Contextual Considerations in Summative Competency Examinations: Relevance to the Long Case
Bibliographic record
Abstract
Long-case patient-based examinations previously formed the basis of summative competency testing in physician certification examinations. These exams were found to be unreliable and have fallen from favor. During the authors' deliberation of the long case in the neurology certification examinations of the Royal College of Physicians and Surgeons of Canada, they considered the examination context and concluded that the appropriate psychometric analysis of the exams is highly contingent on the context. The examination context underlying certification examinations has evolved considerably; within a different context, a more cohesive test system based on a quality assurance framework could better manage substantive psychometric issues around case specificity, comprehensiveness, reliability, and compensability. These arguments are in small part psychometric, but are mostly philosophical and have relevance to the profession and the public.
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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 | 0.064 | 0.275 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.012 |
| 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".