Relationships among authentic leadership, manager incivility and trust in the manager
Bibliographic record
Abstract
Purpose This study aims to examine the relationships among authentic leadership of managers and new graduate nurses’ (NGNs) experience of manager incivility and their degree of trust in their managers. Design/methodology/approach A secondary analysis of data using a non-experimental, correlational design was undertaken. From 2012 to 2013, 3,743 surveys were mailed to NGNs eligible for the study, and 1,020 returned completed questionnaires for a response rate of 27.3 per cent. The hypotheses were tested using hierarchical multiple linear regression. Findings Authentic leadership had a negative relationship with manager incivility, which in turn was negatively related to trust in the manager, and overall the model accounted for 59.9 per cent of the variance in trust. Authentic leadership was positively associated with trust in the manager. Originality/value Findings supported that authentic leadership may be an effective approach to enhance manager–nurse interactions because authentic managers are less likely to display uncivil behavior, which diminishes trust. Findings may be useful to inform the development of positive and respectful work environments and the everyday practice of nurse managers.
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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 | 0.003 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".