The influence of authentic leadership and areas of worklife on work engagement of registered nurses
Bibliographic record
Abstract
AIM: To examine the relationships among nurses' perceptions of nurse managers' authentic leadership, nurses' overall person-job match in the six areas of worklife and their work engagement. BACKGROUND: Reports have highlighted the impact of demanding and unsupportive work environments on nurses' wellbeing, resulting in a need for strong nursing leadership to build sustainable and healthier work environments. METHODS: A secondary analysis of data collected from a non-experimental, predictive design survey of a random sample of 280 registered nurses working in acute care hospitals was conducted. RESULTS: An overall person-job match in the six areas of worklife fully mediated the relationship between authentic leadership and work engagement. Further, authentic leadership, overall person-job match in the six areas of worklife and years of nursing experience explained 33.1% of the variance in work engagement. CONCLUSIONS: Findings suggest that nurses who work for managers demonstrating higher levels of authentic leadership report a greater overall person-job match in the six areas of worklife and greater work engagement. IMPLICATIONS FOR NURSING MANAGEMENT: As nurse managers' play a key role in promoting work engagement among nurses, authentic leadership development for nurse managers focusing on self-awareness, relational transparency, ethics and balanced processing would be beneficial.
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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 | 0.013 |
| 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 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".