Authentic leadership, empowerment and burnout: a comparison in new graduates and experienced nurses
Bibliographic record
Abstract
AIM: To examine the effect of authentic leadership and structural empowerment on the emotional exhaustion and cynicism of new graduates and experienced acute-care nurses. BACKGROUND: Employee empowerment is a fundamental component of healthy work environments that promote nurse health and retention, and nursing leadership is key to creating these environments. METHOD: In a secondary analysis of data from two studies we compared the pattern of relationships among study variables in two Ontario groups: 342 new graduates with <2 years of experience and 273 nurses with more than 2 years of experience. RESULTS: A multi-group path analysis using Structural Equation Modelling indicated an acceptable fit of the final model (χ(2) = 17.52, df = 2, P < 0.001, CFI = 0.97, IFI = 0.97 and RMSEA = 0.11). Authentic leadership significantly and negatively influenced emotional exhaustion and cynicism through workplace empowerment in both groups. CONCLUSIONS: The authentic behaviour of nursing leaders was important to nurses' perceptions of structurally empowering conditions in their work environments, regardless of experience level, and ultimately contributed to lower levels of emotional exhaustion and cynicism. IMPLICATIONS FOR NURSING MANAGEMENT: Leadership training for nurse managers may help develop the empowering work environments required in today's health-care organizations in order to attract and retain nurses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".