Competencies physicians need to lead – a Canadian case
Bibliographic record
Abstract
Purpose Emerging evidence correlates increased physician leadership effectiveness with improved patient and healthcare system outcomes. To maximize this benefit, it is critical to understand current physician leadership needs. The purpose of this study is to understand, through physicians' self-reporting, their own and others' most effective and weakest leadership skills in relation to the LEADS leadership capabilities framework. Design/methodology/approach The authors surveyed 209 Canadian physician leaders about their perceptions of their own and other physicians' leadership abilities. Thematic analysis was used, and the results were coded deductively into the five LEADS categories, and new categories emerging from inductive coding were added. Findings The authors found that leaders need more skills in the areas of Engage Others and Lead Self, and an emergent category of Business Skills, which includes financial competency, budgeting, facilitation, etc. Further, Achieve Results, Develop Coalitions and Systems Transformation are skills least reported as needed in both self and others. Originality/value The authors conclude that LEADS, in its current form, has a gap in the competencies prescribed, namely, "Business Skills". They recommend the development of a more comprehensive LEADS framework that includes such skills as financial literacy/competency, budgeting, facilitation, etc. The authors also found that certain dimensions of LEADS are being overlooked by physicians in terms of importance (Systems Transformation, Achieve Results, Develop Coalitions), and this warrants greater investigation into the reasons why these skills are not as important as the others (Engage Others and Lead Self).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".