Gender-Affirming identification documents are associated with positive health-related outcomes for transgender people in Bolivia
Bibliographic record
Abstract
Purpose Legislation has improved access to gender-affirming identification documents (IDs) in many countries but few studies address the laws’ effects. We document the relationship between gender-affirming IDs and four measures of health-related outcomes for a nonprobability sample of transgender people in Bolivia, where a 2016 gender identity law improved access to gender-affirming IDs.Methods We interviewed 18 transgender advocates in three Bolivian cities about the law and its effects. We then developed a survey with the Organization of Transgender, Transsexual, and Transfeminine People of Bolivia (OTRAF) and surveyed 93 transgender people from across Bolivia about their demographics, work, health, discrimination experiences, and political engagement in June 2023.Results Interviews emphasize the importance of gender-affirming IDs to transgender Bolivians. Survey respondents who had changed their names and gender data on their IDs were significantly more likely to report taking a human immunodeficiency virus (HIV) test in the last six months, to have taken hormone replacement therapy (HRT), and to be taking HRT under doctor supervision than those who have not, in logistic regression and propensity score matching analyses. The analyses include variables that could influence health-related outcomes such as income, education, employment, gender identity, location, and age.Conclusion We suggest that access to gender-affirming IDs can improve health-related outcomes and that gender identity laws have important public health impacts for transgender communities.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".