What Predicts a Clinical Discussion About PrEP? Results From Analysis of a U.S. National Cohort of HIV-Vulnerable Sexual and Gender Minorities
Bibliographic record
Abstract
HIV-outcome inequities remain prevalent in the U.S. Medical providers (MPs) are gatekeepers of PrEP, and understanding the dynamics of PrEP assessments is of major interest for public health. We analyzed data from Together 5000, an internet-based U.S. national cohort of sexual and gender minority (SGM) individuals aged 16-49 years and at risk for HIV. Among those eligible for PrEP uptake (n = 6264), we modeled predictors of discussing PrEP with an MP. A third (31%) of participants had spoken to a MP about PrEP. Among those who spoke to a MP, 45% suggested they would initiate PrEP; this outcome was more common among participants older than 24. With a persistent stagnant uptake nationwide, new opportunities to influence PrEP uptake must be explored. An attractive less targeted space is the medical office, specifically ways to support an initial and continued discussion about PrEP between MPs and their patients.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".