Healthy Workplaces for Nurses: A Review of Lateral Violence and Evidence-Based Interventions
Bibliographic record
Abstract
Most nurses in the United States have experienced workplace bullying, also referred to as lateral violence. Workplace bullying is serious within professional nursing practice. These behaviors are often associated with detrimental consequences for nurses, their patients, and the greater health care organization. We performed a literature review to summarize recent studies on this pervasive yet persistent problem as well as evidence-based solutions. In environments where managers, supervisors, and administrators are unable or unwilling to address lateral violence, a common pattern is that offenders continue to target new employees and cause turmoil for workers and patients in healthcare settings. This work environment also causes harm and endangers patients. Although workplace bullying cannot be fixed with just one solution, there are different initiatives healthcare settings and educational institutions can implement to help prevent and eliminate workplace bullying, such as improving leadership training and interprofessional communication. Once these initiatives are put into practice, healthcare practices can start saving money, increasing employee satisfaction, retaining workers, and providing better healthcare services for their patients.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".