Challenges in Translation: Lessons from Using Business Pedagogy to Teach Leadership in Undergraduate Medicine
Bibliographic record
Abstract
Problem: Leadership is increasingly recognized as a core physician competency required for quality patient care, continual system improvement, and optimal healthcare team performance. Consequently, integration of leadership into medical school curriculum is becoming a priority. This raises the question of the appropriate context, timing, and pedagogy for conveying this competency to medical students.\nIntervention: Our program introduced a 1-week leadership course grounded in business pedagogy to Year 1 medical students. The curriculum centred on four themes: (a) Understanding Change, (b) Effective Teamwork, (c) Leading in Patient Safety, and (d) Leadership in Action. Post-curriculum qualitative student feedback was analyzed for insight into student satisfaction and attitude towards the leadership course content.\nContext: The Undergraduate Medical Education program of the Schulich School of Medicine & Dentistry, Western University, is delivered over 4 years across 2 campuses in London and Windsor, Ontario, Canada. Course structure moved from traditional passive lectures to established business pedagogy, which involves active engagement in modules, case-based discussions, insights from guest speakers, and personal reflection.\nOutcome: A student-led survey evaluated student opinion regarding the leadership course content. Students valued career development reading materials and insights from guest speakers working in healthcare teams. Students did not relate to messages from speakers in senior healthcare leadership positions. Course scheduling late in the second semester was viewed negatively. Overall student opinion suggested that the 1-week course was suboptimal for establishing leadership principles and translated business pedagogy was ineffective in this context.\nLessons Learned: Leadership curriculum in Undergraduate Medical Education should be grounded in a healthcare context relevant to the student's stage of training. Student engagement may be better supported if leadership is framed as a competency throughout their career. Schools considering such innovations could draw lessons from other professional schools and utilize material and faculty that resonate with students.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".