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A fast online questionnaire for screening mental illness symptoms during the COVID-19 pandemic.
Chen, Fang; Yan, Weizheng; Calhoun, Vince D; Yu, Linzhen; Chen, Lili; Hao, Xiaoyi; Zheng, Leilei.
Afiliación
  • Chen F; Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
  • Yan W; Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
  • Calhoun VD; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA.
  • Yu L; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, USA.
  • Chen L; Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
  • Hao X; Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
  • Zheng L; Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Transl Psychiatry ; 12(1): 311, 2022 08 04.
Article en En | MEDLINE | ID: mdl-35927227
ABSTRACT
The COVID-19 pandemic has caused massive effects on the situation of public mental health. A fast online questionnaire for screening and evaluating mental symptoms is urgent. In this work, we developed a new 19-item self-assessment Fast Screen Questionnaire for Mental Illness Symptoms (FSQ-MIS) to quickly identify mental illness symptoms. The FSQ-MIS was validated on a total of 3828 young adult mental disorder patients and 984 healthy controls. We applied principal component analysis (PCA), receiver operating characteristic (ROC) curve, and general log-linear analysis (GLA) to evaluate the construct and parallel validity. Results demonstrate that the proposed FSQ-MIS shows high test-retest reliability (0.852) and split-half reliability (0.844). Six factors obtained using PCA explained 54.3% of the variance and showed high correlations with other widely used scales. The ROC results (0.716-0.983) revealed high criterion validity of FSQ-MIS. GLA demonstrated the advantage of FSQ-MIS in predicting anxiety and depression prevalence in COVID-19, supporting the efficiency of FSQ-MIS as a tool for research and clinical practice.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: COVID-19 / Trastornos Mentales Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Adult / Humans Idioma: En Revista: Transl Psychiatry Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: COVID-19 / Trastornos Mentales Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Adult / Humans Idioma: En Revista: Transl Psychiatry Año: 2022 Tipo del documento: Article País de afiliación: China