GRADE equity guidelines 1: considering health equity in GRADE guideline development: introduction and rationale
Bibliographic record
Abstract
OBJECTIVES: This article introduces the rationale and methods for explicitly considering health equity in the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology for development of clinical, public health, and health system guidelines. STUDY DESIGN AND SETTING: We searched for guideline methodology articles, conceptual articles about health equity, and examples of guidelines that considered health equity explicitly. We held three meetings with GRADE Working Group members and invited comments from the GRADE Working Group listserve. RESULTS: We developed three articles on incorporating equity considerations into the overall approach to guideline development, rating certainty, and assembling the evidence base and evidence to decision and/or recommendation. CONCLUSION: Clinical and public health guidelines have a role to play in promoting health equity by explicitly considering equity in the process of guideline development.
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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 | 0.236 | 0.631 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".