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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».