Examining Academic Leader’s work in implementing Competency-based Medical Education using Organizational Learning Theory
Bibliographic record
Abstract
Context Competency-based medical education (CBME) implementation is being carried out in many medical schools worldwide. Academic Leadership is a strategy where selected Faculty act to influence peers to adopt change. The Université de Montréal medical school, has adopted this strategy to implement CBME. Purpose This paper aims to describe the work of Academic Leaders in the process of CBME implementation and to explore relevance of the Nonaka and Toyama organizational learning theory to map implementation progress. Method Because knowledge creation model focuses on the relationships between leaders and social structures, embedded case study was selected. Diverse sampling method was used to select three departments: internal medicine, surgery and psychiatry, based on the number of CBME training activities. Data collection was at two intervals, two years apart. Semi-structured interviews (individual and group) were conducted with Department Heads and Academic Leaders. Thematic analysis was conducted on the 15 interview transcriptions. Results As implementation begins, Leaders critically revisit accepted teaching routines and develop a common conception of CBME. This enables leaders to communicate with a wider audience and work within existing committees and working groups where they “break down” CBME into practical concepts. This practical understanding, disseminated through Entrustable Professional Activities, enables observable change. Conclusion Leaders’ roles evolved from an “expert” that disseminates knowledge about CBME through lectures, to a responsive and pragmatic supporting role by developing and writing practical tools in collaboration with peers and program directors.
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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 | 0.021 | 0.032 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".