Bibliographic record
Abstract
To the Editor: Calls for increased physician leadership are growing in number and strength.1–3 The recent proliferation of leadership curricula in medical schools and residency programs suggests that educators are beginning to believe that focused leadership development efforts early in medical education could produce more and better-trained physician leaders. Our own experience developing an innovative leadership education and development program in the University of Toronto’s Faculty of Medicine is consistent with study findings at the Mayo Medical School, in which 85% of student leaders felt leadership should be taught in medical school.4 We are convinced that students enter medical school with an interest in leadership and a desire to build their skills. Yet medical curricula have been slow to develop frameworks in support of these interests. To harness this enthusiasm, medical schools must actively strive to facilitate leadership education. Toward this end, we offer five evidence-based suggestions: Leadership development programs should be student focused and flexible, encourage formal mentorship, expose students to disciplines outside of medicine, be rigorously evaluated, and engage accreditation agencies to measure and promote leadership development. Two meta-analyses, covering studies from 1951 to 2001, demonstrated that leadership development programs can be effective across many disciplines, particularly when they are tailored to the needs of the trainee and reflect the objectives of the organization.5,6 These formalized approaches, which mix lessons in theory with practical experiences, should be used to help medical students realize their leadership potential. Amol A. Verma, MD, MPhil Resident in internal medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada; [email protected] Jordan D. Bohnen, MD, MBA Resident in general surgery, Massachusetts General Hospital, Boston, Massachusetts.
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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".