The wisdom of mistrust: qualitative insights from transgender women who participated in PrEP research in Lima, Peru
Bibliographic record
Abstract
Although pre-exposure prophylaxis (PrEP) is a remarkable biomedical advance to prevent HIV, ongoing research on PrEP contributes to and interacts with a legacy of HIV experimentation on marginalized communities in resource-limited settings. This paper explores the complexity of PrEP research mistrust among Peruvian transgender (trans) women who completed a PrEP adherence intervention and those who refused participation (i.e. declined to enrol, voluntarily withdrew, and/or were lost to follow-up).Data were derived from 86 trans women (mean age 29 years) participants in the formative (four focus groups (n = 32), 20 interviews) and the evaluation stages (34 interviews) of a social network-based PrEP intervention for trans women in Lima, Peru. The formative stage took place from May to July 2015, while the evaluative stage took place from April to May 2018. Audio files were transcribed verbatim and analysed via an immersion crystallization approach using Dedoose (v.6.1.18).Three paradoxes of trans women's participation in PrEP science as a "key" population emerged as amplifying mistrust: (1) increases in PrEP research targeting trans women but limited perceived improvements in HIV outcomes; (2) routine dismissal by research physicians and staff of PrEP-related side effects and the social realities of taking PrEP, resulting in questions about who PrEP research is really for and (3) persistent limitations on PrEP access for trans women despite increasing involvement in clinical trials, fostering feelings of being a "guinea pig" to advance PrEP science.Findings highlight the wisdom inherent in PrEP mistrust as a reflection of trans women's experiences that underscore the broken bonds of trust between communities, researchers and the research enterprise. PrEP mistrust is amplified through perceived paradoxes that suggest to trans women that they are key experimental participants but not target PrEP users outside of research settings. Findings highlight the urgent need to reframe mistrust not as a characteristic of trans women to be addressed through education and outreach, but as a systemic institutional- and industry-level problem replicated, manifested and ultimately to be corrected, through global HIV science.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".