Nurse educators’ workplace empowerment, burnout, and job satisfaction: testing Kanter's theory
Bibliographic record
Abstract
BACKGROUND: Empowerment has become an increasingly important factor in determining college nurse educator burnout, work satisfaction and performance in current restructured college nursing programmes in Canada. AIM: This paper reports a study to test a theoretical model specifying relationships among structural empowerment, burnout and work satisfaction. METHOD: A descriptive correlational survey design was used to test the model in a sample of 89 Canadian full-time college nurse educators employed in Canadian community colleges. The instruments used were the Conditions of Work Effectiveness Questionnaire, Job Activities Scale, Organizational Relationship Scale, Maslach Burnout Inventory Educator Survey and Global Job Satisfaction Questionnaire. RESULTS: College nurse educators reported moderate levels of empowerment in their workplaces as well as moderate levels of burnout and job satisfaction. Empowerment was significantly related to all burnout dimensions, most strongly to emotional exhaustion (r = -0.50) and depersonalization (r = -0.41). Emotional exhaustion was strongly negatively related to access to resources (r = -0.481, P = 0.0001) and support (r = -0.439, P = 0.0001). Multiple regression analysis revealed that 60% of the variance in perceptions of job satisfaction was explained by high levels of empowerment and low levels of emotional exhaustion [R(2) = 0.596, F (1, 86) = 25.01, P = 0.0001]. While both were significant predictors of perceived job satisfaction, empowerment was the stronger of the two (beta = 0.49). CONCLUSIONS: The results provide support for Kanter's organizational empowerment theory in the Canadian college nurse educator population. Higher levels of empowerment were associated with lower levels of burnout and greater work satisfaction. These findings have important implications for nurse education administrators.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".