Structure of health-care dyad leadership: an organization’s experience
Bibliographic record
Abstract
Purpose This study aims to explore the structural aspects (roles, responsibilities and reporting) of dyad leadership in one health-care organization (HCO). Design/methodology/approach The perceptions of 32 leaders (17 physician leaders and 15 dyad co-leaders) in formal leadership positions (six first-level with formal authority limited to teams or divisions, 23 middle-level with wider departmental or program responsibility and three senior-level with institution-wide authority) were obtained through focus groups and surveys. In addition, five senior leaders were interviewed. Descriptive statistics was used for quantitative data, and qualitative data were analyzed for themes by coding and categorization. Findings There are a large number of shared responsibilities in the hybrid model, as most activities in HCOs bridge administrative and professional spheres. These span the leadership (e.g. global performance and quality improvement) and management (e.g. human resources, budgets and education delivery) domains. The individual responsibilities, except for staff and physician engagement are in the management domain (e.g. operations and patient care). Both partners are responsible for joint decision-making, projecting a united front and joint reporting through a quadrat format. The mutual relationship and joint accountability are key characteristics and are critical to addressing potential conflicts and contradictions and achieving coherence. Practical implications Clarity of role will assist development of standardized job descriptions and required competencies, recruitment and leadership development. Originality/value This is an original empirical study presenting an integrated view of dyad leaders and senior leadership, meaningful expansion of shared responsibilities including academic functions and developing mutual relationship and emphasizing the central role of stability generating management functions.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".