Paper 3: a systematic review of definitions for “racial health equity” and related terms within health-related articles
Bibliographic record
Abstract
OBJECTIVES: To systematically evaluate definitions of "racial health equity" (RHE) and related terms within health-related academic literature. STUDY DESIGN AND SETTING: We systematically evaluated definitions of RHE and related terms within health-related academic articles. Articles published in English were included, and no date restrictions were imposed. RESULTS: We found 20 original articles containing relevant definitions out of 1816 retrieved articles, thirteen of which were published from 2020 to 2023. Themes used in the definitions varied; racism (n = 12) and quality of healthcare (n = 10) were the most common. Additional themes, including social hierarchy or marginalization, discrimination, justice, unmet social needs, and historical events were described within some definitions. Eleven of the included manuscripts defined race as a social construct. CONCLUSION: This study depicts RHE as an emerging concept with limited consensus on racism, quality of health, and social determinants of health as important underlying frameworks. To center equity efforts and actions under a workable and shared vision, we recommend continued discussions regarding underlying meanings of RHE concepts and propose establishing a definition that promotes unity across health fields and prevents ambiguity.
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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 | 0.043 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.035 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".