The Impact of Staff Nurse Empowerment on Person-Job Fit and Work Engagement/Burnout
Bibliographic record
Abstract
Workplace empowerment is an important strategy for creating positive nursing work environments in a time of a severe nursing shortage. The purpose of this study was to test a model linking staff nurse perceptions of empowerment to their perceived fit with 6 areas of work life and work engagement/burnout using Kanter's work empowerment theory. We tested the model in a cross-sectional correlational survey design with a random sample of 322 staff nurses in acute care hospitals across Ontario. Overall, staff nurses perceived their work environment to be only somewhat empowering. Fifty-three percent reported severe levels of burnout. Overall empowerment had an indirect effect on emotional exhaustion (burnout) through nurses' perceived fit in 6 areas of work life. The final model fit statistics revealed a good fit (chi2 = 32.4, df = 13, GFI = 0.97, IFI = 0.97, CFI = 0.97, RMSEA = 0.07). 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 | 0.003 | 0.016 |
| 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 | 0.002 |
| 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".