Head Office Commitment to Quality-Related Event Reporting in Community Pharmacy
Bibliographic record
Abstract
BACKGROUND: This research explores how perceptions of head office commitment to quality-related event (QRE) reporting differ between pharmacy staff type and between pharmacies with high and low QRE reporting and learning performance. QREs include known, alleged or suspected medication errors that reach the patient as well as medication errors that are intercepted prior to dispensing. METHODS: A survey questionnaire was mailed in the spring of 2010 to 427 pharmacy managers, pharmacists and pharmacy technicians in Nova Scotia. Nonparametric statistics were used to determine differences based on pharmacy staff type and pharmacy performance. Content analysis was used to analyze the responses to open-ended survey questions. RESULTS: A total of 210 surveys were returned, for a response rate of 49.2%. However, the current study used only the subgroup of pharmacy staff who self-reported working at a chain pharmacy, for a total of 124 usable questionnaires. The results showed that community pharmacies viewed head office commitment to QRE reporting as an area to improve. In general, high-performing pharmacies ranked head office commitment higher than low-performing pharmacies. DISCUSSION: One possible reason why high-performing pharmacies ranked the variables higher may be that increased levels of head office support for QRE processes have led these pharmacies to adopt and commit to QRE processes and thus increase their performance. CONCLUSION: Demonstrated commitment to QRE reporting, ongoing encouragement and targeted messages to staff could be important steps for head office to increase QRE reporting and learning 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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".