Collaborating internationally on physician leadership education: first steps
Bibliographic record
Abstract
Purpose Physicians are often ill-equipped for the leadership activities their work demands. In part, this is due to a gap in traditional medical education. An emergent international network is developing a globally relevant leadership curriculum for postgraduate medical education. The purpose of this article is to share key learnings from this process to date. Design/methodology/approach The Toronto International Summit on Leadership Education for Physicians (TISLEP) was hosted by the Royal College of Physicians and Surgeons of Canada, and the University of Toronto's Faculty of Medicine and Institute of Health Policy, Management and Evaluation. Of 64 attendees from eight countries, 34 joined working groups to develop leadership competencies. The CanMEDS Competency Framework, stage of learner development and venue of learning formed the scaffold for the work. Emotional intelligence was selected as the topic to test the feasibility of fruitful international collaboration; results were presented at TISLEP 2015. Findings Dedicated international stakeholders engaged actively and constructively through defined working groups to develop a globally relevant, competency-based curriculum for physician leadership education. Eleven principles are recommended for consideration in physician leadership curriculum development. Defining common language and taxonomy is essential for a harmonized product. The importance of establishing an international network to support implementation, evaluation, sustainability and dissemination of the work was underscored. Originality/value International stakeholders are collaborating successfully on a graduated, competency-based leadership curriculum for postgraduate medical learners. The final product will be available for adaptation to local needs. An international physician leadership education network is being developed to support and expand the work underway.
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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 | 0.083 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.005 | 0.041 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.022 | 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".