Transforming Health: International Rights-Based Advocacy for Trans Health
Bibliographic record
Abstract
Trans people across the world face substantial barriers to adequate health and health care. They are targets of discrimination and violence, are at greater risk of contracting HIV, and experience a higher incidence of mental health problems like depression. They face discrimination from health care providers, a lack of doctors trained to address their needs, and the refusal of many national health systems and insurance providers to cover their care. Yet trans communities are building alliances to promote trans health and to fight for policies that respect gender diversity and human rights. This report profiles projects from 16 organizations in twelve countries that address these barriers. These projects offered general health services as well as those related to gender transition, trained health care providers to respond to the needs and concerns of trans patients, conducted public education campaigns about discrimination against trans people, advocated for legal and medical policy changes, and organized trans communities to empower themselves. Collecting insights from these projects, Transforming Health makes recommendations to governments, rights advocates, health professionals and public health organizations, and health and rights donors. Organizations profiled include: Shustha Jibon (Bangladesh), Gay/Bi/Queer Trans Men’s Working Group (Canada), Kyrgyz Labrys (Kyrgyzstan), GenderDoc-M (Moldova), REDTRANS Nicaragua (Nicaragua), Trans-Gayten (Serbia), Gender DynamiX (South Africa), Mitr Trust (United States / India), Planned Parenthood of Mar Monte, (United States), Transgender Law Center (United States), STP 2012 International Stop Trans Pathologization Campaign (International), and the World Health Organization.
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 | 0.039 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.038 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".