Defining Leadership in Undergraduate Medical Education, Networks, and Instructors: A Scoping Review
Bibliographic record
Abstract
Reviews of the literature on leadership training in undergraduate medical education have been conducted since 2014. Previous reviews have not identified networks, defined leadership, studied the selection criteria for instructors, nor analyzed leadership as interprofessional or transprofessional education. This scoping review fills these gaps. Inclusion criteria included use of competency-based education to teach leadership in universities, and quality assessment. Indexes and grey literature in Spanish, Portuguese, and English languages were included from six databases. Hand searching and consultation were employed for selected bodies of literature. This review identified leadership interventions in nine countries which had national and international networks primarily in English-speaking and European countries. No literature was found in Spanish-speaking or Portuguese-speaking countries, nor in Africa. Teaching leadership was linked mainly with undergraduate medical education and interprofessional education. This review identified 23 leadership and leader definitions and underscored the importance of including values in leadership definitions. Instructors were selected by discipline, role, experience, and expertise. This review may be used to inform the teaching of leadership in undergraduate medical curricula by suggesting potential networks, reflecting on diverse leadership definitions and interprofessional/transprofessional education, and assisting in selection of instructors.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".