064 Identifying, Describing and Evaluating Guideline Implementability Tools
Bibliographic record
Abstract
Background Research shows that guidelines are more easily translated to practice when accompanied by information that helps users accommodate, implement and evaluate use of the recommendations. Guidelines vary in whether and how they offer such information, which we refer to as guideline implementability tools (GItools). Objectives To identify, describe and evaluate exemplar GItools that address Resource Implications, Implementation and Evaluation, and suggest how they could be improved. Methods GItools were identified in several sources (guidelines, Medline, professional organisation web sites, Implementation Science, Internet, expert referrals) and two individuals independently assessed each on criteria recommended by G-I-N members: purpose statement, instructions for use, citations for source of content, and how it was developed. Results The search produced 228 potential tools. Of these 94 were ineligible and 63 met no assessment criteria. Of the remaining 71 tools, 13 (18.3%), 24 (33.8%), 23 (32.4%) and 11 (15.5%) met 1, 2, 3 and 4 criteria, respectively; and 57 (80.3%), 37 (52.1%), 41 (57.7%) and 41 (57.7%) provided purpose, instructions, citations and development details, respectively. Most tools addressed Implementation (44, 62.0%). Twenty-eight (39.4%) were guideline-specific and 43 (60.6%) were generic. Discussion Few GItools met all assessment criteria. GItools could be more informative across all criteria. Few GItools were available to help users assess resource needs or evaluate guideline use. Many GItools were applicable to a variety of guidelines. Implications We identified a number of ways to improve GItools. Collaborative development and sharing of both generic and condition-specific GItools could make efficient use of resources.
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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 | 0.099 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".