Association of quality of nursing care with violence load, burnout, and listening climate
Bibliographic record
Abstract
BACKGROUND: Violence against nurses is common. Previous research has recommended further development of the measurement of violence against nurses and integration of the individual and ward-related factors that contribute to violence against hospital nurses. This study was designed to address these issues by investigating the associations between violence, the listening climate of hospital wards, professional burnout, and perceived quality of care. For this purpose, we used a new operationalization of the violence concept. METHODS: We sought nurses to participate in the study through social media which yielded 765 nurses working in various healthcare systems across Israel who volunteered to complete a self-administered online questionnaire. 80% of the sample were hospital nurses, and 84.7% were female. The questionnaire included validated measures of burnout, listening climate, and quality of care. Instead of using the traditional binary measure of exposure to violence to capture the occurrence and comprehensive impact of violence, this study measured the incremental load of violence to which nurses are subjected. RESULTS: There were significant correlations between violence load and perceived quality of care and between constructive and destructive listening climates and quality of care. Violence load contributed 14% to the variance of burnout and 13% to the variance of perceived quality of care. The ward listening climate moderated the relationship between burnout and quality of care. CONCLUSIONS: The results of this study highlight the impact of violence load among nurses and the ward listening climate on the development of burnout and on providing quality care. The findings call upon policymakers to monitor violence load and allocate resources to foster supportive work environments to enhance nurse well-being and improve patient care 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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".