265WS Improving Guideline Implementability With Guide-M (Guideline Implementability For Decision Excellence Model): An Interactive Workshop
Bibliographic record
Abstract
Background We developed a framework of guideline uptake called GuIDE-M (Guideline Implementability for Decision Excellence-Model) based on an extensive literature review. It describes four domains covering guideline content to optimise the implementability of recommendations (Stakeholder development, Evidence synthesis, Considered Judgement and Feasibility) and two domains related to communication of content (Language and Format). Objectives/Goal (1) To learn about GuIDE-M, (2) To conduct an assessment of participants’ current use of the GuIDE-M domains in guideline development or assessment and (3) To determine priorities for tool development to operationalize GuIDE-M domains. Target Group, Suggested Audience Guideline developers, guideline users and researchers. [5] Description of the Workshop and Methods used to Facilitate Interactions (1) Introduction (15 minutes). A brief foundational overview of GuIDE-M. (2) Facilitated Assessment (60 minutes). Participants will break into small groups to discuss one or more of the domains in GuIDE-M. There they will (a) conduct a more detailed review of the domain, (b) assess the extent to which their guideline-related activities align with GuIDE-M principles, (c) reflect on the extent to which improving in the area is a priority, (d) discuss methods and available tools to operationalize the domain concepts, and (e) explore the types of tool(s) that should be developed to incorporate domain concepts into guideline development. Participants will be invited to remain involved as evaluators, pilot-testers and developers of these tools. The facilitated assessment will happen twice (2 x 30 minutes) to allow participants to focus on two of the GuIDE-M domains. (3) Wrap-Up (15 minutes).
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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 | 0.055 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".