Preferences for HIV preexposure prophylaxis care among gay, bisexual, and other MSM: a large discrete choice experiment
Bibliographic record
Abstract
OBJECTIVE: We aimed to identify preferences for preexposure prophylaxis (PrEP) care among diverse gay, bisexual, and other MSM (GBM) in the United States with discrete choice experiment (DCE). DESIGN: We conducted two DCEs to elicit care delivery preferences for starting and continuing PrEP among 16-49-year-old GBM who were HIV-negative and not using PrEP from across the United States. DCEs assessed preferences for care options including location, formulation (pills, injectable), lab testing, and costs. Participants completed 16 choice tasks, and utility scores and relative importance were estimated. We performed latent class analyses to identify groups within each DCE, and multivariable logistic regression to identify sociodemographic characteristics associated with class membership. RESULTS: Among 1514 participants, 46.5% identified as Latino, 21.4% Black, and 25.2 White. For Starting PrEP DCE, two latent classes were identified: 'In-Person' (28.5%), which preferred in-person care and lab testing, and 'Virtual' (71.5%), which preferred telehealth and at-home lab testing. For Continuing PrEP DCE, two latent classes were identified: 'Pills' (23.6%), which preferred oral PrEP with low-cost options and 'No cost/injectable' (76.4%), which strongly preferred no-costs and injectable PrEP. In multivariable models for Starting PrEP and for Continuing PrEP, latent class membership was significantly associated with a range of sociodemographic characteristics, including race/ethnicity, income, housing instability, and provider and PrEP stigma. CONCLUSION: The preferences identified for PrEP care in this diverse GBM sample indicate the need for multiple care and formulation choices, including elimination of costs to improve PrEP uptake. DCE findings can guide implementation efforts to improve equitable access to PrEP.
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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) | 0.000 | 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 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".