Access to transition-related health care among transmasculine people in India: A mixed-methods investigation
Bibliographic record
Abstract
Little research has examined the health care experiences of transmasculine people in India, where government initiatives to improve access to transition-related (also called gender-affirmative) care have recently been announced. We draw on data from 'Our Health Matters', a mixed-methods community-based participatory research project, to characterize the transition-related care experiences of transmasculine people in India. Peer researchers conducted 40 virtual qualitative interviews in Hindi or Marathi from July to September 2021. Between November 2022 and January 2023, 377 transmasculine people participated in a multi-mode survey available in five languages. Qualitative data were analysed with a combination of framework analysis and grounded theory techniques. Data were mixed using a convergent parallel approach. Transmasculine persons' care journeys began with information-seeking, relying on peers and internet searches. In choosing between the public and private healthcare systems, they weighed issues of quality and affordability: the public system was perceived as lower-quality and difficult to access but most could not afford private care, leading to delays in care. Indeed, unmet need was common; 36.4% of survey participants were planning but had not begun to receive transition-related care and 80.2% wanted at least one transition-related surgery. Although some participants encountered stigma and refusal of care when seeking hormones, survey participants reported largely positive experiences with their hormone prescribers, which may reflect the influence of peer referrals. Participants underwent psychological assessments prior to transition-related care, which some experienced as disempowering and a barrier to disclosing mental health challenges. Finally, participants who were able to access care reported improved well-being, although surgical dissatisfaction was not uncommon (26.2%). Trans-inclusive medical training and continuing education are critical to enhancing access to high-quality transition-related care. Transmasculine people generally relied on peers and grassroots organizations for information, system navigation, and financial assistance. Strengthening these existing community resources may improve access to care.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".