WHAT POLICIES ARE IN USE ACROSS CANADA TO REDUCE INAPPROPRIATE MEDICATION USE IN OLDER ADULTS?
Bibliographic record
Abstract
Decision-making regarding the initiation and cessation of medications in older adults is primarily the responsibility of clinicians, however, this can be impacted by appropriate health policy. This study investigated policies across Canadian jurisdictions designed to discourage the use of inappropriate medications, and encourage deprescribing. A nation-wide questionnaire containing 10 open-ended questions, were distributed to the Canadian Pharmaceutical Directors Forum through the Ministries of Health in 2015. Two reviewers categorized responses and analyzed themes. Ten of 12 jurisdictions completed the questionnaires. Policies identified included de-listing specific medications, dose restriction, limited use/special authorization and incentives for reviewing medications or refusing to fill inappropriate prescriptions. 60% of jurisdictions coordinated collaboration across academic, health care, and policy sectors to provide additional services. Gaps noted included the potential for substitution of alternate harmful drug therapies. A range of strategies exist across Canada to reduce inappropriate medicines in older adults, with variable success.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".