Social, economic, and physical side effects impact PrEP uptake and persistence among transgender women in Peru
Bibliographic record
Abstract
INTRODUCTION: Oral pre-exposure prophylaxis (PrEP) for HIV-1 infection is over 99% effective in protecting against HIV acquisition when used consistently and appropriately. However, PrEP uptake and persistent use remains suboptimal, with a substantial gap in utilization among key populations who could most benefit from PrEP. In Latin America specifically, there is poor understanding of barriers to PrEP uptake and persistence among transgender (trans) women. METHODS: In April-May 2018, we conducted qualitative interviews lasting 25-45 min as part of an end-of-project evaluation of TransPrEP, a pilot RCT that examined the impact of a social network-based peer support intervention on PrEP adherence among trans women in Lima, Peru. Participants in the qualitative evaluation, all adult trans women, included individuals who either (1) screened eligible to participate in the TransPrEP pilot, but opted not to enroll (n = 8), (2) enrolled, but later withdrew (n = 6), (3) were still actively enrolled at the time of interview and/or successfully completed the study (n = 16), or (4) were study staff (n = 4). Interviews were audio recorded and transcribed verbatim. Codebook development followed an immersion/crystallization approach, and coding was completed using Dedoose. RESULTS: Evaluation participants had a mean age of 28.2 years (range 19-47). When describing experiences taking PrEP, participant narratives highlighted side effects that spanned three domains: physical side effects, such as prolonged symptoms of gastrointestinal distress or somnolence; economic challenges, including lost income due to inability to work; and social concerns, including interpersonal conflicts due to HIV-related stigma. Participants described PrEP use within a broader context of social and economic marginalization, with a focus on daily survival, and how PrEP side effects negatively contributed to these stressors. Persistence was, in some cases, supported through the intervention's educational workshops. CONCLUSION: This research highlights the ways that physical, economic, and social side effects of PrEP can impact acceptability and persistence among trans women in Peru, amplifying and layering onto existing stressors including economic precarity. Understanding the unique experiences of trans women taking PrEP is crucial to informing tailored interventions to improve uptake and persistence.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".