Community Pharmacy Incident Reporting: A New Tool for Community Pharmacies in Canada
Bibliographic record
Abstract
Incident reporting offers insight into a variety of intricate processes in healthcare. However, it has been found that medication incidents are under reported in the community pharmacy setting. The Community Pharmacy Incident Reporting (CPhIR) program was created by the Institute for Safe Medication Practices Canada specifically for incident reporting in the community pharmacy setting in Canada. The initial development of key elements for CPhIR included several focus-group teleconferences with pharmacists from Ontario and Nova Scotia. Throughout the development and release of the CPhIR pilot, feedback from pharmacists and pharmacy technicians was constantly incorporated into the reporting program. After several rounds of iterative feedback, testing and consultation with community pharmacy practitioners, a final version of the CPhIR program, together with self-directed training materials, is now ready to launch. The CPhIR program provides users with a one-stop platform to report and record medication incidents, export data for customized analysis and view comparisons of individual and aggregate data. These unique functions allow for a detailed analysis of underlying contributing factors in medication incidents. A communication piece for pharmacies to share their experiences is in the process of development. To ensure the success of the CPhIR program, a patient safety culture must be established. By gaining a deeper understanding of possible causes of medication incidents, community pharmacies can implement system-based strategies for quality improvement and to prevent potential errors from occurring again in the future. This article highlights key features of the CPhIR program that will assist community pharmacies to improve their drug distribution system and, ultimately, enhance patient safety.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".