High Willingness to Use HIV Pre-Exposure Prophylaxis Among Transgender Women in Argentina
Bibliographic record
Abstract
Abstract Purpose: In Argentina, transgender women face a disproportionately high prevalence of HIV infection (34%). Although not currently approved in Argentina, pre-exposure prophylaxis (PrEP) may offer a potential effective HIV prevention tool for this population. In this study, we assessed the willingness to use PrEP among transgender women in Argentina. Methods: Data were drawn from a nationwide cross-sectional survey conducted among transgender women in 2013. Using multivariable logistic regression, we assessed the prevalence of and factors associated with willingness to use PrEP among transgender women with negative or unknown HIV status. Results: This study included 337 transgender women (278 HIV negative and 59 with unknown HIV status), most of whom had a history of sex work involvement (81.8%). Overall, 301 (89.3%) expressed willingness to use PrEP. In a multivariable analysis, having casual sexual partners was positively associated with willingness to use PrEP (adjusted odds ratio [AOR]=4.26, 95% confidence interval [CI] 1.73–10.51), while discrimination by healthcare workers was negatively associated (AOR=0.33, 95% CI 0.12–0.88). Conclusion: We found high levels of willingness to use PrEP among transgender women in Argentina, suggesting that there is high perception of HIV risk in this population. However, discrimination by healthcare workers was a strong negative correlate of willingness to use PrEP, suggesting that multilevel interventions that address gender-based stigma in healthcare settings will be critical for the success of PrEP as an HIV prevention strategy in this population.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".