Mental health and challenges of transgender women: A qualitative study in Brazil and India
Bibliographic record
Abstract
Background: Transgender women from low- and middle-income countries (LMICs) are understudied, their coping strategies and struggles underrecognised.Aims: This study aimed to explore the lived experiences of transgender women from two major cities located in Brazil and India, LMICs with high rates of transphobia and gender-based violence.Methods: We conducted a mixed-methods, exploratory study, including focus group discussions (FGDs) and brief survey interviews with 23 transgender women from Hyderabad, India and 12 transgender women from Rio de Janeiro, Brazil. Herein we present the combined (qualitative and quantitative) results related to discrimination, stigma, violence, and suicidality in transgender women’s lives.Results: Three major themes emerged from FGDs: stigma and discrimination; violence, and suicidality. Lack of education and working opportunities influence high levels of poverty and engagement in survival sex work by transgender women in both cities. Study participants live in large cities with more than 6 million inhabitants, but transgender women reported chronic social isolation. Participants disclosed frequent suicide ideation and suicide attempts. Brief surveys corroborate FGD findings, identifying high prevalence of discrimination, intimate partner violence, suicidality and low social support.Discussion: Multiple layers of stigma, discrimination, violence and social isolation affect transgender women’s quality of life in Hyderabad and Rio de Janeiro. Strategies sensitive to gender and culture should be implemented to tackle entrenched prejudice and social exclusion reported by transgender women. Additional social support strategies, better access to education and employment opportunities are also urgently needed. Improving the availability of evidence-based mental health interventions addressing the high prevalence of suicidality among transgender women from Hyderabad, India and Rio de Janeiro, Brazil should be prioritized.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".