Social Networks Play a Complex Role in HIV Prevention Knowledge, Attitudes, Practices, and the Uptake of PrEP Through Transgender Women Communities Centered Around Three “Casas Trans” in Lima, Peru: A Qualitative Study
Bibliographic record
Abstract
Transgender women's (TW) social networks may facilitate HIV prevention information dissemination and normative reinforcement. We conducted a qualitative study of social networks among 20 TW affiliated with 3 "casas trans" (houses shared among TW) in Lima, Peru, using diffusion of innovations theory to investigate community-level HIV prevention norms. Participants completed demographic questionnaires, social network interviews, and semistructured in-depth interviews. Median age was 26 and all participants engaged in sex work. Interviews revealed high HIV prevention knowledge and positive attitudes, but low engagement in HIV prevention. Respondents primarily discussed HIV prevention with other TW. Network members' opinions about pre-exposure prophylaxis (PrEP) frequently influenced respondents' personal beliefs, including mistrust of healthcare personnel, concern that PrEP efficacy was unproven, fear of adverse effects, and frustration regarding difficulty accessing PrEP. Patterns of influence in TW networks may be leveraged to improve uptake of HIV prevention tools, including PrEP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".