Medical leadership competencies for physicians: a systematic scoping review
Bibliographic record
Abstract
INTRODUCTION: The concept of 'medical leadership' has emerged as a critical issue in healthcare, prompting numerous countries to adopt measures toward enhancing leadership competency among physicians. This includes the development of medical leadership competency models. This scoping review aims to map and systematise the existing literature on generalised medical leadership competency models and context-specific leadership competencies for physicians, providing a comprehensive framework for future research. METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for the scoping review framework. A comprehensive search was conducted across peer-reviewed academic databases and grey literature sources. RESULTS: 16 generalised medical leadership models and 13 context-specific competency studies were identified. While most models have been developed in North America and Europe, context-specific competency studies have expanded globally. Frequency analysis highlights the significant influence and application of medical leadership competency models from the UK, the USA, Canada and Switzerland. CONCLUSION: A comparative analysis across countries emphasises the importance of considering contextual and cultural factors when developing and implementing medical leadership competencies. Over the last three decades, medical competency development has reflected a shift towards collective leadership within healthcare, with a focus on team-based, patient-centred approaches in the increasingly complex healthcare systems. Additionally, there is a growing need for competencies that address emerging challenges in healthcare, such as cultural sensitivity, crisis management, business skills and digital literacy.
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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 | 0.040 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".