Quality-related events reported by community pharmacies in Nova Scotia over a 7-year period: a descriptive analysis
Bibliographic record
Abstract
BACKGROUND: Quality-related events are defined as medication errors that reach the patient (e.g., incorrect drug, dose and quantity), in addition to medication errors that are intercepted before dispensing (i.e., near misses). The aim of this study is to quantify and characterize such events as reported by community pharmacies in a Canadian province. METHODS: A retrospective analysis was conducted on quality-related events reported to the Community Pharmacy Incident Reporting system from 301 community pharmacies in Nova Scotia between Oct. 1, 2010, and June 30, 2017. We performed a descriptive analysis on these events with respect to the discoverer, patient outcome, medication system stages and type. RESULTS: = 928) were associated with patient harm. Incorrect dose or frequency, incorrect quantity and incorrect drug were the most common types of quality-related events reported. Most of the quality-related events occurred at order entry, followed by preparation and dispensing, and prescribing. INTERPRETATION: Quality-related events reported by community pharmacies differ from those reported in institutional settings with respect to patient outcome, medication system stages and type. This analysis provides valuable information to guide quality improvement initiatives to strengthen medication safety in community pharmacies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".