Violence Against Nurses Working in US Emergency Departments
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to investigate emergency nurses' experiences and perceptions of violence from patients and visitors in US emergency departments (EDs). BACKGROUND: The ED is a particularly vulnerable setting for workplace violence, and because of a lack of standardized measurement and reporting mechanisms for violence in healthcare settings, data are scarce. METHODS: Registered nurse members (n = 3,465) of the Emergency Nurses Association participated in this cross-sectional study by completing a 69-item survey. RESULTS: Approximately 25% of respondents reported experiencing physical violence more than 20 times in the past 3 years, and almost 20% reported experiencing verbal abuse more than 200 times during the same period. Respondents who experienced frequent physical violence and/or frequent verbal abuse indicated fear of retaliation and lack of support from hospital administration and ED management as barriers to reporting workplace violence. CONCLUSION: Violence against ED nurses is highly prevalent. Precipitating factors to violent incidents identified by respondents is consistent with the research literature; however, there is considerable potential to mitigate these factors. Commitment from hospital administrators, ED managers, and hospital security is necessary to facilitate improvement and ensure a safer workplace for ED nurses.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".