Violence and aggression against nurses during the <scp>COVID</scp> ‐19 pandemic in Latin America. From the emerging leaders program of the Interamerican Society of Cardiology ( <scp>SIAC</scp> )
Bibliographic record
Abstract
INTRODUCTION: During the Coronavirus (COVID-19) pandemic, healthcare providers have overcome difficult experiences such as workplace violence. Nurses are particularly vulnerable to workplace violence. The objective of this study was to characterize violence and aggression against nurses during the COVID-19 pandemic in Latin America. METHODS: An electronic cross-sectional survey was conducted in 19 Latin American countries to characterize the frequency and type of violent actions against front-line healthcare providers. RESULTS: Of the original 3544 respondents, 16% were nurses (n = 567). The mean age was 39.7 ± 9.0 years and 79.6% (n = 2821) were women. In total, 69.8% (n = 2474) worked in public hospitals and 81.1% (n = 2874) reported working regularly with COVID-19 patients. Overall, about 68.6% (n = 2431) of nurses experienced at least one episode of workplace aggression during the pandemic. Nurses experienced weekly aggressions more frequently than other healthcare providers (45.5% versus 38.1%, p < .007). Nurses showed a trend of lower reporting rates against the acts of aggression suffered (p = .076). In addition, nurses were more likely to experience negative cognitive symptoms after aggressive acts (33.4% versus 27.8%, p = .028). However, nurses reported considering changing their work tasks less frequently compared to other healthcare providers after an assault event (p = .005). CONCLUSION: Workplace violence has been a frequent problem for all healthcare providers during COVID-19 pandemic in Latin America. Nurses were a particularly vulnerable subgroup, with higher rates of aggressions and cognitive symptoms and lower rate of complaints than other healthcare providers who suffered from workplace violence. It is imperative to develop strategies to protect this vulnerable group from aggressions during their tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".