How to adapt existing evidence-based clinical practice guidelines: a case example with smoking cessation guidelines in Canada
Bibliographic record
Abstract
OBJECTIVE: To develop and encourage the adoption of clinical practice guidelines (CPGs) for smoking cessation in Canada by engaging stakeholders in the adaptation of existing high-quality CPGs using principles of the ADAPTE framework. METHODS: An independent expert body in guideline review conducted a review and identified six existing CPGs, which met a priori criteria for quality and potential applicability to the local context. Summary statements were extracted and assigned a grade of recommendation and level of evidence by a second expert panel. Regional knowledge exchange brokers recruited additional stakeholders to build a multidisciplinary network of over 800 clinicians, researchers and decision-makers from across Canada. This interprofessional network and other stakeholders were offered various opportunities to provide input on the guideline both online and in person. We actively encouraged end-user input into the development and adaptation of the guidelines to ensure applicability to various practice settings and to promote adoption. RESULTS: The final guideline contained 24 summary statements along with supporting clinical considerations, across six topic area sections. The guideline was adopted by various provincial/territorial and national government and non-governmental organisations. CONCLUSIONS: This method can be applied in other jurisdictions to adapt existing high-quality smoking cessation CPGs to the local context and to facilitate subsequent adoption by various stakeholders.
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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 | 0.052 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| 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".