Factors related to preventive COVID-19 infection behaviors among people with mental illness

Kun Chia Chang, Carol Strong, Amir H. Pakpour, Mark D. Griffiths, Chung Ying Lin

研究成果: Article同行評審

16 引文 斯高帕斯(Scopus)

摘要

Background/Purpose: Because of the spread of novel coronavirus disease 2019 (COVID-2019), preventive COVID-19 infection behaviors become important for individuals, especially those who are vulnerable. The present study proposes a model to explain the preventive COVID-19 infection behaviors among people with mental illness in Taiwan. Methods: A cross-sectional design was carried out and 414 patients with mental illness (230 males [55.6%]; mean age = 46.32 [SD = 10.86]) agreed to participate in the study. All the participants completed the Preventive COVID-19 Infection Behaviors Scale, Self-Stigma Scale-Short, Believing COVID-19 Information Scale, Fear of COVID-19 Scale, and Depression Anxiety Stress Scale-21. Regression models and structural equation modeling (SEM) were applied to examine the factors associated with preventive COVID-19 infection behaviors. Results: Both regression models and SEM showed that trust in COVID-19 information sources (standardized coefficient [β] = 0.211 in regression; β = 0.194 in SEM) and fear of COVID-19 (β = −0.128 in regression; β = −0.223 in SEM) significantly explained preventive behaviors among individuals with mental illness. The SEM further showed that fear of COVID-19 was significantly explained by trust in COVID-19 information sources (β = 0.220) and self-stigma (β = 0.454). Conclusion: Based on the results, healthcare providers should help individuals with mental illness reduce self-stigma and fear of COVID-19 which would consequently improve their preventive COVID-19 infection behaviors. Moreover, improving trust in COVID-19 information sources for individuals with mental illness may be another method to improve their preventive behaviors.

原文English
頁(從 - 到)1772-1780
頁數9
期刊Journal of the Formosan Medical Association
119
發行號12
DOIs
出版狀態Published - 2020 十二月

All Science Journal Classification (ASJC) codes

  • Medicine(all)

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