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Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China
BACKGROUND: With the spread of vaccines, more and more countries have controlled the outbreak of the COVID-19. In this post-epidemic era, these countries began to revive their economy. However, pollution remains in the environment, and people’s physical and psychological health has been under threat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466653/ https://www.ncbi.nlm.nih.gov/pubmed/34563147 http://dx.doi.org/10.1186/s12889-021-11823-4 |
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author | Ma, Rongyang Wu, Hong Deng, Zhaohua |
author_facet | Ma, Rongyang Wu, Hong Deng, Zhaohua |
author_sort | Ma, Rongyang |
collection | PubMed |
description | BACKGROUND: With the spread of vaccines, more and more countries have controlled the outbreak of the COVID-19. In this post-epidemic era, these countries began to revive their economy. However, pollution remains in the environment, and people’s physical and psychological health has been under threat due to some over-prevention behaviors. Instruments for governmental agencies to manage these behaviors are not yet available. This study aims to develop a measurement model to identify and measure the degree of over-prevention behaviors during the COVID-19 epidemic in China. METHODS: A survey online was conducted to collect cognition from 1528 Chinese people, including descriptions of various over-prevention behaviors defined by health authorities. Factor analyses were used to develop the measurement model and test its validity. Logistic regression analyses were conducted to explore demographic characteristics, indicating people who are inclined to exhibit over-prevention behaviors. RESULTS: Four main factors were extracted to develop the model (eigenvalue = 7.337, 3.157, 1.447, and 1.059, respectively). The overall reliability (Cronbach’s α = 0.900), the convergent (AVE > 0.5, CR > 0.8 for each factor) and discriminant validity is good. There is also a good internal consistency among these factors (Cronbach’s α = 0.906, 0.852, 0.882, and 0.763, respectively). In Factor 1, gender has a negative effect (Beta = − 0.294, P < 0.05, OR = 0.745), whereas employment has a positive effect. Workers in institutions exhibit the greatest effect (Beta = 0.855, P < 0.001, OR = 2.352). In Factor 2, employment has a negative effect, with workers in institutions exhibit the greatest role (Beta = − 0.963, P < 0.001, OR = 0.382). By contrast, education level has a positive effect (Beta = 0.430, P < 0.001, OR = 1.537). In Factor 3, age plays a negative role (Beta = − 0.128, P < 0.05, OR = 0.880). CONCLUSIONS: People show a discrepancy in the cognition toward various over-prevention behaviors. The findings may have implications for decision-makers to reduce the contradiction between the epidemic and economic revival via managing these behaviors. |
format | Online Article Text |
id | pubmed-8466653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84666532021-09-27 Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China Ma, Rongyang Wu, Hong Deng, Zhaohua BMC Public Health Research BACKGROUND: With the spread of vaccines, more and more countries have controlled the outbreak of the COVID-19. In this post-epidemic era, these countries began to revive their economy. However, pollution remains in the environment, and people’s physical and psychological health has been under threat due to some over-prevention behaviors. Instruments for governmental agencies to manage these behaviors are not yet available. This study aims to develop a measurement model to identify and measure the degree of over-prevention behaviors during the COVID-19 epidemic in China. METHODS: A survey online was conducted to collect cognition from 1528 Chinese people, including descriptions of various over-prevention behaviors defined by health authorities. Factor analyses were used to develop the measurement model and test its validity. Logistic regression analyses were conducted to explore demographic characteristics, indicating people who are inclined to exhibit over-prevention behaviors. RESULTS: Four main factors were extracted to develop the model (eigenvalue = 7.337, 3.157, 1.447, and 1.059, respectively). The overall reliability (Cronbach’s α = 0.900), the convergent (AVE > 0.5, CR > 0.8 for each factor) and discriminant validity is good. There is also a good internal consistency among these factors (Cronbach’s α = 0.906, 0.852, 0.882, and 0.763, respectively). In Factor 1, gender has a negative effect (Beta = − 0.294, P < 0.05, OR = 0.745), whereas employment has a positive effect. Workers in institutions exhibit the greatest effect (Beta = 0.855, P < 0.001, OR = 2.352). In Factor 2, employment has a negative effect, with workers in institutions exhibit the greatest role (Beta = − 0.963, P < 0.001, OR = 0.382). By contrast, education level has a positive effect (Beta = 0.430, P < 0.001, OR = 1.537). In Factor 3, age plays a negative role (Beta = − 0.128, P < 0.05, OR = 0.880). CONCLUSIONS: People show a discrepancy in the cognition toward various over-prevention behaviors. The findings may have implications for decision-makers to reduce the contradiction between the epidemic and economic revival via managing these behaviors. BioMed Central 2021-09-25 /pmc/articles/PMC8466653/ /pubmed/34563147 http://dx.doi.org/10.1186/s12889-021-11823-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Ma, Rongyang Wu, Hong Deng, Zhaohua Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China |
title | Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China |
title_full | Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China |
title_fullStr | Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China |
title_full_unstemmed | Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China |
title_short | Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China |
title_sort | identify and measure the degree of over-prevention behaviors in the post-covid-19 era in china |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466653/ https://www.ncbi.nlm.nih.gov/pubmed/34563147 http://dx.doi.org/10.1186/s12889-021-11823-4 |
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