The impact of workplace violence on medical-surgical nurses’ health outcome: A moderated mediation model of work environment conditions and burnout using secondary data
Bibliographic record
Abstract
BACKGROUND: Workplace violence is a prevalent phenomenon in healthcare and nurses are particularly at risk from workplace violence due to the nature of their work or inadequacies within their work environments. Although workplace violence is known to have serious negative implications for nurses, patients and the larger healthcare system, the mechanism through which it functions is less clear. OBJECTIVE: The purpose of this study is to examine whether work environment conditions moderate the mediating effect that burnout has on the relationship between workplace violence and three health outcomes. DESIGN: A secondary analysis of cross-sectional correlational survey data was conducted. SETTING: The study took place in British Columbia, Canada. PARTICIPANTS: 537 medical-surgical nurses were included in the study. METHODS: Survey data were analyzed using moderated mediation regressions with the PROCESS macro on SPSS. RESULTS: Burnout mediated the relationship between workplace violence and health outcomes including musculoskeletal injuries, anxiety disorders and sleep disturbances. Work environment conditions moderated the direct relationship between workplace violence and burnout; and the indirect relationship between workplace violence and the three health outcomes. In healthier work environments, workplace violence was more strongly related to increased reports of burnout, musculoskeletal injuries, anxiety disorders and sleep disturbances compared to less healthy work environments. CONCLUSION: Nurses in healthier work environments may not expect workplace violence, and they may be at more burnout risk than nurses in less healthy environments who have normalized unsafe work conditions. Violence may be the new 'reality shock' for nurses.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".