Perceptions of leadership style between nurse managers and their staff in Eastern Saudi Arabia: A cross sectional survey
Bibliographic record
Abstract
Background: Understanding nurses’ perceptions about their nurse managers is a crucial element to consider as it helps in the performance of the nurse managers and retention of nurses and reflects the nature of a competent workforce in achieving the organisational goals.Objective: To explore if there is a difference in perceptions of leadership style between nurse managers and their staff and discuss why this occurs.Methods: A cross-sectional descriptive comparative research design was used.Results: Nurse managers rated themselves as using transformational and transactional factors more than the nurses perceived them utilising these various leadership styles. Nurse managers, however, rated themselves lower than nurses in both laissez-faire and management-by-exception-passive.Discussion: The leadership style preferred by the followers is consistently rated higher than the leadership style that their leaders are utilising. Formation of accurate self-perception is a delicate process, especially for people in management positions. Bias in higher self-ratings may occur for several reasons, including gender, which forms the basis of this discussion.Conclusions: The results highlight the need for nurse managers to reflect on their practices and find new ways to enhance their leadership styles.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".