Pharmacists’ perceptions of error reporting systems
Bibliographic record
Abstract
Background Healthcare-related adverse events directly impact patient safety. Effective reporting of adverse events and workplace factors affecting the quality and quantity of reporting has been a recent focus. In Nova Scotia, pharmacists have been required to report quality-related events (QREs), errors, and near misses since 2010 through the Canadian Pharmacy Incident Reporting (CPhIR) database. This study aims to better understand how healthcare professionals who use the CPhIR system feel about their experience with QREs and the QRE reporting process. Methods A total of 1000 registered pharmacists and staff were contacted through the Nova Scotia College of Pharmacists. Five focus group meetings were conducted from May to October 2018, consisting of 17 community pharmacists, pharmacy technicians, and assistants. Analysis Thematic analysis was used to identify and define emerging themes in the transcripts by multiple readers. The Actor-Network Analytical Theory helped draw a web of connections in producing a safety culture that extends beyond the roles at the dispensing counter. Results It was found that participants were committed to minimizing and reporting errors, but using the CPhIR database system is both time-consuming and onerous. Additionally, there was a lack of continuity in communicating the QRE system protocols and compliance protocols for the system. Conclusions Community-based pharmacy culture needs to shift from a compliance-based culture, where error reporting is based on completion and volume, to a just culture that embraces quality and learning from mistakes, a critical element of safe dispensing.
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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 | 0.011 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".