Multiple Minority Stress and Behavioral Health Among Young Black and Latino Sexual Minority Men
Bibliographic record
Abstract
Purpose: Young Black and Latino sexual minority men may experience multiple minority stressors, which may negatively impact behavioral health. To investigate this, longitudinal associations between multiple minority stressors and behavioral health outcomes were examined over a 2-year period. Methods: Data were from the Healthy Young Men's Cohort Study. The present study used five waves of data collected every 6 months from 2016 to 2019. Participants were young Black and/or Latino sexual minority men in Los Angeles, California ( n = 448; aged 16–24). Participants self-reported their experiences of racial and homonegative discrimination, internalized homonegativity, alcohol use, and depressive symptoms. Latent growth curve models with time-varying covariates examined contemporaneous associations between minority stressors as well as general life stress and behavioral health measures. Results: After accounting for general life stress and demographic characteristics, racial discrimination was persistently associated with unhealthy alcohol use. Internalized homonegativity was associated with unhealthy alcohol use initially, but this association became non-significant over time. In models predicting depressive symptoms, racial discrimination was a significant predictor at early waves, and homonegative discrimination and internalized homonegativity emerged as significant predictors at later waves. Conclusion: These results help clarify which multiple minority stressors are more prominent in their relationship to young Black and Latino sexual minority men's unhealthy alcohol use and depressive symptoms. Interventions targeting multiple minority stressors may be needed at different times during young adulthood.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".