Collaborating internationally on physician leadership development: why now?
Bibliographic record
Abstract
Purpose This paper aims to highlight the importance of leadership development for all physicians within a competency-based medical education (CBME) framework. It describes the importance of timely international collaboration as a key strategy in promoting physician leadership development. Design/methodology/approach The paper explores published and Grey literature around physician leadership development and proposes that international collaboration will meet the expanding call for development of leadership competencies in postgraduate medical learners. Two grounding frameworks were used: complexity science supports adding physician leadership training to the current momentum of CBME adoption, and relational cultural theory supports the engagement of diverse stakeholders in multiple jurisdictions around the world to ensure inclusivity in leadership education development. Findings An international collaborative identified key insights regarding the need to frame physician leadership education within a competency-based model. Practical implications International collaboration can be a vehicle for developing a globally relevant, generalizable physician leadership curriculum. This model can be expanded to encourage innovation, scholarship and program evaluation. Originality/value A competency-based leadership development curriculum is being designed by an international collaborative. The curriculum is based on established leadership and education frameworks. The international collaboration model provides opportunities for ongoing sharing, networking and diversification.
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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 | 0.031 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".