Syndemic violence victimization, alcohol and drug use, and HIV transmission risk behavior among HIV-negative transgender women in India: A cross-sectional, population-based study
Bibliographic record
Abstract
Transgender women globally are disproportionately burdened by HIV. Co-occurring epidemics of adverse psychosocial exposures accelerate HIV sexual risk, including among transgender women; however, studies using additive models fail to examine synergies among psychosocial conditions that define a syndemic. We examined the impact of synergistic interactions among 4 psychosocial exposures on condomless anal sex (CAS) among transgender women in India. A national probability-based sample of 4,607 HIV-negative transgender women completed the Indian Integrated Biological and Behavioural Surveillance survey, 2014-2015. We used linear probability regression and logistic regression to assess 2-, 3-, and 4-way interactions among 4 psychosocial exposures (physical violence, sexual violence, drug use, and alcohol use) on CAS. Overall, 27.3% reported physical and 22.3% sexual violence victimization (39.2% either physical or sexual violence), one-third (33.9%) reported frequent alcohol use and 11.5% illicit drug use. Physical violence was associated with twofold higher odds of CAS in the main effects model. Statistically significant two- and three-way interactions were identified, on both the multiplicative and the additive scales, between physical violence and drug use; physical and sexual violence; physical violence, sexual violence, and alcohol use; and physical violence, alcohol use and drug use. Physical and sexual violence victimization, and alcohol and drug use are highly prevalent and synergistically interact to increase CAS among HIV-negative transgender women in India. Targeted and integrated multilevel initiatives to improve the assessment of psychosocial comorbidities, to combat systemic transphobic violence, and to provide tailored, trauma-informed alcohol and substance use treatment services may reduce HIV risk among transgender women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".