Evaluating regional and temporal variations in quality indicators for opioid initiation and pharmacist medication reviews in Ontario: A population-based repeated cross-sectional study
Bibliographic record
Abstract
Background: Quality indicators (QIs) are measures used to evaluate quality of services but are often underused in pharmacy practice. This study examines trends in 2 established QIs in community pharmacy. Methods: We conducted a repeated cross-sectional study in Ontario using administrative data collected between 2013 and 2023. We measured 2 QIs designed for pharmacy practice annually: (1) percentage of newly dispensed opioid prescriptions exceeding 50 morphine milligram equivalents (MME), and (2) percentage of eligible patients receiving pharmacist medication reviews within 7 days of hospital discharge. Regional differences were summarized using variance calculations, while temporal trends were analyzed using Mann-Kendall tests. Results: The opioid indicator demonstrated a consistent decline in the percentage of newly dispensed opioid prescriptions exceeding 50 MME across Ontario, with an absolute reduction of 10.5% from 2013 (25.6%) to 2023 (15.1%). High-dose opioid initiation ranged from 12.8% (Central) to 16.7% (West) in 2023 (range 3.9%, variance 2.3%). Significant time trends were found for all regions, with the largest reductions observed in urban regions. For the medication review indicator, provincial trends declined by 7.6%, from 16.7% in 2013 to 9.1% in 2017, followed by a modest recovery to 12.5% by 2023. Regionally, rates of medication reviews varied, with rural areas maintaining higher uptake rates compared with urban centres. Rates ranged from 7.8% (Toronto) to 16.2% (North) in 2023 (range 8.4%, variance 10.0%). A significant time trend was found only in Eastern Ontario. Conclusion: Significant declines in high-dose opioid initiation but inconsistent uptake of reviews across regions indicate opportunities for improvement in pharmacy practice.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".