Exploring Integration in Action: Competencies as Building Blocks of Expertise
Bibliographic record
Abstract
PURPOSE: Competency frameworks such as the CanMEDS roles and the ACGME core competencies may lead to the implicit assumption that physicians can learn and practice individual competencies in isolation. In contrast, models of adaptive expertise suggest that the integration of competencies reflects the capabilities of an expert physician. Thus, educational programming aimed at teaching discrete roles or competencies might overlook expert physician capabilities that are central to patient care. To develop expertise, learning opportunities must reflect expert capabilities. To better understand the relationship between competency-based medical education and expert development, the authors sought to explore how integrated competencies are enacted during patient care by postgraduate medical trainees. METHOD: Using a cognitive ethnographic approach, in 2014-2015 the authors conducted observations and-to refine and elaborate these observations-ad hoc informal interviews with 13 postgraduate trainee participants. Data collection resulted in 92 hours of observation, 26 patient case portraits, and a total of 220 pages of field notes for analysis. Through analysis, the authors identified and examined moments when postgraduate trainees appeared to be simultaneously enacting multiple competencies. RESULTS: The authors identified two key expert capabilities in moments of integrated competence: finding complexity and being patient-centered. They described two mechanisms for these forms of integration: valuing the patient's narrative of their illness, and integrated understanding. CONCLUSIONS: Understanding integrated competencies as the building blocks of expert capabilities, along with recognizing the importance of mechanisms that support integration, offers an opportunity to use existing competency-based frameworks to understand and teach adaptive expertise.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".