The development of a guideline implementability tool (GUIDE-IT): a qualitative study of family physician perspectives
Bibliographic record
Abstract
BACKGROUND: The potential of clinical practice guidelines has not been realized due to inconsistent adoption in clinical practice. Optimising intrinsic characteristics of guidelines (e.g., its wording and format) that are associated with uptake (as perceived by their end users) may have potential. Using findings from a realist review on guideline uptake and consultation with experts in guideline development, we designed a conceptual version of a future tool called Guideline Implementability Tool (GUIDE-IT). The tool will aim to involve family physicians in the guideline development process by providing a process to assess draft guideline recommendations. This feedback will then be given back to developers to consider when finalizing the recommendations. As guideline characteristics are best assessed by end-users, the objectives of the current study were to explore how family physicians perceive guideline implementability, and to determine what components should comprise the final GUIDE-IT prototype. METHODS: We conducted a qualitative study with family physicians in Toronto, Ontario. Two experienced investigators conducted one-hour interviews with family physicians using a semi-structured interview guide to 1) elicit feedback on perceptions on guideline implementability; 2) to generate a discussion in response to three draft recommendations; and 3) to provide feedback on the conceptual GUIDE-IT. Sessions were audio taped and transcribed verbatim. Data collection and analysis were guided by content analyses. RESULTS: 20 family physicians participated. They perceived guideline uptake according to facilitators and barriers across 6 categories of guideline implementability (format, content, language, usability, development, and the practice environment). Participants' feedback on 3 draft guideline recommendations were grouped according to guideline perception, cognition, and agreement. When asked to comment on GUIDE-IT, most respondents believed that the tool would be useful, but urged to involve "regular" or community family physicians in the process, and suggested that an online system would be the most efficient way to deliver it. CONCLUSIONS: Our study identified facilitators and barriers of guideline implementability from the perspective of community and academic family physicians that will be used to build our GUIDE-IT prototype. Our findings build on current knowledge by showing that family physicians perceive guideline uptake mostly according to factors that are in the control of guideline developers.
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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 | 0.028 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".