Competence by Design: The Role of High-Stakes Examinations in a Competence Based Medical Education System
Bibliographic record
Abstract
Competency based medical education is developed utilizing a program of assessment that ideally supports learners to reflect on their knowledge and skills, allows them to exercise a growth mindset that prepares them for coaching and eventual lifelong learning, and can support important progression and certification decisions. Examinations can serve as an important anchor to that program of assessment, particularly when considering their strength as an independent, third-party assessment with evidence that they can predict future physician performance and patient outcomes. This paper describes the aims of the Royal College of Physicians and Surgeons of Canada’s (“the Royal College”) certification examinations, their future role, and how they relate to the Competence by Design model, particularly as the culture of workplace assessment and the evidence for validity evolves. For example, high-stakes examinations are stressful to candidates and focus learners on exam preparation rather than clinical learning opportunities, particularly when they should be developing greater autonomy. In response, the Royal College moved the written examination earlier in training and created an exam quality review, by a specialist uninvolved in development, to review the exam for clarity and relevance. While learners are likely to continue to focus on the examination as an important hurdle to overcome, they will be preparing earlier in training, allowing them the opportunity to be more present and refine their knowledge when discussing clinical cases with supervisors in the Transition to Practice phase. The quality review process better aligns the exam to clinical practice and can improve the educational impact of the examination preparation process.
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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 | 0.184 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".