Effects of workplace incivility and workload on nurses’ work attitude: The mediating effect of burnout
Bibliographic record
Abstract
AIM: The study's aim was to examine how workplace incivility and workload influence nurses' work attitudes (turnover intention, job satisfaction, and organizational commitment) using the stress-strain-outcome framework. BACKGROUND: There is a lack of comprehensive research on the combined effects of workplace incivility and workload on nurses' work attitudes. INTRODUCTION: Two workplace stressors, incivility and workload, were hypothesized to lead to burnout, which in turn influences nurses' work attitudes. METHODS: A cross-sectional, descriptive correlational study was conducted. Survey data were collected from 1,255 direct care nurses with a minimum of 6 months' nursing experiences in 34 general hospitals across Korea. Structural equation modeling was used to test the hypothesized model. This study is reported using the STROBE checklist. RESULTS: As hypothesized, both workplace incivility and workload increased burnout. Heightened burnout correlated with increased turnover intention, lowered job satisfaction, and reduced organizational commitment. While workplace incivility impacted these outcomes both directly and indirectly via its effect on burnout, workload influenced the outcomes solely through burnout. CONCLUSION: The study's findings are based on one, nonrandomized sample of nurses working at South Korean hospitals. Despite such study limitations, the study findings support the adverse impact of two workplace stressors on burnout and nurses' work attitudes. IMPLICATIONS FOR NURSING: Evidence-informed interventions for both workplace stressors include training programs, clear policy guidelines, open communication channels, and supportive work environments. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Zero tolerance and equity, diversity and inclusivity policies to promote workplace civility must be enforced. Workload needs to be patient-centered, ensuring a "fit" between patient needs and nurse staffing.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".