Bibliographic record
Abstract
Purpose The purpose of this paper is to describe research that examined physician leadership development using complexity science principles. Design/methodology/approach Intensive interviewing of 21 participants and document review provided data regarding physician leadership development in health-care organizations using five principles of complexity science (connectivity, interdependence, feedback, exploration-of-the-space-of-possibilities and co-evolution), which were grouped in three areas of inquiry (relationships between agents, patterns of behaviour and enabling functions). Findings Physician leaders are viewed as critical in the transformation of healthcare and in improving patient outcomes, and yet significant challenges exist that limit their development. Leadership in health care continues to be associated with traditional, linear models, which are incongruent with the behaviour of a complex system, such as health care. Physician leadership development remains a low priority for most health-care organizations, although physicians admit to being limited in their capacity to lead. This research was based on five principles of complexity science and used grounded theory methodology to understand how the behaviours of a complex system can provide data regarding leadership development for physicians. The study demonstrated that there is a strong association between physician leadership and patient outcomes and that organizations play a primary role in supporting the development of physician leaders. Findings indicate that a physician's relationship with their patient and their capacity for innovation can be extended as catalytic behaviours in a complex system. The findings also identified limiting factors that impact physicians who choose to lead, such as reimbursement models that do not place value on leadership and medical education that provides minimal opportunity for leadership skill development. Practical Implications This research provides practical applications for physician leadership development and emphasizes that it is incumbent upon physicians and organizations to focus attention on this to achieve improved patient and organizational outcomes. Originality/value This study pairing complexity science and physician leadership represents a unique way to view the development of physician leaders within the context of the complex system that is health care.
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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 | 0.026 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".