Identifying the social mechanisms for multiracial-monoracial health disparities
Bibliographic record
Abstract
The United States is witnessing rapid growth in multiracial populations, yet the social mechanisms producing health disparities between multiracial and monoracial groups remain poorly understood. Using the nationally representative Behavioral Risk Factor Surveillance System (2001–2012, N = 4,363,547), we examine mental and physical health outcomes through self-reported measures of poor mental and physical health days, systematically investigating four pathways potentially explaining multiracial-monoracial health disparities: 1) socioeconomic status, 2) early life adversity, 3) race-related experiences, and 4) health behaviors. Results based on negative binomial regressions and Karlson-Holm-Breen mediation tests reveal that Black multiracial, American Indian or Alaska Native multiracial, and Other multiracial individuals report worse mental and physical health despite higher socioeconomic status compared to their monoracial counterparts. Among Asian multiracial individuals, worse health outcomes compared to monoracial peers are partially attributed to socioeconomic factors and health behaviors. Across all multiracial groups, health disadvantages are largely explained by differences in early life social conditions, particularly exposure to family instability and adverse childhood experiences. Unexpectedly, race-related experiences show suppression rather than mediation effects, suggesting that accounting for discrimination actually increases observed health gaps. Our findings demonstrate how non-socioeconomic pathways, particularly early life adversity, play crucial roles in producing health disparities in an increasingly diverse society.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".