A CanMEDS Competency-Based Assessment Tool for High-Fidelity Simulation in Internal Medicine: The Montreal Internal Medicine Evaluation Scale (MIMES)
Bibliographic record
Abstract
High-fidelity simulation is an efficient and holistic teaching method. However, assessing simulation performances remains a challenge. We aimed to develop a CanMEDS competency-based global rating scale for internal medicine trainees during simulated acute care scenarios. Methods Our scale was developed using a formal Delphi process. Validity was tested using 6 videotaped scenarios of 2 residents managing unstable atrial fibrillation, rated by 6 experts. Psychometric properties were determined using a G-study and a satisfaction questionnaire. Results Most evaluators favourably rated the usability of our scale, and attested that the tool fully covered CanMEDS competencies. The scale showed low to intermediate generalization validity. Conclusions This study demonstrated some validity arguments for our scale. The best assessed aspect of performance was communication; further studies are planned to gather further validity arguments for our scale and to compare assessment of teamwork and communication during scenarios with multiple versus single residents.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".