A Summary of Guidance on Addressing Racial and Ethnic Health Equity in Systematic Reviews and Evidence-Based Guidelines
Bibliographic record
Abstract
Racial and ethnic health equity is the absence of unfair and avoidable or remediable differences in health and well-being among persons belonging to different racial and ethnic groups. This article summarizes current guidance and identifies practices for systematic reviewers and guideline groups to develop clinical practice guidelines that mitigate such inequities. Current guidance recommends that systematic reviews and clinical practice guidelines ensure a wider perspective; identify, prioritize, and develop equity-focused topics and questions; and apply specific methods and processes to answer equity-focused questions. Ensuring a wider perspective involves incorporating persons with lived experiences and other relevant nonclinical expertise into review and guideline teams as well as engagement of patients and members of affected populations in the review and guideline process. Examples for identifying and developing equity-focused topics and questions include using health equity as a criterion to select and prioritize topics, developing topics specific to mitigating racial and ethnic health inequities, and addressing upstream drivers of inequities and implementation considerations. Appropriate methods and processes might include considering different types of study designs, selecting the type of review accordingly, and using suitable evidentiary frameworks and thresholds to answer a broader set of equity-relevant questions. Several review, health technology assessment, guideline, and other health care decision-maker groups are implementing guidance to address racial and ethnic 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 | 0.063 | 0.261 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.014 |
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".