Mitigating the Impact of Hospital Restructuring on Nurses
Bibliographic record
Abstract
BACKGROUND: A decade of North American hospital restructuring in the 1990s resulted in the layoff of thousands of nurses, leading to documented negative consequences for both nurses and patients. Nurses who remained employed experienced significant negative physical and emotional health, decreased job satisfaction, and decreased opportunity to provide quality care. OBJECTIVE: To develop a theoretical model of the impact of hospital restructuring on nurses and determine the extent to which emotionally intelligent nursing leadership mitigated any of these impacts. METHODS: The sample was drawn from all registered nurses in acute care hospitals in Alberta, Canada, accessed through their professional licensing body (N = 6,526 nurses; 53% response rate). Thirteen leadership competencies (founded on emotional intelligence) were used to create 7 data sets reflecting different leadership styles: 4 resonant, 2 dissonant, and 1 mixed. The theoretical model was then estimated 7 times using structural equation modeling and the seven data sets. RESULTS: Nurses working for resonant leaders reported significantly less emotional exhaustion and psychosomatic symptoms, better emotional health, greater workgroup collaboration and teamwork with physicians, more satisfaction with supervision and their jobs, and fewer unmet patient care needs than did nurses working for dissonant leaders. DISCUSSION: Resonant leadership styles mitigated the impact of hospital restructuring on nurses, while dissonant leadership intensified this impact. These findings have implications for future hospital restructuring, accountabilities of hospital leaders, the achievement of positive patient outcomes, the development of practice environments, the emotional health and well-being of nurses, and ultimately patient care outcomes.
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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 | 0.005 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".