Multiple, active-offer referrals for HIV pre-exposure prophylaxis by nurses yields high uptake among gay, bisexual, and other men who have sex with men
Bibliographic record
Abstract
INTRODUCTION: Current Canadian guidelines focus on indications and uptake of preexposure prophylaxis (PrEP) among groups at-risk for HIV, such as gay, bisexual, and men who have sex with men (GBM). Less, however, is known about the outcomes of PrEP offers. This study presents on the responses of GBM to multiple offers for PrEP. METHODS: In Ottawa, Canada, we instituted Canada's first nurse-led PrEP program, pre-exposure prophylaxis by nurses (PrEP-RN), where nurses offered PrEP referrals to persons with indicators for HIV. Responses to offers from nurses and HIV diagnoses were recorded and assessed for multiple occurrences based on responses. Descriptive analyses were used to report frequencies and percentages of findings and chi-square analyses were conducted to determine significance based on HIV risk for those who accepted versus declined PrEP. RESULTS: Over a 4-year period, 644 PrEP offers were made to 236 unique patients, all of whom were GMB, the majority of whom identified as male. Of the eligible patients, 50.8% accepted and 50.0% declined after multiple offers. Seven trajectories were identified in terms of reasons for accepting or declining PrEP. PrEP referrals made based on clinical guidelines and to those who had changes in risk factors over time were significantly correlated with PrEP acceptance. We noted five HIV diagnoses, all of which were among GBM who declined PrEP at least once. CONCLUSIONS: Based on these findings, it appears multiple PrEP may yield increased PrEP acceptance among a sample of GBM.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".