Evolving Maintenance of Certification in Canada: A Collaborative Journey
Bibliographic record
Abstract
Continuous professional development (CPD) is crucial for physicians to maintain and enhance their skills. In response to the changing context of CPD and health care, this study applied a design thinking approach to transform and modernise the Royal College of Physicians and Surgeons of Canada's Maintenance of Certification (MOC) Program. A member-wide survey and co-design sessions with physicians, CPD leaders, and patient representatives were conducted, emphasising the importance of their insights and experiences. The data revealed key themes for the programme such as fostering meaningful learning, addressing barriers to CPD, supporting collaboration, and responding to the need for modern, flexible CPD delivery methods. Using "empathy", "define", "ideate", "prototype", and "test" phases, we continuously refined the MOC framework of CPD activities based on comprehensive user experiences and needs insights. The revised framework was iteratively prototyped and validated to ensure it was user-friendly and aligned with professional and regulatory requirements. The findings underscore the effectiveness of the design thinking approach in creating a dynamic, responsive MOC framework that supports CPD and meets the evolving needs of medical professionals. This approach not only demonstrates the effectiveness of design thinking but also the importance of engaging users in the development process, making them feel valued and integral to the transformation of the MOC Program.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".