The dyad leadership model : defining a new physician/nurse relationship
Bibliographic record
Abstract
Health care systems across Canada continue to be challenged to meet the growing health care demands of the population. These challenges are set against the backdrop of fiscal realities that demand strong leadership that strikes a balance between providing quality and safe care and obtaining financial sustainability in the taxpayer-funded system. A leadership model that is emerging in health organizations is the dyad model that often partners a physician and a nurse administrative leader as a leadership team to have accountability and responsibility for large, complex portfolios. In an effort to determine whether the physician-nurse dyad leadership model provides for a successful leadership partnership, this study examined whether the historical social relationship between male physicians and female nurses has an impact on that leadership partnership. A qualitative research approach was taken in this study using interpretative phenomenological analysis to frame the data collection and analysis. Semi-structured interviews were conducted with six key informants which included three male physician leaders and three female nurse leaders. The findings demonstrate that the physician and nurse leaders have the ability to develop relationships when there is respect for the skills and knowledge that each brings to the partnership and where each one acknowledges there are instances and circumstances where there is a need to have confidence and trust in the ability of the partner to take the lead. The support exists for the dyad relationship and that it is an effective model in health care as it allows administrators and clinicians to draw on the other’s knowledge, skills and experience to form an interdependent leadership partnership. \nKeywords: Leadership, health care, physician role, nurse role, dyad leadership model, gender
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".