Addressing Racial and Ethnic Health Equity in Systematic Reviews and Evidence-Based Guidelines: Overview and Background for the Series
Bibliographic record
Abstract
Systematic reviews and other evidence synthesis products support clinical practice guidelines, policy and coverage decisions, and future research directions. These products can help promote health equity by examining why differences in outcomes exist, how underrepresentation or overrepresentation in the evidence affects generalizability, and how to address underlying societal sources of disparities. This article provides an overview of and background for a series of articles sponsored by the Agency for Healthcare Research and Quality and the Robert Wood Johnson Foundation. The series focuses on racial and ethnic health equity as one approach to enhance the utility of systematic reviews in addressing inequities. Together, the articles in the series address what end users of systematic reviews, specifically guideline developers, have done thus far; how best to methodologically address racial health equity; and what steps to take next.
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 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 | 0.040 | 0.148 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".