The relationship between the authentic leadership of nurses and structural empowerment: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To verify the relationship between authentic leadership of nurses and structural empowerment. METHOD: This is a systematic review carried out at the Virtual Health Library on the Journal Portal of the Coordination for the Improvement of Higher Education Personnel, Online System for the Search and Analysis of Medical Literature, Scientific Electronic Library Online and Science Direct/Embase, and consulted until April 2019. Studies with nurses, evidencing the relationship between authentic leadership and structural empowerment, published between 2012 and 2018 in Brazilian Portuguese, English or Spanish were included. RESULTS: Five studies were included, with variables other than structural empowerment: job satisfaction, burnout, bulling, mental health, performance, social capital, working environment, nurse retention, and quality of care. Authentic leadership showed a positive relationship with structural empowerment, improving engagement and job satisfaction, reducing burnout and increasing quality of care. CONCLUSION: Health institutions, in addition to Canada, where researchers on this topic are located, can invest in authentic leadership to improve structural empowerment by providing greater commitment from nurses, increased job satisfaction and quality of care provided.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".