Implementation and acceptance of pharmacists’ prescribing of human immunodeficiency virus (HIV) pre-exposure prophylaxis (PrEP)
Bibliographic record
Abstract
Background: Pre-exposure prophylaxis (PrEP) for human immunodeficiency virus (HIV) is highly effective at reducing the risk of acquiring HIV. PrEP is underused due, in part, to prescriber inaccessibility. The overall aim of this study was to evaluate the impact of pharmacist PrEP management (including prescribing and monitoring) on clinical and acceptance outcomes in patients who are at high risk for HIV exposure. Methods: Pharmacist-led PrEP management was guided by a prescribing protocol and implemented at 10 community pharmacies in Nova Scotia over 6 months. Baseline and follow-up bloodwork determined HIV status, coinfection(s), and other eligibility criteria for initial and refill PrEP prescriptions. Patient acceptance was measured according to the theoretical framework for acceptability of health care interventions. Results: Forty-five participants met eligibility criteria, and 37 remained for the study duration. Around half of the participants had never used PrEP before, and all identified as men who have sex with men or transgender women. Participants were highly accepting of the service and agreed that pharmacist-led PrEP management should always be available. Few reported privacy or stigma/discrimination concerns. All participants remained HIV-negative during study participation, and participants with coinfections were linked with care ( n = 4). Interpretation: The service was acceptable and effective for patients. Future work is required to reach underserved populations, particularly individuals with injection-related HIV risk factors. Conclusions: Pharmacist-led PrEP management can provide an alternative way to obtain PrEP for higher-risk patients. This study resulted in a regulation change on July 1, 2024 that authorized pharmacists to prescribe PrEP in Nova Scotia.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".