S8– Adopting a realist review approach to conceptualizing the relationship between the perceived characteristics of clinical practice guidelines and their uptake
Bibliographic record
Abstract
Evidence generation and synthesis Other evidence generation and synthesis Although the concept of implementability (defined as "a set of characteristics that predict the relative ease of guideline implementation") has been operationalized in the Guideline Implementability Appraisal (GLIA) tool, the relationship between the perceived characteristics of clinical practice guidelines (CPGs) and their uptake in practice is not clearly understood. Synthesizing the literature in this area, which spans disciplines and terminologies, is difficult using traditional systematic review methods. To explore the relationship between the perceived characteristics of CPGs and their use, we drew from the realist review approach pioneered by Ray Pawson. This unique approach incorporated qualitative methods such as purposive, snowball, and opportunistic sampling, as well as some of the search methods of traditional systematic reviews. The modified realist review enabled the examination of relevant fields, literature, and theories and facilitated collaboration with experts to clarify the relationship between CPG characteristics and guideline uptake. The preliminary set of seven guideline dimensions drawn from this review–actionable, clear, complex, evidence-based, feasible, flexible, specific–were conceptualized as a series of trade-offs, as the presence of one or more characteristics can differentially impact the presence of another and can be differentially valued by guideline developers and guideline users. Realist review-informed synthesis is an effective method for reviewing complex and under-theorized topics. The preliminary set of dimensions (and their trade-offs) demonstrates that steps to improving guideline uptake may require the facilitation of dialogue among guideline developers and users. The inclusion of end-users in this dialogue is primary, as we argue that uptake is best judged by those who use CPGs. Ongoing work involves validating these dimensions and developing a tool to negotiate these trade-offs.
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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 | 0.305 | 0.430 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".