A framework of the desirable features of guideline implementation tools (GItools): Delphi survey and assessment of GItools
Bibliographic record
Abstract
BACKGROUND: Guidelines are the foundation for healthcare planning, delivery and quality improvement but are not consistently implemented. Few guidelines are accompanied by guideline implementation tools (GItools). Users have requested GItools, and developers have requested guidance on how to develop GItools. First it is necessary to characterize GItools. The purpose of this research was to generate a framework of desirable features of GItools. METHODS: Items representing desirable GItool features were generated by a cross-sectional survey of the international guideline community. Items were confirmed by 31 guideline developers, implementers and researchers in a two-round Delphi survey administered on the Internet. The resulting GItool framework was applied with a sample of GItools accompanying guidelines identified in the National Guideline Clearinghouse. RESULTS: The cross-sectional survey was completed by 96 respondents from Australia, Canada, the United Kingdom, the United States, The Netherlands, and various other countries. Seven of nine items were rated by the majority as desirable. A total of 31 panelists from 10 countries including Australia, Canada, Germany, New Zealand, Peru, Saudi Arabia, Spain, the United Kingdom, and the United States took part in a two-round Delphi survey. Ten items achieved consensus as desirable GItool features in round #1, and two additional items in round #2. A total of 13 GItools for Resource Planning, Implementation and Evaluation were identified among 149 guidelines on a variety of clinical topics (8.7%). Many GItools did not possess features considered desirable. CONCLUSIONS: Inclusion of higher quality GItools in guidelines is needed to support user adoption of guidelines. The GItool framework can serve as the basis for evaluating and adapting existing GItools, or developing new GItools. Further research is needed to validate the framework, develop and implement instruments by which developers can apply the framework, and specify which guidelines should be accompanied by GItools.
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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 | 0.248 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| 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; 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".