The development, evolution, and maintenance of structural racism for the study of health inequities: An expanded framework for Asian, Black, Hispanic, Indigenous, and White Americans
Bibliographic record
Abstract
Evaluating the relationship between structural racism and health inequity is conceptually and empirically complex. This critical review of policies and events extends a previously published framework for understanding structural racism in health research across ethnoracial groups from 1400 to present. We apply this framework for Asian, Black or African American, Hispanic/Latinx, Native Hawaiian/Pacific Islander, and White groups and reflect on, compare, and contrast the overarching patterns within and across groups. Our findings illustrate the utility of our framework as a tool for conceptualizing and operationalizing structural racism in future health research. We suggest that health scholars can advance the field by: (1) recognizing multiple, reinforcing domains of structural racism; (2) expanding research beyond a Black-White binary to include other ethnoracial groups; (3) emphasizing the role of time and its different manifestations as exposure across the life course and cohorts; (4) highlighting the implications of collective resistance and agency as alternatives to deficit models; and (5) disaggregating data, whenever possible, to avoid rendering smaller ethnoracial groups invisible.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".