Understanding How Sexual and Gender Minority Stigmas Influence Depression Among Trans Women and Men Who Have Sex with Men in India
Bibliographic record
Abstract
PURPOSE: Few studies have assessed how sexual and gender minority stigmas affect the mental health of trans women and self-identified men who have sex with men (MSM) in India, populations with a high HIV burden. We tested whether social support and resilient coping act as mediators of the effect of sexual and gender minority stigmas on depression as proposed by Hatzenbuehler's psychological mediation framework, or as moderators based on Meyer's minority stress theory. METHODS: We conducted a cross-sectional survey among trans women (n = 300) and MSM (n = 300) recruited from urban and rural sites in India. Standardized scales were used to measure depression (outcome variable), transgender identity stigma/MSM stigma (predictor variables), and social support and resilient coping (tested as moderators and parallel mediators). The mediation and moderation models were tested separately for trans women and MSM, using Hayes' PROCESS macro in SPSS. RESULTS: Participants' mean age was 29.7 years (standard deviation 8.1). Transgender identity stigma and MSM stigma were significant predictors (significant total and direct effects) of depression, as were social support and resilient coping. Among trans women and MSM, social support and resilient coping mediated (i.e., significant specific indirect effects), but did not moderate, the effect of stigma on depression, supporting the psychological mediation framework. CONCLUSION: Sexual and gender minority stigmas are associated with depression, with social support and resilient coping as mediators. In addition to stigma reduction interventions at the societal level, future interventions should focus on improving social support and promoting resilience among trans women and MSM in India.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".