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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".