Structural, Dosing, and Risk Change Factors Affecting Discontinuation of Pre-exposure Prophylaxis (PrEP) in a Large Urban Clinic
Why this work is in the frame
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Bibliographic record
Abstract
Understanding why clients stop taking pre-exposure prophylaxis (PrEP) is critical to improve PrEP delivery and ultimately reduce HIV incidence. We analyzed data from a programmatic evaluation conducted at the Los Angeles LGBT Center from February to May 2018. Of 180 respondents to the emailed survey, 91 had stopped taking PrEP and 11 never started. Among former PrEP users, most common reasons for stopping were entering a monogamous relationship (43%) and side effects (40%). Ten of 11 who never started PrEP reported access barriers (e.g., cost, insurance problems). A quarter of inactive clients re-engaged with PrEP services following the survey and 15% restarted PrEP by October 2018. Improving PrEP retention may require multifaceted interventions-e.g., tailored discussions about stopping and restarting PrEP safely as HIV risk changes, ensuring consistent access to affordable PrEP, and alternative dosing strategies. An emailed survey may be a simple, effective strategy to reengage some PrEP clients.
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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 | 0.001 |
| 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) | 0.000 | 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 it