A critical analysis of The Royal College of Physicians and Surgeons of Canada examination experience
Bibliographic record
Abstract
The Royal College of Physicians and Surgeons of Canada (RCPSC) plays a leading role in specialty and subspecialty post graduate medical education (PGME) in Canada. As the RCPSC accredits PGME programs, these programs are structured to meet the RCPSC Competence by Design model and their CanMEDS roles. RCPSC Certification is required by Medical Regulatory Agencies (MRAs) across Canada as a condition of entry to independent practice. The RCPSC relies heavily on the use of high-stakes subject examinations as a key component of its Certification process. Recently, questions have been raised regarding the usefulness of such high-stakes examinations. If such examinations are to be fair and equitable, they must be designed and implemented in accordance with best practices for educational testing and the processes for implementation and grading must be transparent and fair. This paper reviews the recent literature on high-stakes examinations and best practices in examination construction, references the findings of a survey of RCPSC examination experiences conducted by the Society for Canadians Studying Medicine Abroad exploring the perception of respondents, and raises concerns regarding RCPSC examinations related to validity, reliability, and fairness. The paper concludes by recommending closer scrutiny of RCPSC examination processes by interested stakeholders and by provincial MRA's who delegate entry to practice decisions to the RCPSC.
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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 | 0.021 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".