Perspectives on Racial and Ethnic Health Equity in Systematic Reviews and Evidence-Based Guidelines
Bibliographic record
Abstract
Evidence synthesis and guideline groups have the potential to address health disparities. In June 2024, the Agency for Healthcare Research and Quality (AHRQ) and the Robert Wood Johnson Foundation (RWJF) cosponsored a summit to address racial and ethnic health equity in systematic reviews and other syntheses and guidelines, with support from Cochrane US. This article summarizes cross-cutting themes around future directions for systematic reviews and guidelines. Discussions addressed include the rationale for addressing racial health equity in systematic reviews and guidelines; representation of people with lived experience in systematic reviews and guidelines; approaches to developing and addressing equity-focused scope, including frameworks, methods, and thoughtful interpretation in systematic reviews; challenges and opportunities for guideline recommendations; need for standardized language and reporting for race and ethnicity in primary research studies, systematic reviews, and guidelines; and measures to track the progress of incorporating and addressing racial and ethnic health equity in systematic reviews and guidelines. Participants acknowledged that a one-size-fits-all approach was not possible or desired. Consensus priorities for next steps were to develop methods guidance to address equity in systematic reviews and guidelines; develop measures to track the progress of addressing racial and health equity in systematic reviews and guidelines; operationalize engaging representative interest holders in systematic reviews and guidelines; and share resources and learning for advancing health equity.
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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 | 0.728 | 0.781 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.009 | 0.061 |
| Scholarly communication | 0.038 | 0.060 |
| Open science | 0.014 | 0.041 |
| Research integrity | 0.041 | 0.071 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".