Enhancing health equity considerations in guidelines: health equity extension of the GIN-McMaster Guideline Development Checklist
Bibliographic record
Abstract
Background: Practice guidelines may reduce health inequities by addressing preventable and unjust differences in health. However, health equity considerations are often inadequately integrated into the guideline planning and development process. This article describes a pragmatic approach to enhancing health equity considerations within guidelines by introducing an extension to the GIN-McMaster Guideline Development Checklist (GDC). Methods: We reviewed the latest guidance on enhancing health equity considerations in guideline development to draft the checklist and deployed a global online survey from March 27th, 2024, to May 13th, 2024 to gather consensus. We conducted a methodological review of guideline development handbooks to identify best practices in health equity considerations. An advisory board comprised of diverse interest-holders informed the development of the checklist. We made revisions based on the survey feedback and review findings. Findings: We present 21 extension items spanning 16 of the 18 guideline development topics from the GIN-McMaster GDC. Key additions include planning for engagement with individuals experiencing inequities in guideline development activities, applying an equity lens, and considering health equity in recommendation formulation, dissemination and implementation strategies. This checklist gives value to lived experiences to enrich health equity assessments, complementing empirical evidence to inform guideline recommendations. Guideline developers should assess guideline sensitivity to health equity to determine resource prioritization for optimal implementation of the extension items. Interpretation: The GIN-McMaster health equity extension provides guidance for the streamlined integration of health equity considerations throughout the guideline development process. Using this tool alongside the original GIN-McMaster GDC may lead to more equitable and impactful guidelines. Funding: This project was partially funded by Public Health Agency of Canada. The funder was not involved in the conceptualization or design or the conduct of the project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".