The Contribution of Hospital Nursing Leadership Styles to 30-day Patient Mortality
Bibliographic record
Abstract
BACKGROUND: Nursing work environment characteristics, in particular nurse and physician staffing, have been linked to patient outcomes (adverse events and patient mortality). Researchers have stressed the need for nursing leadership to advance change in healthcare organizations to create safer practice environments for patients. The relationship between styles of nursing leadership in hospitals and patient outcomes has not been well examined. OBJECTIVE: The purpose of this study was to examine the contribution of hospital nursing leadership styles to 30-day mortality after controlling for patient demographics, comorbidities, and hospital factors. METHODS: Ninety acute care hospitals in Alberta, Canada, were categorized into five styles of nursing leadership: high resonant, moderately resonant, mixed, moderately dissonant, and high dissonant. In the secondary analysis, existing data from three sources (nurses, patients, and institutions) were used to test a hypothesis that the styles of nursing leadership at the hospital level contribute to patient mortality rates. RESULTS: Thirty-day mortality was 7.8% in the study sample of 21,570 medical patients; rates varied across hospital categories: high resonant (5.2%), moderately resonant (7.4%), mixed (8.1%), moderately dissonant (8.8%), and high dissonant (4.3%). After controlling for patient demographics, comorbidities, and institutional and hospital nursing characteristics, nursing leadership styles explained 5.1% of 72.2% of total variance in mortality across hospitals, and high-resonant leadership was related significantly to lower mortality. CONCLUSIONS: Hospital nursing leadership styles may contribute to 30-day mortality of patients. This relationship may be moderated by homogeneity of leadership styles, clarity of communication among leaders and healthcare providers, and work environment characteristics.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".