Active-Offer Nurse-Led PrEP (PrEP-RN) Referrals: Analysis of Uptake Rates and Reasons for Declining
Bibliographic record
Abstract
While pre-exposure prophylaxis (PrEP) is an effective HIV prevention strategy, its uptake is limited. To address barriers, we piloted a nurse-led PrEP clinic in an STI clinic and had public health nurses refer patients during STI follow-up. We recorded the number of PrEP offers and declines and clinic uptake. We conducted a thematic analysis of patients' responses from nursing notes written at the time patients declined PrEP. From August 6, 2018 to August 5, 2019, nurses offered a PrEP referral to 261 patients who met our criteria; only 47.5% accepted. Qualitative analysis identified four themes: (1) perceptions of risk, (2) lack of interest, (3) inability to manage, and (4) concerns about PrEP. Our patients did not feel sufficiently at-risk for HIV to use PrEP and maintained that PrEP was for a reckless "other". This analysis sheds light on how assumptions about risk affect PrEP uptake, particularly among those at-risk for HIV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".