Intersectional stigma, health behaviors, and allostatic load among sexual and gender diverse people
Bibliographic record
Abstract
Sexual and gender diverse (SGD) people face significant health disparities linked to chronic stigma exposure. Yet, the biological mechanisms underlying these inequities remain unexplored. This project examines how lifetime intersectional discrimination influences allostatic load (AL) —the cumulative biological 'wear and tear' of chronic stress—and explores the role of health behaviors (smoking, alcohol use, drug use, sleep quality and physical activity) as potential mediators in this pathway. Our team recruited 357 adults separated into 7 subgroups stratified by gender identity and sexual orientation. Blood samples and biometrics were collected from each participant and analysed to create a composite AL index incorporating 16 biomarkers across multiple physiological systems. Psychosocial variables including intersectional discrimination experiences and health behaviors were derived from validated questionnaires. Results show that intersectional discrimination experiences were positively associated with AL after controlling for age, indicating that both major discriminatory events and cumulative daily experiences independently contribute to physiological dysregulation through enacted stigma and discrimination exposure. Additionally, both gender identity and sexual orientation relate to differential AL patterns, with masculine-spectrum people (cisgender and transgender men) and sexual minority men (bisexual and gay men) showing the highest AL levels. Contrary to our hypothesis, health behaviors did not mediate the relationship between discrimination and AL, suggesting that discrimination may exert direct biological effects through stress response systems without requiring negative behavioral pathways. These findings provide further support for recognition of the impacts of structural and social determinants of health among SGD communities and the need for policy changes that protect against structural inequities. • Transgender and cisgender men show the highest allostatic load levels; cisgender women show the lowest • Bisexual and gay men demonstrate the highest allostatic load levels among all sexual orientation groups • Both major lifetime discrimination and day-to-day lifetime discrimination independently predict allostatic load levels • Health behaviors do not mediate the major discrimination-allostatic load associations in this sample • Age emerges as the strongest predictor of physiological stress burden across groups
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".