An Ecological Approach to Reducing Potentially Inappropriate Medication Use: Canadian Deprescribing Network
Bibliographic record
Abstract
Polypharmacy is growing in Canada, along with adverse drug events and drug-related costs. Part of the solution may be deprescribing, the planned and supervised process of dose reduction or stopping of medications that may be causing harm or are no longer providing benefit. Deprescribing can be a complex process, involving the intersection of patients, health care providers, and organizational and policy factors serving as enablers or barriers. This article describes the justification, theoretical foundation, and process for developing a Canadian Deprescribing Network (CaDeN), a network of individuals, organizations, and decision-makers committed to promoting the appropriate use of medications and non-pharmacological approaches to care, especially among older people in Canada. CaDeN will deploy multiple levels of action across multiple stakeholder groups simultaneously in an ecological approach to health system change. CaDeN proposes a unique model that might be applied both in national settings and for different transformational challenges in health care.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".