Uptake of Quality-Related Event Standards of Practice by Community Pharmacies
Bibliographic record
Abstract
Quality-related events (QREs), including medication errors and near misses, are an inevitable part of community pharmacy practice. As QREs have significant implications for patient safety, pharmacy regulatory authorities across North America are increasing their expectations regarding QRE reporting and learning. Such expectations, commonly encapsulated as standards of practice (SoP), vary greatly between pharmacy jurisdictions and may range from the simple requirement to document QREs occurring within the pharmacy, all the way to requiring that quality improvement plans have been put in place. This research explores the uptake of QRE reporting and learning SoP and how this uptake varies based on pharmacy characteristics including location, prescription volume, and pharmacy type. Secondary data analysis of 91 community pharmacy assessments in Nova Scotia, Canada, was used to explore uptake of QRE standards. Overall, pharmacies are performing relatively well on reporting QREs. However, despite initial success with basic QRE reporting, community pharmacy uptake of QRE learning activities is lagging.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".