Comparing public policies impacting prescribing and medication management in primary care in two Canadian provinces
Bibliographic record
Abstract
The challenges of polypharmacy and inappropriate prescribing are recognized internationally. This study synthesizes and compares the policies related to these issues introduced in Canada's two most populous provinces - Ontario and Quebec - over the first two decades of the 21st century. Drawing on policy documents and consultations with experts, we found that while medication management to address polypharmacy and inappropriate prescribing has not been an explicit and consistent policy target in either province, some policy changes sought to directly or indirectly impact medication management. These changes include the introduction of primary care teams that include pharmacists, the introduction of a medication review performed by pharmacists (in Ontario), increased emphasis on quality improvement with some attention to potentially inappropriate medications (specifically opioids in Ontario), and investments in information technology to improve communication across providers and move toward electronic prescribing to improve medication safety and appropriateness. Despite growing evidence of the problem of polypharmacy and inappropriate prescribing, there has been limited policy attention targeting these problems directly, and policy changes with potential to improve prescribing and medication management may not have been fully realized. Further research to evaluate the impact of these changes on provider behaviours, and on patient outcomes, warrants attention.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".