State-level heterogeneity in associations between structural stigma and individual health care access: A multilevel analysis of transgender adults in the United States
Bibliographic record
Abstract
OBJECTIVE: State-level variation in how restrictive policies affect health care access for transgender populations has not been widely studied. Therefore, we assessed the association between structural stigma and four measures of individual health care access among transgender people in the United States, and the extent to which structural stigma explains state-level variability. METHODS: Data were drawn from the 2015-2019 Behavioral Risk Factor Surveillance System and the Human Rights Campaign's State Equality Index. We calculated weighted proportions and conducted multilevel logistic regression of individual heterogeneity and discriminatory accuracy. RESULTS: An increase in the structural stigma score by one standard deviation was associated with lower odds of health care coverage (OR = 0.80; 95% CI: 0.66, 0.96) after adjusting for individual-level confounders. Approximately 11% of the total variance for insurance coverage was attributable to the state level; however, only 18% of state-level variability was explained by structural stigma. Adding Medicaid expansion attenuated the structural stigma-insurance association and explained 22% of state-level variation in health insurance. For the remaining outcomes (usual source of care, routine medical check-up, and cost-related barriers), we found neither meaningful associations nor considerable between-state variability. CONCLUSIONS: Our findings support the importance of Medicaid expansion and transgender-inclusive antidiscrimination protections to enhance health care insurance coverage. From a measurement perspective, however, additional research is needed to develop and validate measures of transgender-specific structural stigma to guide future policy interventions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".