Continuous Professional Development: The Ontario Experience in Profes- sional Self-Regulation Through Quality Assurance and Peer Review
Bibliographic record
Abstract
Zubin Austin, PhD, Della Croteau, MCEd, Anthony Marini, PhD, and Claudio Violato, PhD a Leslie Dan Faculty of Pharmacy, University of Toronto b Ontario College of Pharmacists c University of Calgary Continuous professional development has emerged as a significant issue for pharmacy educators across North America. As a result of major regulatory changes governing pharmacy practice, the province of Ontario has developed a comprehensive quality assurance and peer review program designed to systematically assess the patient care competencies of practicing pharmacists. Key program components include: (1) a 2part registration process wherein pharmacists elect to pursue Part A, direct patient care, or Part B, non-direct patient care; (2) a learning portfolio to demonstrate lifelong learning; and (3) a practice review process with a remediation component. In the 5 years that the Program has been in place 86% of practicing pharmacists were able to self-direct their professional development, while 14% required peer assistance. Performance differences emerged based on the number of years since graduating, place of graduation, and site of practice. These results provide pharmacy educators with important information regarding the needs of practicing pharmacists, as measured through direct assessment of their clinical knowledge and skills.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".