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Evaluation of Covid-19 pandemic on components of social and mental health using machine learning, analysing United States data in 2020

Dados Bibliográficos

ID15519391
AutoresSeyed-Ali Sadegh-Zadeh (0000-0001-6197-3410, University of Staffordshire), Mahboobe Bahrami (0000-0002-6083-9858, Isfahan University of Medical Sciences), Amirreza Najafi (0000-0002-8617-5463, Tarbiat Modares University), Meisam Asgari-Ahi (University of Tehran), Russell Campion (0000-0001-5566-876X, University of Staffordshire), Amir M Hajiyavand (0000-0002-6666-7866, University of Birmingham, autor correspondente)
Ano2022
Volume13
Páginas933439-933439
Data de publicação2022-08-08
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Psychiatry (JOURNAL)
Identificadores do periódicoISSN: 1664-0640 • E-ISSN: 1664-0640
EditoraFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2022.933439
PMID36003977
OpenAlexW4290546600
IdiomaEN
Referências citadas37

This study estimates the occurrence of mental illness among adults with and without a history of mental disease during the COVID-19 preventative limitations. With the persistence of quarantine limitations, the prevalence of psychiatric issues grew. In the third survey, which was done under quarantine or house restrictions, mental health problems and acute stress reactions were substantially greater than in the prior two surveys. The findings of the study reveal that more focused messaging and support are needed for those with a history of mental illness throughout the implementation of restrictions

Coronavirus disease 2019 (COVID-19 · Disease · Environmental health · Logistic regression · Machine learning · Mental health · Mental illness · Pandemic · Psychiatry · Quality (philosophy · Sample (material · Computer Science · COVID-19 and Mental Health · Digital Mental Health Interventions · Medicine · Mental Health Research Topics · Psychology · Gerontology

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