Distributed leadership in healthcare: leadership dyads and the promise of improved hospital outcomes
Bibliographic record
Abstract
PURPOSE: This paper aims to extend the consideration of distributed leadership in health-care settings. Leadership is typically studied from the classical notion of the place of single leaders and continues to examine distributed leadership within small teams or horizontally. The purpose is to develop a practical understanding of how distributed leadership may occur vertically, between different layers of the health-care leadership hierarchy, examining its influence on health-care outcomes across two hospitals. DESIGN/METHODOLOGY/APPROACH: Using semi-structured interviews, data were collected from 107 hospital employees (including executive leadership, clinical management and clinicians) from two hospitals in Australia and the USA. Using thematic content analysis, an iterative process was adopted characterized by alternating between social identity and distributed leadership literature and empirical themes to answer the question of how the practice of distributed leadership influences performance outcomes in hospitals? FINDINGS: The perceived social identities of leadership groups shaped communication and performance both positively and negatively. In one hospital a moderating structure emerged as a leadership dyad, where leadership was distributed vertically between hospital hierarchal layers, observed to overcome communication limitations. Findings suggest dyad creation is an effective mechanism to overcome hospital hierarchy-based communication issues and ameliorate health-care outcomes. ORIGINALITY/VALUE: The study demonstrates how current leadership development practices that focus on leadership relational and social competencies can benefit from a structural approach to include leadership dyads that can foster these same competencies. This approach could help develop future hospital leaders and in doing so, improve hospital outcomes.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".