Co-occurring psychosocial conditions are associated with increased HIV acquisition and transmission risk among young transgender women in Lima, Peru
Bibliographic record
Abstract
In Peru, transgender women (TW) are highly burdened by the HIV epidemic and stigma-related psychosocial conditions. Yet, a dearth of research has assessed co-occurring psychosocial conditions and HIV vulnerability among young TW. From February-July 2022, a community-recruited sample of young TW ages 16-24 years (N = 211) completed a cross-sectional socio-behavioral survey and HIV testing in Lima. Poisson regression models with robust variance estimated the association of indexes of co-occurring psychosocial conditions-childhood (family rejection, bullying, adverse childhood experiences, childhood sexual abuse), violence (psychological, physical, sexual, police violence), mental health (psychological distress, posttraumatic stress disorder, alcohol use disorder, non-injection drug use), and all (range = 0-12)-with past 6-month anal or vaginal condomless sex. Median age was 23 years, the majority were ethno-racial minority (35.1% Indigenous, 34.1% Mestiza, 12.3% Afro-Peruvian), 50.7% reported past 30-day sex work, 33.6% were HIV seropositive, and 42.0% reported past 6-month condomless sex. In separate multivariable sociodemographic-adjusted models, each index was associated with elevated prevalence of past 6-month condomless sex (all p < 0.05). For the overall index, each psychosocial condition increased the prevalence of past 6-month condomless sex by 16% (range = 8-23%). Understanding and intervening on co-occurring psychosocial conditions will be vital to mitigate HIV vulnerability among young TW in this context.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".