Exploring the leadership styles of nurse managers in Hail, Saudi Arabia: A cross-sectional analysis
Bibliographic record
Abstract
Objective: Leadership’s impact in healthcare is crucial as it notably shapes the experiences and performance of nursing staff. This study explores the dominant leadership styles among nurse managers in Hail, Saudi Arabia, as experienced by their nursing staff. The inquiry also examines how these leadership approaches directly influence critical organizational outcomes, including leader effectiveness, employee satisfaction, and staff’s willingness to exert extra effort.Methods: A cross-sectional design involving participants recruited via convenience sampling from four government hospitals in Hail, Saudi Arabia. Data were collected using the 45-item Likert-type Multifactor Leadership Questionnaire (MLQ) and analyzed using SPSS Statistics.Results: Among the 372 nurses analyzed, transformational leadership (2.56 ± 0.75) significantly outscored other styles (p < .001) and had the highest correlation with the leadership outcomes of effectiveness, extra effort, and satisfaction (R2 of 0.828, 0.786, and 0.760, respectively) compared to the transactional and laissez-faire leadership styles. Additionally, linear regression analysis revealed that transformational leadership explained 69% of effectiveness, 61.7% of extra effort, and 58% of satisfaction variances. Within the transformational framework, “inspirational motivation” strongly correlated with positive outcomes.Conclusions: This study emphasizes transformational leadership’s essential role in healthcare, urging nurse leaders to embrace this style, with a focus on strategies that boost motivation. It also recommends that healthcare institutions initiate targeted programs to develop their leaders’ transformational leadership 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".