Factors Associated with Transgender-Based Discrimination Among <i>Travestis</i> and Transgender Women in Rio de Janeiro, Brazil
Bibliographic record
Abstract
Purpose: Transgender-based discrimination is associated with poor mental health, unemployment, and poverty. Travestis and transgender women (trans women) frequently experience transgender-based discrimination, but associated factors are understudied. Our objective was to identify the factors associated with transgender-based discrimination among trans women from Brazil. Methods: We used data from Transcendendo, a clinic-based cohort of trans women in Rio de Janeiro, Brazil. Eligible participants were ≥18 years old, assigned male sex at birth, and self-identified as travestis , transgender women, or other trans feminine identities. We analyzed baseline data for participants enrolled from August 2015 to March 2020. Face-to-face questionnaires collected data on socio-demographics, gender identity and expression, and transgender-based discrimination experience. Factors associated with transgender-based discrimination were evaluated through multivariable linear regression. Results: Out of the 587 participants, 559 (95%) were included (28 excluded due to missing data). Mean age was 33 years, the majority identified as transgender women (40%), 71% reported current or past sex work, and 43% self-reported as living with HIV. In multivariable regression models, factors significantly associated with transgender-based discrimination included having no supporting or one supporting parental figure (vs. support from both parents), living in poverty (vs. not), and current and past sex work (vs. never). Conclusions: Trans women without family support, who engaged in sex work or were economically deprived were more prone to transgender-based discrimination. A lack of parental support, the strongest independent predictor of transgender-based discrimination experiences, likely contributes to emotional and structural vulnerabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".