Self-assessment and continuing professional development: The Canadian perspective
Bibliographic record
Abstract
INTRODUCTION: Several recent studies highlight that physicians are not very accurate at assessing their competence in clinical domains when compared to objective measures of knowledge and performance. Instead of continuing to try to train physicians to be more accurate self-assessors, the research suggests that physicians will benefit from learning programs that encourage them to reflect on their clinical practice, continuously seek answers to clinical problems they face, compare their knowledge and skills to clinical practice guidelines and benchmarks, and seek feedback from peers and their health care team. METHODS: This article describes the self-assessment learning activities of the College of Family Physicians of Canada Maintenance of Proficiency program (Mainpro) and the Royal College of Physicians and Surgeons of Canada Maintenance of Certification program. (MOC) RESULTS: The MOC and the Mainpro programs incorporate several self-evaluation learning processes and tools that encourage physicians to assess their professional knowledge and clinical performance against objective measures as well as guided self-audit learning activities that encourage physicians to gather information about their practices and reflect on it individually, with peers and their health care team. Physicians are also rewarded with extra credits when they participate in either of these kinds of learning activities. DISCUSSION: In the future, practice-based learning that incorporates self-assessment learning activities will play an increasingly important role as regulators mandate that all physicians participate in continuing professional development activities. Research in this area should be directed to understanding more about reflection in practice and how we can enable physicians to be more mindful.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".