Bem-estar psicológico entre travestis e mulheres transexuais no Estado de São Paulo, Brasil
Bibliographic record
Abstract
This article sought to estimate factors associated with the psychological well-being of transvestites and trans women. It is a cross-sectional study with 602 transvestites and trans individuals in seven cities in the state of São Paulo, Brazil from 2014 to 2015. We carried out a sample selection through a consecutive approach and using the snowball technique. The dependent variable was psychological well-being (WHOQOL-BREF) and the independent variables were: sociodemographic characteristics, body modifications, health conditions, violence and incarceration. We used a multiple variance analysis to identify associated factors. Most were black or brown and were aged between 25 and 39 years, had up to complete secondary education, individual income of up to two times the minimum wage and worked, and 42.3% were sex workers. Around one-quarter had been incarcerated. Around one-quarter were in treatment for HIV. Mean psychological well-being score was 63.2 (95%CI: 61.8-64.6). In the multiple analysis, the factors associated with lower psychological well-being were: not having a fixed address, having lower educational levels, being dissatisfied with personal relationships, friend support or the gender-affirming procedures they had undergone and having suffered verbal or sexual violence. While worse living conditions and exposure to violence harm the psychological well-being of transvestites and trans women, the possibility of undergoing desired body transformations and having their social name respected interfere positively in their evaluations of their lives.
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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 | 0.002 |
| 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 | 0.001 |
| 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".