DEVELOPMENT AND IMPLEMENTATION OF DEPRESCRIBING GUIDELINES
Bibliographic record
Abstract
Class-specific deprescribing guidelines are a potential solution to address polypharmacy. This study aimed to understand factors associated with successful deprescribing guideline implementation and whether self-efficacy for deprescribing was affected. Deprescribing guidelines were developed using AGREE-II (Appraisal of Guidelines for Research and Evaluation), and GRADE (Grading of Recommendations Assessment, Development, and Evaluation) to rate certainty of evidence and recommendation strength. Decision-support algorithms were developed based on guideline content. Developmental evaluation was performed using ethnographic methods: observations and interviews with site implementation teams (three Family Health Teams and three Long-Term Care sites in Ottawa, Canada). Self-efficacy surveys were completed. Three evidence-based deprescribing guidelines were developed (proton pump inhibitors, benzodiazepine receptor agonists and antipsychotics). Implementation was supported by incorporation of algorithms into pharmacist/physician medication reviews. Practice site priorities and processes shaped ability to incorporate recommendations; aligning guidelines with existing processes is critical for implementation. Self-efficacy increased among 9 consistent respondents across all guidelines.
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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 | 0.024 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".