The development of guideline implementation tools: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Research shows that guidelines featuring implementation tools (GItools) are more likely to be used than those without GItools, however few guidelines offer GItools and guidance on developing GItools is lacking. The objective of this study was to identify common processes and considerations for developing GItools. METHODS: Interviews were conducted with developers of 4 types of GItools (implementation, patient engagement, point-of-care decision-making and evaluation) accompanying guidelines on various topics created in 2008 or later identified in the National Guideline Clearinghouse. Participants were asked to describe the GItool development process and related considerations. A descriptive qualitative approach was used to collect and analyze data. RESULTS: Interviews were conducted with 26 GItool developers in 9 countries. Participants largely agreed on 11 broad steps, each with several tasks and considerations. Response variations identified issues lacking uniform approaches that may require further research including timing of GItool development relative to guideline development; decisions about GItool type, format and content; and whether and how to engage stakeholders. Although developers possessed few dedicated resources, they relied on partnerships to develop, implement and evaluate GItools. INTERPRETATION: GItool developers employed fairly uniform and rigorous processes for developing GItools. By supporting GItool development, the GItool methods identified here may improve guideline implementation and use.
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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 | 0.047 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".