Healthcare avoidance due to anticipated discrimination among transgender people: A call to create trans-affirmative environments
Bibliographic record
Abstract
Transgender people encounter interpersonal and structural barriers to healthcare access that contribute to their postponement or avoidance of healthcare, which can lead to poor physical and mental health outcomes. Using the 2015 U.S. Transgender Survey, this study examined avoidance of healthcare due to anticipated discrimination among transgender adults aged 25 to 64 (N = 19,157). Multivariable logistic regression analysis was conducted to test whether gender identity/expression, socio-demographic, and transgender-specific factors were associated with healthcare avoidance. Almost one-quarter of the sample (22.8%) avoided healthcare due to anticipated discrimination. Transgender men had increased odds of healthcare avoidance (AOR = 1.32, 95% CI = 1.21–1.45) relative to transgender women. Living in poverty (AOR = 1.52, 95% CI = 1.40–1.65) and visual non-conformity (AOR = 1.48, 95% CI = 1.33–1.66) were significant risk factors. Having health insurance (AOR = 0.87, 95% CI = 0.79–0.96) and disclosure of transgender identity (AOR = 0.77, 95% CI = 0.68–0.87) were protective against healthcare avoidance. A significant interaction of gender identity/expression with health insurance was found; having health insurance moderated the association between gender identity/expression and healthcare avoidance. Providers should consider gender differences, socio-demographic, and transgender-specific factors to improve accessibility of services to transgender communities. A multi-level and multi-faceted approach should be used to create safe, trans-affirmative environments in health systems.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".