Workplace Violence in Healthcare Settings: A Scoping Review of Guidelines and Systematic Reviews
Bibliographic record
Abstract
Workplace violence in healthcare settings is alarmingly common and represents significant financial and human cost. The aim of this scoping review was to identify and summarize evidence on strategies to prevent and/or manage workplace violence in healthcare settings. Searches were limited to evidence-based clinical practice guidelines and systematic reviews published between 2015 and 2021. Multiple databases were searched and screened. Quality of the included guidelines and reviews was also assessed. Three guidelines and 33 systematic reviews were included. Both the Occupational Safety and Health Administration 2015 and Registered Nurses’ Association of Ontario 2019 guidelines provided useful recommendations for building a comprehensive prevention program. Evidence-based risk assessment, prevention and management, and education and training are all central components. Regular reassessment and adjustment is required. Included reviews (n = 33) were grouped into five main categories: violence toward nurses (n = 10); violence toward healthcare workers in general (n = 8); violence in the emergency department (n = 5); violence related to mental health (n = 5); and measurement related to workplace violence (n = 5). Multicomponent interventions were often more effective than those applied in isolation. We found consistent support for certain strategies including education and training, post-incident debriefing, multidisciplinary rapid response teams, and environmental modifications; however, the strength of evidence and certainty of conclusions were limited across reviews. This scoping review found that strong leadership that cultivates and enforces a culture of inclusivity, support, and respect is a prerequisite for a successful workplace violence prevention program. Rigorous comparative effectiveness research testing interventions are needed.
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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 | 0.037 | 0.167 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.037 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".