Understanding the relationship between the perceived characteristics of clinical practice guidelines and their uptake: protocol for a realist review
Bibliographic record
Abstract
BACKGROUND: Clinical practice guidelines have the potential to facilitate the implementation of evidence into practice, support clinical decision making, specify beneficial therapeutic approaches, and influence public policy. However, these potential benefits have not been consistently achieved. The limited impact of guidelines can be attributed to organisational constraints, the complexity of the guidelines, and the lack of usability testing or end-user involvement in their development. Implementability has been referred to as the perceived characteristics of guidelines that predict the relative ease of their implementation at the clinical level, but this concept is as yet poorly defined. The objective of our study is to identify guideline attributes that affect uptake in practice by considering evidence from four disciplines (medicine, psychology, management, human factors engineering) to determine the relationship between the perceived characteristics of recommendations and their uptake and to develop a framework of implementability. METHODS: A realist-review approach to knowledge synthesis will be used to understand attributes of guidelines (e.g., its text and content) and how changing these elements might impact clinical practice and clinical decision making. It also allows for the exploration of 'what works for whom, in what circumstances, and in what respects'. The realist review will be structured according to Pawson's five practical steps in realist reviews: (1) clarifying the scope of the review, (2) determining the search strategy, (3) ensuring proper article selection and study quality assessment, (4) extracting and organising data, and (5) synthesising the evidence and drawing conclusions. Data will be synthesised according to a two-stage analysis: (1) we will extract and define all relevant guideline attributes from the different disciplines, then create a shortlist of unique attributes and investigate their relationships with uptake, and (2) we will compare and contrast the attributes and guideline uptake within each and between the four disciplines to create a robust framework of implementability. DISCUSSION: Creating guidelines that are designed to maximise uptake may be a potentially effective and inexpensive way of increasing their impact. However, this is best achieved by a comprehensive framework to inform the design of guidelines drawing on a range of disciplines that study behaviour change. This study will use a customised realist-review approach to synthesising the literature to better understand and operationalise a complex and under-theorised concept.
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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 | 0.169 | 0.268 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.068 | 0.017 |
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".