072 Validation of the Guideline Implementability for Decision Excellence Model (Guide-M)
Bibliographic record
Abstract
Background We developed a Guideline Implementability for Decision Excellence-Framework Model (GuIDE-M) based on the robust evidentiary base of a realist review on guideline attributes. GuIDE-M emerged as a conceptual representation of factors to facilitate the development of more implementable guidelines. Objectives Validity assessment of GuIDE-M with international guideline developers. Methods We assessed GuIDE-M using a stepwise validation process: Stage 1 involved consultation with a multi-disciplinary group of Canadian experts (including guideline research, psychology, management, and human factors engineering) to assess the sense and structure of the conceptual GuIDE-M. In Stage 2, 200 international guideline developers will assess GuIDE-M using an innovative online assessment platform, which includes an interactive video-based system to enable objective assessment of the model and its components. Results In Stage 1, consultation with 10 multi-disciplinary experts informed major structural changes (e.g., addition of a 6th domain) and minor sense changes (e.g., collapsing like attributes) to the model. Stage 2 (in progress) will assess the organisation of the model according to Stage 1 findings: 1) To consider Stakeholder involvement, Evidence synthesis, Considered judgement and Feasibility in the development of guidelines; 2) and to communicate this content using effective Language and Format. Discussion We are applying an innovative stepwise process to rigorously validate and refine GuIDE-M internationally. Implications for Guideline Developers/Users This study represents a novel contribution to guideline developers and will offer a comprehensive, validated model that considers an exhaustive set of evidence-based factors to facilitate guideline uptake.
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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 | 0.058 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads 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".