Narrative means to professional ends: new strategies for teaching CanMEDS roles in Canadian medical schools.
Bibliographic record
Abstract
PROBLEM ADDRESSED: Medical students in training are rarely encouraged to engage in reflective thinking around clinical encounters. Writing exercises can be an effective route for encouraging student reflection. OBJECTIVE OF PROGRAM: The purpose of this pilot project was to determine if using reflective writing in teaching the CanMEDS roles helps to increase students' understanding of the roles in the clinical context. PROGRAM DESCRIPTION: A pilot project was undertaken with 10 third-year medical students at the University of Toronto in Ontario. Students wrote about a different CanMEDS role for each session based on supplied writing prompts. Students also completed a Narrative Reflection Tool at the end of each group session. A selection of writing samples was assessed for reflection and for an understanding of the CanMEDS roles. Students were also given an opportunity to provide feedback on the program. CONCLUSION: Students demonstrated a good grasp of the CanMEDS roles, strong reflective capacity, and engagement in the learning process. Results suggest reflective writing has an important role in encouraging personal reflection and reflective thinking in the clinical context.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".