Insights from Regulatory Data on Development Needs of Community Pharmacy Professionals
Bibliographic record
Abstract
The aim of this study was to use data available to a Canadian health professions regulator (Ontario College of Pharmacists) to identify areas of opportunity where practitioners (pharmacists and pharmacy technicians) could benefit from further development, in order to optimize practice and improve the quality of care. Four de-identified datasets were used to extract themes from areas of jurisprudence (1969 exam records), member practice assessments (2610 records), pharmacy assessments (2024 records) and conduct (640 case records). Outcome measures included performance in examinations and assessments and competency gaps identified in conduct investigations. Thematic analysis of outcomes was done in two stages. First, the four outcomes were derived independently for each dataset. Second, the top five issues were extracted for each dataset. It was hypothesized that common themes in competency gaps across all four datasets would emerge from this top five selection. We found three main common areas of opportunity where practitioners could benefit from further development: patient assessment and safety; documentation; and ethical, legal and professional responsibilities.
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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 | 0.030 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".