A Systematic Review: Effectiveness of Interventions to De-escalate Workplace Violence against Nurses in Healthcare Settings
Bibliographic record
Abstract
Workplace violence (WPV) is an increasing cause of concern around the globe, and healthcare organizations are no exception. Nurses may be subject to all kinds of workplace violence due to their frontline position in healthcare settings. The purpose of this systematic review is to identify and consider different interventions that aim to decrease the magnitude/prevalence of workplace violence against nurses. The standard method by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA, 2009) has been used to collect data and assess methodological quality. Altogether, twenty-six studies are included in the review. The intervention procedures they report on can be grouped into three categories: stand-alone trainings designed to educate nurses; more structured education programs, which are broader in scope and often include opportunities to practice skills learned during the program; multicomponent interventions, which often include organizational changes, such as the introduction of workplace violence reporting systems, in addition to workplace violence training for nurses. By comparing the findings, a clear picture emerges; while standalone training and structured education programs can have a positive impact, the impact is unfortunately limited. In order to effectively combat workplace violence against nurses, healthcare organizations must implement multicomponent interventions, ideally involving all stakeholders.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".