Why we need to teach leadership skills to medical students: a call to action
Bibliographic record
Abstract
Health system reform models since the early 1990s have recommended leadership training for medical students, graduates and health workers. Clinicians often have leadership roles thrust on them early in their postgraduate career. Those who are not well trained in leadership and the knowledge that comes with leadership skills may struggle with the role, which can impact patient safety and create unhealthy working environments. While there is some literature published in this area, there appears to be little formal evaluation of the teaching of leadership, with scarcely any discussion about the need to do so in the future. There are clear gaps in the research evidence of how to teach and assess medical leadership teaching. In this paper, three leadership frameworks from Australia, Canada and the UK are compared in terms of leadership capabilities for a global view of medical leadership training opportunities. A literature review of the teaching, assessment and evaluation of leadership education in medical schools in Australia, the UK and America is also discussed and gaps are identified. This paper calls for an education shift to consider practical health system challenges, citing the mounting evidence that health system reform will require the teaching and rigorous evaluation of leadership methods. Opportunities for teaching leadership in the curricula are identified, as well as how to transform leadership education to include knowledge and practice so that students have leadership skills they can use from the time they graduate.
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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 | 0.062 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.031 | 0.047 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".