Intentional partnering: a grounded theory study on developing effective partnerships among nurse and physician managers as they co‐lead in an evolving healthcare system
Bibliographic record
Abstract
AIM: The aim of this study was to describe the process of how nurse and physician managers in formalized dyads work together to address clinical management issues in the surgical division of one hospital setting. BACKGROUND: Nurse and physician managers are uniquely positioned to co-lead and transform healthcare delivery. However, little is known about how this management dyad functions in the healthcare setting. DESIGN: A constructivist grounded theory approach was used to investigate the process of how nurse and physician managers work together in formalized dyads in an urban Canadian university affiliated teaching hospital. METHODS: Data collection occurred from September 2013-August 2014. Data included participant observation (n = 142 hours) and intensive interviews (n = 36) with nurse-physician manager dyads (12 nurses, 9 physicians) collected in a surgical department. Theoretical sampling was used to elaborate on properties of emerging concepts and categories. RESULTS/FINDINGS: A substantive theory on 'intentional partnering' was generated. Nurses' and physicians' professional agendas, which included their interests and purposes for working with each other served as the starting point of 'intentional partnering'. The theory explains how nurse and physician managers align their professional agendas through the processes of 'accepting mutual necessity', 'daring to risk (together)' and 'constructing a shared responsibility'. Being credible, earning trust and safeguarding respect were fundamental to communicating effectively. CONCLUSION: Intentional partnering elucidates the relational components of working together and the strategizing that occurs as each partner deliberates on what he or she is willing to accept, risk and put into place to reap the benefits of collaborating.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".