Enhancing Internal Medicine Residents’ Royal College Exam Competency Using In-Training Written Exams within a Competency Based Medical Education Framework
Bibliographic record
Abstract
Background: Canadian residency programs have adopted competency-based medical education, where time-based learning systems are replaced with core competency “milestones” that must be achieved before a student progresses. Assessment tools must be developed to predict performance prior to high-stakes milestones, so interventions can be targeted to improve performance. Objectives: 1. To characterize how well each of three practice written exams predicts passing the Canadian Internal Medicine Royal College (RC) exam. 2. To determine if writing practice exams is perceived to improve performance on the RC exam. Methods: Three 105-question multiple choice question exams were created from a range of internal medicine topics, and offered one month apart to 35 residents. Percentile ranks on each practice exam were compared to the result (pass/fail) on the RC exam. Surveys were completed within 1 month after the RC exam. Results: There were 35 residents invited to participate. Practice exams (PE) 1, 2, and 3 were taken by 33, 26, and 22 residents, for an exam participation rate of 94.3, 74.3, and 62.9%, respectively. Failure on the RC exam could be predicted by percentile ranking <15% on PE1 (OR 19.5, p=0.017) or PE2 (OR 63.0, p=0.006), and by percentile ranking <30% on PE1 (OR 28.8, p=0.003), PE2 (OR 24.0, p=0.010) or PE3 (OR 15.0, p=0.046). The survey was sent out to the 33 participants. Of those, the total number of respondents was 25, with a response rate of 75.5%. Survey takers agreed that practice written exams improved performance on the RC exam (18/25, 88%). Conclusions: Performance in the Canadian Internal Medicine RC Exam can be predicted by performance on any of three practice written exams. This tool can therefore identify trainees for whom additional resources should be invested, to prevent failure of a high-stakes milestone within the competency based medical education framework.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".