Workplace empowerment, incivility, and burnout: impact on staff nurse recruitment and retention outcomes
Bibliographic record
Abstract
AIM: The aim of this study was to examine the influence of empowering work conditions and workplace incivility on nurses' experiences of burnout and important nurse retention factors identified in the literature. BACKGROUND: A major cause of turnover among nurses is related to unsatisfying workplaces. Recently, there have been numerous anecdotal reports of uncivil behaviour in health care settings. METHOD: We examined the impact of workplace empowerment, supervisor and coworker incivility, and burnout on three employee retention outcomes: job satisfaction, organizational commitment, and turnover intentions in a sample of 612 Canadian staff nurses. RESULTS: Hierarchical multiple linear regression analyses revealed that empowerment, workplace incivility, and burnout explained significant variance in all three retention factors: job satisfaction (R(2) = 0.46), organizational commitment (R(2) = 0.29) and turnover intentions (R(2) = 0.28). Empowerment, supervisor incivility, and cynicism most strongly predicted job dissatisfaction and low commitment (P < 0.001), whereas emotional exhaustion, cynicism, and supervisor incivility most strongly predicted turnover intentions. CONCLUSIONS: In our study, nurses' perceptions of empowerment, supervisor incivility, and cynicism were strongly related to job satisfaction, organizational commitment, and turnover intentions. IMPLICATIONS FOR NURSING MANAGEMENT: Managerial strategies that empower nurses for professional practice may be helpful in preventing workplace incivility, and ultimately, burnout.
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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 | 0.010 |
| 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 | 0.000 |
| 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".