Violence and Mental Health Among Gender-Diverse Individuals Enrolled in a Human Immunodeficiency Virus Program in Karnataka, South India
Bibliographic record
Abstract
Abstract Purpose: Gender-diverse individuals in India face considerable discrimination, stigma, and violence. There is a dearth of published literature describing experiences of violence among this population and potential links to mental health. Methods: A questionnaire was administered to 282 study participants, 18 years of age and older, who self-identified as hijra, kothi, double decker, or bisexual and were actively enrolled in a local HIV prevention program in Bangalore, India in 2012. Responses were used to calculate a composite depression/anxiety score. Associations between sociodemographic characteristics and experiences of physical and sexual violence in the previous six months were tested and differences in depression/anxiety score based on experiences of violence were explored. Results: Recent physical violence was common among study participants and was reported among 46% of nirvan (emasculated) hijras (transgender), 42% of akwa (not emasculated) hijras, and 25% of kothis (feminine acting males). Rape in the previous year was particularly common among akwa hijras (39%). Factors associated with being raped included younger age, less education, and employment in basti (blessings), sex work, chela (disciple of hijra guru), or at a community-based organization. Kothis had the highest depression/anxiety score. No significant difference in depression/anxiety score based on recent history of physical violence or rape was found. Conclusions: Physical violence and poor mental health are common among gender-diverse individuals in Bangalore, Karnataka. There is a need for services that cater to the unique mental health needs of gender-diverse individuals in India, following rights-based approaches that address the underlying roots of oppression they encounter.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".