Leader Empowering Behaviours, Staff Nurse Empowerment and Work Engagement/Burnout
Bibliographic record
Abstract
Efforts to improve nursing working conditions are critical to retaining nurses currently in the system and attracting newcomers to the profession (Laschinger et al. 2003b). The nurse leader's empowering behaviours can be pivotal in the way nurses react to their work environment. The purpose of this study was to test a model examining the relationship between nurse leaders' empowerment behaviours, perceptions of staff empowerment, areas of work life and work engagement using Kanter's theory of structural power in organizations. A cross-sectional correlational survey design tested the model in a random sample of 322 staff nurses in acute care hospitals across Ontario. Overall, staff nurses perceived their leaders' behaviours to be somewhat empowering and their work environment to be moderately empowering. Fifty-three percent reported severe levels of burnout. Leader empowering behaviour had an indirect effect on emotional exhaustion (burnout) through structural empowerment and overall fit in the six areas of work life. The final model statistics revealed a good fit (chi(2)=30.4, chi=3, chi=0.96, chi=0.95, chi=0.95). These findings suggest that the Leader's empowering behaviours can enhance person-job fit and prevent burnout. These findings have important implications in the current nursing shortage.
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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 | 0.007 |
| 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) | 0.002 | 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".