Authentic leadership, performance, and job satisfaction: the mediating role of empowerment
Bibliographic record
Abstract
AIM: To report a study conducted to test a model linking authentic leadership of managers with nurses' perceptions of structural empowerment, performance, and job satisfaction. BACKGROUND: Authentic leadership has been proposed as the root element of effective leadership needed to build healthier work environments because there is special attention to the development of empowering leader-follower relationships. Although the influence of leadership style and empowerment on job satisfaction is well documented, there are few studies examining the influence of authentic leadership on nurses' empowerment and work outcomes. DESIGN: A non-experimental, predictive survey. METHOD: In 2008, a random sample of 600 Registered Nurses working in acute care hospitals across Ontario in Canada was surveyed. The final sample consisted of 280 (48% response rate) nurses. Variables were measured using the Authentic Leadership Questionnaire, Conditions of Work Effectiveness Questionnaire, Global Job Satisfaction Survey, and General Performance scale. The theoretical model was tested using structural equation modelling. RESULTS/OUTCOMES: The final model fit the data acceptably. Authentic leadership significantly and positively influenced staff nurses' structural empowerment, which in turn increased job satisfaction and self-rated performance. CONCLUSION: The results suggest that the more managers are seen as authentic, by emphasizing transparency, balanced processing, self-awareness and high ethical standards, the more nurses perceive they have access to workplace empowerment structures, are satisfied with their work, and report higher performance.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".