How equity is addressed in clinical practice guidelines: a content analysis
Bibliographic record
Abstract
OBJECTIVES: Considering equity into guidelines presents methodological challenges. This study aims to qualitatively synthesise the methods for incorporating equity in clinical practice guidelines (CPGs). SETTING: Content analysis of methodological publications. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Methodological publications were included if they provided checklists/frameworks on when, how and to what extent equity should be incorporated in CPGs. DATA SOURCES: We electronically searched MEDLINE, retrieved references, and browsed guideline development organisation websites from inception to January 2013. After study selection by two authors, general characteristics and checklists items/framework components from included studies were extracted. Based on the questions or items from checklists/frameworks (unit of analysis), content analysis was conducted to identify themes and questions/items were grouped into these themes. PRIMARY OUTCOMES: The primary outcomes were methodological themes and processes on how to address equity issues in guideline development. RESULTS: 8 studies with 10 publications were included from 3405 citations. In total, a list of 87 questions/items was generated from 17 checklists/frameworks. After content analysis, questions were grouped into eight themes ('scoping questions', 'searching relevant evidence', 'appraising evidence and recommendations', 'formulating recommendations', 'monitoring implementation', 'providing a flow chart to include equity in CPGs', and 'others: reporting of guidelines and comments from stakeholders' for CPG developers and 'assessing the quality of CPGs' for CPG users). Four included studies covered more than five of these themes. We also summarised the process of guideline development based on the themes mentioned above. CONCLUSIONS: For disadvantaged population-specific CPGs, eight important methodological issues identified in this review should be considered when including equity in CPGs under the guidance of a scientific guideline development manual.
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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 | 0.170 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.029 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".