Comparación de la violencia y agresiones sufridas por el personal de salud durante la pandemia de COVID-19 en Argentina y el resto de Latinoamérica
Bibliographic record
Abstract
OBJECTIVES.: Motivation for the study. The COVID-19 pandemic has caused profound repercussions at different socio-environmental levels. Its impact on violence against healthcare team workers in Argentina has not been well documented. Main findings. The present study evidenced high rates of aggression, particularly verbal aggression. In addition, almost half of the participants reported having suffered these events on a weekly basis. All participants who experienced violence reported having experienced post-event symptoms, and up to one-third reported having considered changing their profession after these acts. Implications. It is imperative to take action to prevent acts of violence against health personnel, or to mitigate its impact on the victims. . To explore the frequency and impact of violence against healthcare workers in Argentina and to compare it with the rest of their Latin American peers during the COVID-19 pandemic. MATERIALS AND METHODS.: A cross-sectional study was conducted by applying an electronic survey on Latin American medical and non-medical personnel who carried out health care tasks since March 2020. We used Poisson regression to estimate crude (PR) and adjusted (aPR) Prevalence Ratios with their respective 95% confidence intervals. RESULTS.: A total of 3544 participants from 19 countries answered the survey; 1992 (56.0%) resided in Argentina. Of these, 62.9% experienced at least one act of violence; 97.7% reported verbal violence and 11.8% physical violence. Of those who were assaulted, 41.5% experienced violence at least once a week. Health personnel from Argentina experienced violence more frequently than those from other countries (62.9% vs. 54.6%, p<0.001), and these events were more frequent and stressful (p<0.05). In addition, Argentinean health personnel reported having considered changing their healthcare tasks and/or desired to leave their profession more frequently (p<0.001). In the Poisson regression, we found that participants from Argentina had a higher prevalence of violence than health workers from the region (14.6%; p<0.001). CONCLUSIONS.: There was a high prevalence of violence against health personnel in Argentina during the COVID-19 pandemic. These events had a strong negative impact on those who suffered them. Our data suggest that violence against health personnel may have been more frequent in Argentina than in other regions of the continent.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".