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 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 | 0.148 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".