Regulatory Authority Approaches to Deploying Quality Improvement Standards to Community Pharmacies
Bibliographic record
Abstract
BACKGROUND: Continuous quality improvement (CQI) programs provide an effective means to improve the safety and quality of community pharmacy practice. The role of formal support processes in ensuring the success of these CQI programs is explored in this research using the SafetyNET-Rx project. OBJECTIVE: The primary objectives of this research were to determine how knowledge of, and confidence in, mandated CQI standards differs among pharmacies with access to formal support mechanisms and those without and the challenges faced by both. METHODS: A survey questionnaire was mailed to 179 community pharmacies in Nova Scotia, Canada, in spring 2011. Quantitative results were analyzed using the Mann-Whitney U test for nonparametric data. Qualitative open-ended responses were analyzed using content analysis. RESULTS: Performing the Mann-Whitney U test indicated that a number of differences exist between the 2 groups with respect to: (1) staff knowledge of reporting quality-related events (QREs) to an anonymous database; (2) conducting annual pharmacy safety self-assessments; (3) confidence in meeting these 2 elements; and (4) documenting changes to address QREs. A number of challenges were identified by respondents through the open-ended questions. CONCLUSIONS: This research highlights the value of the active provision of formal support when developing standards related to quality improvement.
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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 | 0.194 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".