Structural, Dosing, and Risk Change Factors Affecting Discontinuation of Pre-exposure Prophylaxis (PrEP) in a Large Urban Clinic
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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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".