Learning the CanMEDS roles in a near-peer shadowing program: A mixed methods randomized control trial
Bibliographic record
Abstract
BACKGROUND: The development of competency frameworks, such as CanMEDS, has helped define professional behavior, but programs that translate their theoretical aspects into practical learning are lacking. AIM: To improve instruction of the CanMEDS framework, the University of Alberta implemented a program in which 83 first-year medical students shadowed a first-year resident for eight months. METHODS: A randomized trial compared participants' attitudes and knowledge regarding CanMEDS to controls. A concurrent-triangulation mixed methods design with questionnaires and interviews provided a comprehensive understanding of program experiences. RESULTS: Students reported increasing their understanding of CanMEDS and increased their acceptance of the framework's importance and knowledge of its contents when compared to controls. Residents also reported that their knowledge of CanMEDS had increased. Participants considered the program to be effective for learning CanMEDS and developing professionalism, especially when paired with clinical encounters relevant to given professional roles. CONCLUSION: This simple, low cost, near-peer shadowing program can be useful for teaching professional behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".