Health equity considerations in guideline development: a rapid scoping review
Bibliographic record
Abstract
BACKGROUND: Systematic guidance for considering health equity in guidelines is lacking. This scoping review aims to synthesize current best practices for integrating health equity into guideline development and the benefits or drawbacks of these practices. METHODS: We searched Ovid MEDLINE ALL and Embase Classic+Embase on the Ovid platform, CINAHL on EBSCO, and Web of Science (Core Collection) from 2010 to 2022. We searched grey literature from 2015 to 2022, using the Canadian Agency for Drugs and Technologies in Health Grey Matters checklist and searches of potentially relevant websites. Articles were screened independently by 1 reviewer. Proposed best practices, advantages and disadvantages, and tools were extracted independently by 1 reviewer and qualitatively synthesized based on the relevant steps of a comprehensive checklist covering the stages of guideline development. RESULTS: We included 26 articles that proposed best practices for incorporating health equity within the guideline development process. These practices were organized under different stages of the development process, including guideline planning, evidence review, guideline development and dissemination. Included studies provided best practices from guideline producers, articles discussing health equity in current guidelines, articles addressing strategies to increase equity in the guideline implementation process, and literature reviews of promising health equity practices. INTERPRETATION: Our scoping review identified best practices to incorporate health equity considerations at each phase of guideline development. Identified practices may be used to inform equity-promoting strategies with the guideline development process; however, guideline producers should carefully consider the advantages and disadvantages of best practices when integrating 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 | 0.385 | 0.661 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.053 | 0.040 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".