Perceived access to gender-affirming care, completion of gender-affirming medical interventions, and psychological distress among transgender women of color: the TURNNT cohort study
Bibliographic record
Abstract
Transgender women of color (TWOC) are disproportionately impacted by psychological distress. Though gender-affirming care (GAC) has been recommended to alleviate this distress, research examining associations between perceived access to GAC, specific gender-affirming medical interventions (GAMIs), and mental health among TWOC in the United States remains limited. In this study, we examine cross-sectional and longitudinal associations between perceived access to GAC, completion of specific GAMIs, and psychological distress among TWOC, using modified Poisson regression and multilevel linear modeling. Data came from the Trying to Understand Relationships, Networks and Neighborhoods Among Transgender Women of Color (TURNNT) Cohort Study. In multivariable analyses, increased access to GAC was associated with reduced psychological distress risk. All assessed GAMIs suggested protective effects against psychological distress (aRR < 1), with statistical significance found for breast augmentation and facial feminization surgery. On average, those with unmet GAMI needs experienced higher distress risk than those without. Longitudinally, those experiencing reduced access to GAC over 6 months faced the highest distress risk among all trajectory groups (aRR: 1.40, 95% CI, 1.08-1.82). Our findings support the need for further inquiry in this area and suggest that policies protecting and increasing access to GAC may improve mental health among TWOC. This article is part of a Special Collection on Methods in Social Epidemiology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".