Effects of Leadership and Span of Control on Nurses' Job Satisfaction and Patient Satisfaction
Bibliographic record
Abstract
BACKGROUND: Hospital restructuring has resulted in nurse managers' having direct responsibility for a greatly expanded number of units and staff. However, very little research has examined the impact of these larger spans of control on nurse and patient outcomes. OBJECTIVE: This study examined the relationships between leadership style, span of control, nurses' job satisfaction and patient satisfaction, as well as the moderating effect of span of control on the relationship between leadership style and the two outcomes. METHODS: The study was conducted at seven teaching and community hospitals with a sample of 51 units, 41 nurse managers, 717 nurses and 680 patients. Data analyses included multiple regression and hierarchical linear modelling. RESULTS: The study findings provided support for the theoretical relationships among leadership style, span of control, nurse job satisfaction and patient satisfaction. In addition, the results showed that higher spans of control decreased the positive effects of transformational and transactional leadership styles on job satisfaction and patient satisfaction, and increased the negative effects of management by exception and laissez-faire leadership styles on job satisfaction. DISCUSSION: Leadership matters, and certain leadership styles, particularly transformational, are better than others. Span of control also matters: the wider the span, the lower the nurses' job satisfaction and patient satisfaction. However, as spans of control increase in size, no leadership style, even transformational, can overcome the negative effects.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".