Conceptual and practical challenges in the assessment of physician competencies
Bibliographic record
Abstract
Abstract The shift to using outcomes-based competency frameworks in medical education in many countries around the world requires educators to find ways to assess multiple competencies. Contemporary medical educators recognize that a competent trainee not only needs sound biomedical knowledge and technical skills, they also need to be able to communicate, collaborate and behave in a professional manner. This paper discusses methodological challenges of assessment with a particular focus on the CanMEDS Roles. The paper argues that the psychometric measures that have been the mainstay of assessment practices for the past half-century, while still valuable and necessary, are not sufficient for a competency-oriented assessment environment. New assessment approaches, particularly ones from the social sciences, are required to be able to assess non-Medical Expert (Intrinsic) roles that are situated and context-bound. Realist and ethnographic methods in particular afford ways to address the challenges of this new assessment. The paper considers the theoretical and practical bases for tools that can more effectively assess non-Medical Expert (Intrinsic) roles.
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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 | 0.234 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".