEP programs are demonstrated to be cost-effective and even cost-saving by reducing the long-term healthcare expenditures associated with managing HIV. PrEP is not just an individual-level clinical tool. It is a public health intervention. Alongside “treatment as prevention,” PrEP is an important population-level strategy for eliminating new HIV infections in Canada and can play a role in helping to address complex health inequities affecting communities highly affected by HIV. Navigating drug coverage for patients consumes time and resources among healthcare providers that could be spent helping to improve other social determinants of health. Affordability will remain the foremost barrier to PrEP access until PrEP is made free to all those who meet evidence-based guidelines.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".