The effect of violence prevention strategies on perceptions of workplace safety: A study of medical‐surgical and mental health nurses
Bibliographic record
Abstract
AIMS: To explore associations between specific violence prevention strategies and nurses' perceptions of workplace safety in medical-surgical and mental health settings. BACKGROUND: Workplace violence is on the rise globally. Nurses have the highest risk of violence due to the nature of their work. Violence rates are particularly high among USA and Canadian nurses. Although multiple violence prevention strategies are currently in place in public healthcare organizations in British Columbia, Canada, it is unknown whether these approaches are associated with nurses' perceptions of workplace safety. DESIGN: This is an exploratory correlational design using secondary data. METHODS: Using data obtained from a province-wide survey of nurses between March 2017 - January 2018, this study included 771 nurses from medical-surgical and 189 nurses from mental health settings. Data were analysed using ordinal logistic regressions. RESULTS: For medical-surgical and mental health nurses, greater perceptions of workplace safety were related to employers listening to them with respect to violence prevention strategies. Nurses in both settings were more likely to feel safe when they were not expected to physically intervene during a code white situation. Medical-surgical nurses were more likely to feel safe when code white incident reviews were conducted and fixed alarms were used. Mental health nurses were more likely to report feeling safe when they had enough properly trained code white responders on their unit. CONCLUSION: Nurse-employer engagement is critical to nurses' perceptions of feeling safe at work. Engagement opportunities include nurses' involvement in discussions about appropriate violence prevention strategies, collaborative debriefing after violent incidents and co-development and updates of patients' behavioural care plans.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".