Workplace empowerment, job satisfaction and job stress among Italian mental health nurses: an exploratory study
Bibliographic record
Abstract
AIM: The purpose of the present study was to investigate the relationship between staff nurses' structural empowerment, work stress and job satisfaction in two health care settings in Italy using Kanter's Empowerment Theory. BACKGROUND: With the current scarcity of economic resources and shortage of nurses, it is essential to empower nurses to perform at a high level to ensure high-quality patient care. Structural empowerment is a process that can optimize use of nursing skills and professional expertise, thereby increasing job satisfaction among nurses. METHOD: A convenience sample of 77 nursing staff employed in the Department of Mental Health in central Italy was used in this study (return rate 64%). RESULTS: Structural empowerment was significantly related to their job satisfaction (r = 0.506, P < 0.001), as was global empowerment (r = 0.62). Empowerment also had a significant negative relationship to nurses' work stress (r = -0.28, P < 0.05). CONCLUSION: The results of this study support Kanter's theory of structural empowerment in an Italian nursing sample--a previously unstudied population. IMPLICATIONS FOR NURSING MANAGEMENT: Organizational administration must make every effort to create organizational structures and systems that empower nurses to practice according to professional standards and optimize the use of their knowledge and expertise.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".