Guideline uptake is influenced by six implementability domains for creating and communicating guidelines: a realist review
Bibliographic record
Abstract
OBJECTIVES: To identify factors associated with the implementability of clinical practice guidelines (CPGs) and to determine what characteristics improve their uptake. STUDY DESIGN AND SETTING: We conducted a realist review, which involved searching multiple sources (eg, databases, experts) to determine what about guideline implementability works, for whom, and under what circumstances. Two sets of reviewers independently screened abstracts and extracted data from 278 included studies. Analysis involved the development of a codebook of definitions, validation of data, and development of hierarchical narratives to explain guideline implementability. RESULTS: We found that guideline implementability is associated with two broad goals in guideline development: (1) creation of guideline content, which involves addressing the domains of stakeholder involvement in CPGs, evidence synthesis, considered judgment (eg, clinical applicability), and implementation feasibility and (2) the effective communication of this content, which involves domains related to fine-tuning the CPG's message (using simple, clear, and persuasive language) and format. CONCLUSION: Our work represents a comprehensive and interdisciplinary effort toward better understanding, which attributes of guidelines have the potential to improve uptake in clinical practice. We also created codebooks and narratives of key concepts, which can be used to create tools for developing better guidelines to promote better care.
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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 | 0.046 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".