Health of transgender men in low-income and middle-income countries: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Despite the rapid growth of research on transgender (trans) health globally, the extent of research on trans men and other transmasculine persons assigned the female sex at birth remains unclear. We, therefore, conducted a scoping review on trans men's health in low-income and middle-income countries (LMICs). METHODS: The review included peer-reviewed articles and conference abstracts, and grey literature published from 1 January 1999 to 5 July 2019 in English, French, Hindi or Spanish and reporting original quantitative and/or qualitative data on the health of trans men or transmasculine persons living in LMIC. Studies were excluded if they did not disaggregate data for trans men or if they only described surgical techniques or laboratory values. RESULTS: We included 53 studies (42 peer-reviewed and 11 grey literature) from 19 LMIC. Most were conducted in higher-middle-income countries (n=12) and in Latin America (n=16, 30.2%), the Middle East (n=14, 26.4%) or Sub-Saharan Africa (n=12, 22.6%) and published in 2014 or later (n=44, 83.0%). Approximately half of studies used quantitative methods (52.8%, n=28), of which 64.3% (n=18) had fewer than 50 participants and 14.2% (n=4) had over 150. Across study designs, social determinants of health and gender-affirming care were the most commonly represented domains (49.1% and 47.1% of studies respectively), with common themes including gender-based violence, coercion and discrimination as well as unprescribed hormone use. Other domains represented included mental health (32.1%), sexual and reproductive health (24.5%), general healthcare access (18.9%), physical health (9.4%) and substance use (9.4%). CONCLUSION: Greater inclusion and disaggregation of trans men and transmasculine persons in global health research is needed to support sex- and gender-based analyses of trans health. Community-based research approaches and theoretically driven research may help to increase the relevance and rigour of such research. Funders should invest in research on trans men's health in LMIC.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".