Associations Between Structural Stigma and Allostatic Load Among Sexual Minorities: Results From a Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Structural forms of stigma and discrimination are associated with adverse health outcomes across numerous stigmatized groups, including lesbian, gay, and bisexual (LGB) individuals. However, the biological consequences of structural stigma among LGB populations are understudied. To begin to address this gap, we assessed associations between indicators of structural stigma (i.e., state-level policies) targeting LGB individuals and allostatic load (AL) indices representing physiological dysregulations. METHODS: Pooled data from the continuous 2001-2014 National Health and Nutritional Examination Survey were analyzed (LGB: n = 864; heterosexual: n = 20,310). Ten state-level LGB-related policies (e.g., employment nondiscrimination protections, same-sex marriage) were used to operationalize structural stigma. A sex-specific AL index representing 11 immune, metabolic, and cardiovascular biomarkers was estimated. Multilevel models were used to examine associations between structural stigma and AL, net of nine individual-level characteristics (e.g., education, race/ethnicity, age, and health behaviors). RESULTS: Sexual minority men living in states with low levels of structural stigma experienced significantly lower AL ( β = -0.45, p = .02) compared with sexual minority men living in states with high structural stigma (i.e., fewer protective policies). There was no significant association between structural stigma and AL among sexual minority women. CONCLUSIONS: By demonstrating direct associations between structural stigma and indices of physiological dysregulation, our findings provide a mechanistic understanding of how the social environment can "get under the skin and skull" for sexual minority men in the United States. Future research should explore whether these mechanisms generalize to other marginalized groups exposed to structural stigma.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".