A Realist Review of Violence Prevention Education in Healthcare
Bibliographic record
Abstract
Violence from patients and visitors towards healthcare workers is an international concern affecting the safety and health of workers, quality of care, and healthcare system sustainability. Although the predominant intervention has been violence prevention (VP) education for healthcare workers, evaluating its effectiveness is challenging due to underreporting of violence and the inherent complexity of both violence and the health care environment. This review utilized a theory-driven, realist approach to synthesize and analyze a wide range of academic and grey literature to identify explanations of how and why VP education makes a difference in preventing violence and associated physical and psychological injury to workers. The review confirmed the importance of positioning VP education as part of a VP strategy, and consideration of the contexts that influence successful application of VP knowledge and skills. Synthesis and analysis of patterns of evidence across 64 documents resulted in 11 realist explanations of VP education effectiveness. Examples include education specific to clinical settings, unit-level modeling and mentoring support, and support of peers and supervisors during violent incidents. This review informs practical program and policy decisions to enhance VP education effectiveness in healthcare settings.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".