Recommendations to improve race identification in health records: A rapid scoping review
Bibliographic record
Abstract
Race is a critical variable in understanding health disparities, yet health databases lack consistent practices for identifying race. This rapid scoping review aimed to examine existing recommendations for identifying race in health databases and highlight gaps in current literature to guide future research. Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, searches were conducted in MEDLINE, Embase, and Scopus for relevant literature published between January 2019 and February 2025. Articles were included if they addressed race identification in health databases, were available in English, had full-text access, and were peer-reviewed, knowledge syntheses, or grey literature. All articles were double screened in Covidence, and twenty-one articles were included. Descriptive thematic analysis identified five recommendation categories, including, self-identification and patient-centered practice, standardization across healthcare systems, data quality and completeness, algorithmic and predictive methods, and disaggregated data use and cross sector collaboration. There were common findings on the value of self-identification, cross-system consistency, and tools like natural language processing and imputation models. Some articles emphasized combining multiple strategies to improve system-wide practices, and overall, minimal conflicting evidence was observed. However, gaps remain in operationalizing these recommendations across various healthcare settings. Future directions should prioritize implementation-focused research and cross-jurisdictional comparisons to inform scalable, equity-driven improvements in race data practices. Ultimately, improving the consistency and accuracy of race data will enhance health equity monitoring, guide equitable resource distribution, and inform policies that better reflect the needs of racialized populations.
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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 | 0.331 | 0.603 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.051 | 0.036 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.019 | 0.045 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier 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".