Identifying the exceptional learner in medical education: A doing vs. being framework
Bibliographic record
Abstract
PURPOSE: This study aims to understand what is known about the high performing or exceptional learner in medical education. There is a rich literature about learners in difficulty, yet little is known about those performing at the high end, also known as exceptional learners. METHODS: A qualitative study was undertaken whereby 15 faculty members at the University of Toronto were interviewed to explore their experiences with these learners. RESULTS: Based on the findings, we developed a framework to categorize characteristics of exceptional learners by differentiating them as either 'Being' (a pre-existing attribute or set of values that the learner possesses from the start of training) or 'Doing' (demonstrable characteristics that can be observed or measured). Using this framework, we identified five characteristics in the category of 'Being', five in the category of 'Doing', and two that could be situated in either or both. CONCLUSION: Utilizing this framework to describe exceptional learners will aid teachers in identifying them early in their training so that their training experience can be enhanced. This novel approach contributes to our knowledge of the exceptional medical learner. The optimization of the training experience will maximize the opportunity to ensure that these learners reach their full potential to contribute to the healthcare system.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".