The Relationship of Empowerment and Selected Personality Characteristics to Nursing Job Satisfaction
Bibliographic record
Abstract
PURPOSE: This study reports on a secondary data analysis undertaken to better understand the determinants of job satisfaction for hospital nurses. Both workplace and personal factors can contribute to job satisfaction. THEORETICAL FRAMEWORK: Kanter's theory of structural empowerment and Spreitzer's theory of psychological empowerment explain logical outcomes of managerial efforts to create structural conditions of empowerment. Selected personal attributes were also considered. METHOD AND SAMPLE: Instruments used were 1) Conditions for Work Effectiveness Questionnaire; 2) psychological empowerment tool; 3) a mastery scale; 4) an achievement scale; and 5) a job satisfaction scale. The sample of 347 nurses (58% response rate) came from all specialty areas. RESULTS: Structural and psychological empowerment predicted 38% of the variance in job satisfaction. Achievement and mastery needs were not significant. Other personal attributes can be found in future research to improve job satisfaction. CONCLUSIONS: Through careful manipulation of the hospital environment, both structural and psychological empowerment can be increased, resulting in greater job and patient satisfaction and, ultimately, improved patient outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".