Workplace Violence and Harassment Against Emergency Medicine Residents
Bibliographic record
Abstract
INTRODUCTION: Several studies have shown that workplace violence in the emergency department (ED) is common. Residents may be among the most vulnerable staff, as they have the least experience with these volatile encounters. The goal for this study was to quantify and describe acts of violence against emergency medicine (EM) residents by patients and visitors and to identify perceived barriers to safety. METHODS: This cross-sectional survey study queried EM residents at multiple New York City hospitals. The primary outcome was the incidence of violence experienced by residents while working in the ED. The secondary outcomes were the subtypes of violence experienced by residents, as well as the perceived barriers to safety while at work. RESULTS: A majority of residents (66%, 78/119) reported experiencing at least one act of physical violence during an ED shift. Nearly all residents (97%, 115/119) experienced verbal harassment, 78% (93/119) had experienced verbal threats, and 52% (62/119) reported sexual harassment. Almost a quarter of residents felt safe "Occasionally," "Seldom" or "Never" while at work. Patient-based factors most commonly cited as contributory to violence included substance use and psychiatric disease. CONCLUSION: Self-reported violence against EM residents appears to be a significant problem. Incidence of violence and patient risk factors are similar to what has been found previously for other ED staff. Understanding the prevalence of workplace violence as well as the related systems, environmental, and patient-based factors is essential for future prevention efforts.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".