Hormoneization for trans people in Divinópolis-MG, Brazil: a population description for the recognition of health demands
Bibliographic record
Abstract
Abstract The present study aimed to evaluate the adequacy of hormoneization in the transgender population of Divinópolis-MG according to national and international protocols, as well as associated factors. It is a quantitative cross-sectional investigation with a non-probabilistic sample listed using the virtual snowball technique. Participants answered an online questionnaire on the Google Forms platform. Among the 48 employees, 77% (n = 37) declared themselves to be transgender binary. Thirty participants reported hormoneization, in 17 of them it was considered inadequate (58.8%). The following variables showed the greatest disagreement with the recommended protocols: non-compliance or dissatisfaction with medical follow-up (p < 0.05) and exclusive use of the SUS for health care (p < 0.05). A quarter of the participants stated disrespect for the rectified social or civil name in the places frequented, which was more frequent among non-binary people (p = 0.03). There has been a high incidence of reports of violence and unprotected sexual intercourse in the last year.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".