Adaptive Expertise: Zooming in the Big Picture
Bibliographic record
Abstract
The integration of adaptive expertise (AE), considered an advanced form of expertise, into CanMEDS2025 is underway, but there's a lack of clarity on its practical application. This study seeks to explore and identify specific instances of AE in the clinical context to better understand its real-world implementation and the necessary support structures required for education reform. We generated data from semi-structured interviews using a generic qualitative approach guided by constructivist grounded theory and rich pictures drawing to explore how expert physicians exhibit AE. We sought physicians who had completed all postgraduate medical training and had independently practiced for a minimum of five years within Canada, to ensure participants had accumulated a range of experiences in the clinical setting over time and sufficient domain knowledge to engage in AE. Expert physicians consider unique external contextual factors that cannot be controlled, such as weather or lack of resources (staff/other expertise, equipment, space, hospital area, time) to be novel circumstances. Their approach to challenges were framed by the surrounding contextual factors of the situation. They emphasize the importance of a knowledge foundation and skillset, teamwork, seeking resources, and understanding how the environment in which they work optimally enhances their expertise. This framework highlights critical aspects of AE in the clinical setting. To effectively implement AE in the curriculum, we must address the importance of the context outside the individual. Rather than emphasizing the individual, AE research should be redirected towards an examination of the environment, healthcare system, and support structures in place.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".