Creating space for leadership education in undergraduate medical education in Canada
Bibliographic record
Abstract
The need for effective leadership by physicians is clear, yet the design/delivery of curricula, and assessment of leadership competencies, in Undergraduate Medical Education (UGME) continues to need work. In reappraising their UGME assessment strategies, the Medical Council of Canada (MCC) invited position papers across diverse lenses, including the CanMEDS Intrinsic Roles. This article is foundational work derived from the report on leadership assessment to the MCC. Using Kern's Model of Curriculum development as a guide, we reviewed the landscape of Canadian UGME leadership education through an environmental scan of the published and grey literature, Canadian leadership frameworks and resources, and consultation with learner and faculty leadership. Leadership education across programs was highly variable and learners were often unaware of available opportunities. In response, we have suggested processes for curricular development, including strategies for key content, teaching and assessment, and program evaluation considerations. Leadership education cannot remain another checkbox on a list of UGME experiences. Such training necessitates focused attention and investment to foster ongoing identity formation toward becoming a good doctor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".