‘Our life is pointless … ’: Exploring discrimination, violence and mental health challenges among sexual and gender minorities from Brazil
Bibliographic record
Abstract
Worldwide, Brazil has the highest prevalence of violence and hate crimes against sexual and gender minorities (SGMs) among countries with available data. To explore the impact of this scenario, we conducted a qualitative study with 50 SGMs from Rio de Janeiro, Brazil. Among the participants, 66% screened positive for generalised anxiety disorder, 46% for major depressive disorder and 39% for PTSD. A third reported low self-esteem (32%) and one quarter low social support (26%). Experiences of interpersonal discrimination were highly prevalent (>60%), while institutional discrimination related to employment or healthcare was reported by 46% of participants. Verbal abuse is very common (80%), followed by physical assault (40%). Sexual violence is highly frequent among women. Focus groups analysis highlighted three major domains: (1) stigma and discrimination (family, friends and partners, in schools and health services, influencing social isolation); (2) violence (bullying, harassment, physical and sexual violence); and (3) mental suffering (alcohol and drug abuse, depression, suicidality, anxiety). Our findings suggest a close synergy between experiences of discrimination and violence with selected mental disorders. This complex synergy might be better addressed by longer-term individual and group-level interventions that could foster social solidarity among the different groups that comprise SGMs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".