Prescribing indicators: what can Canada learn from European countries?
Bibliographic record
Abstract
BACKGROUND: Drug therapy can improve patients' quality of life and health outcomes; however, underuse, overuse and inappropriate use of drugs can occur. Systematic examination of potential opportunities for improving prescribing and medication use is needed. OBJECTIVE: To convene a diverse group of stakeholders to learn about and discuss advantages and limitations of data sources, tools and methods related to drug prescribing indicators; foster methods to assess safe, appropriate and cost-effective prescribing; increase awareness of international organizations who develop and apply performance indicators relevant to Canadian researchers, practitioners and decision-makers; and provide opportunities to apply information to the Canadian context. METHODS: Approximately 50 stakeholders (health system decision-makers, senior and junior researchers, healthcare professionals, graduate students) met June 1-2, 2009 in Halifax, Canada. Four foundational presentations on evaluating quality of prescribing were followed by discussion in pre-assigned breakout groups of a prepared case (either antibiotic use or prescribing for seniors), followed by feedback presentations. RESULTS: Many European countries have procedures to develop indicators for prescribing and quality use of medicines. Indicators applied in diverse settings across the European Union use various mechanisms to improve quality, including financial incentives for prescribers. CONCLUSION: Further Canadian approaches to develop a system of Canadian prescribing indicators would enable federal/provincial/territorial and international comparisons, identify practice variations and highlight potential areas for improvement in prescribing, drug use and health outcomes across Canada. A more standardized system would facilitate cross-national research opportunities and enable Canada to examine how European countries use prescribing indicators, both within their country and across the European Union.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".