Intentional Partnering: How nurse and physician managers in formalized dyads work together to address clinical management issues in a hospital setting
Bibliographic record
Abstract
Background: In today's healthcare organizations, the pace of technological change, increasing complexity, competitive demands and risks involved in decision making have made it difficult for one individual to lead alone.Collaborative management structures are critical to transforming healthcare delivery and a co-leadership model offers one such approach.Nurses and physicians are uniquely positioned to share the executive roles of co-leadership; however, little is known about how this management dyad operates in the healthcare setting.Most of what is known about the nurse-physician relationship has been based on research at the clinical unit level from the nurses' perspective.Objective: This grounded theory study seeks to explain how nurse and physician managers in formalized "partnerships" work together to address clinical management issues. Methods/Procedures:A nurse-physician management structure (Partnered Management Model) was adopted throughout an urban Canadian university affiliated teaching hospital in 2008 where nurse and physician managers in each division or program were expected to formally "partner" with each other to address clinical management issues.Dyads were purposefully sampled in the Department of Surgery in 2013 on the recommendation of key stakeholders who believed the department effectively illustrated nurse-physician "partnerships".This was followed by theoretical sampling to elaborate on properties of emerging concepts and categories.A total of 36 interviews with 21 participants (12 nurses, 9 physicians) were audio-recorded and transcribed verbatim.The total time spent in observation was 142 hours (110 hours at senior management level and 32 hours at clinical management level) with field notes recorded for 90 observed events.Peer debriefing, informant/participant feedback and an audit trail of all methodological vi decisions ensured the trustworthiness of the data.Constant comparison, open and focused coding, theoretical sensitivity and memos were used in the data analysis.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 to reap the benefits of partnering through the processes of accepting mutual necessity, daring to risk together and constructing a shared responsibility.Some partners may take the lead or contribute differently in each of the processes.Essential conditions such as being credible, earning trust and safeguarding respect built a foundation for partnering and communicating effectively.Deliberate strategies from senior leadership provided momentum in the intentional partnering process. Conclusions:The theory elucidates the strategizing that underlies the processes as well as the characteristics that influence how the nurse/physician management relationship develops and evolves.The findings may inform the process of developing effective partnerships between nurses and physicians as they take on co-management responsibilities in an evolving healthcare system.The findings may also be applied to health professional management education.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".