O2-S6.05 Sexual violence against men who have sex with men and transgenders (MSM-T) in southern India
Bibliographic record
Abstract
Introduction Sexual violence (SV) has been associated globally with vulnerability to HIV and other sexually transmitted infections (STIs) in marginalised populations. There is little information on SV among men who have sex with men and transgendered individuals (MSM-T) in South Asia. Societal sanctions resulting in stigma and discrimination may place MSM-T at especially high risk for both SV and HIV/STIs. We investigated factors associated with SV among MSM-T, and the relationship between health care utilisation patterns and reported SV. Methods Data were obtained from cross-sectional surveys in four districts in Karnataka state, south India. Bivariate and multivariable logistic regression models were constructed to examine factors related to SV. Multivariable negative binomial regression models examined the association between physician visits and SV. Normalised weights were used to account for a complex sampling design. Results The total sample size was 543. The prevalence of SV in the past year was 18%, with a range by district of 12%–31%. HIV prevalence among those reporting SV was 20%, compared to 12% among those not reporting SV. In a multivariable model, among those reporting sex work involvement, having anal sex with five or more casual sex partners in the past week was associated with SV (AOR: 4.1; 95% CI: 1.2% to 14.3%, p=0.03). Of those not reporting sex work, feminised sexual identities (AOR: 6.10; 95% CI: 1.3% to 28.8%, p=0.03) and younger age (AOR: 0.9; 95% CI: 0.9% to 1.0%, p=0.002) were associated with SV. There was no significant association between numbers of partners and SV among those not reporting sex work. Increased physician visits among those reporting SV was reported only for those involved in sex work (AOR: 1.7; 95% CI: 1.1% to 2.7%, p=0.01). Conclusions There was a trend towards higher HIV prevalence overall among MSM-T reporting sexual violence, as well higher HIV prevalence among important sub-groups experiencing SV. These findings highlight the importance of integrating programs to address sexual violence into STI/HIV prevention programs among MSM-T, and of understanding that there is considerable contextual heterogeneity in vulnerability to violence in this population. The demonstrated higher rates of physician utilisation by male sex workers who report sexual violence suggests an important point of contact for a population which may otherwise be quite hidden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".