The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users
Identifieur interne : 000B83 ( Ncbi/Merge ); précédent : 000B82; suivant : 000B84The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users
Auteurs : Sijia Li [République populaire de Chine] ; Yilin Wang [République populaire de Chine] ; Jia Xue ; Nan Zhao ; Tingshao ZhuSource :
- International Journal of Environmental Research and Public Health [ 1661-7827 ] ; 2020.
Abstract
COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people’s mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.
Url:
DOI: 10.3390/ijerph17062032
PubMed: 32204411
PubMed Central: 7143846
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China;<email>lisj@psych.ac.cn</email> (S.L.);<email>1613118@mail.nankai.edu.cn</email> (Y.W.)</nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af2-ijerph-17-02032">Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Wang, Yilin" sort="Wang, Yilin" uniqKey="Wang Y" first="Yilin" last="Wang">Yilin Wang</name><affiliation><nlm:aff id="af1-ijerph-17-02032">Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;<email>lisj@psych.ac.cn</email> (S.L.);<email>1613118@mail.nankai.edu.cn</email> (Y.W.)</nlm:aff></affiliation><affiliation wicri:level="1"><nlm:aff id="af3-ijerph-17-02032">Department of Psychology, Nankai University, Tianjin 300071, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Psychology, Nankai University, Tianjin 300071</wicri:regionArea><placeName><settlement type="city">Tianjin</settlement></placeName></affiliation></author><author><name sortKey="Xue, Jia" sort="Xue, Jia" uniqKey="Xue J" first="Jia" last="Xue">Jia Xue</name><affiliation><nlm:aff id="af4-ijerph-17-02032">Factor Inwentash Faculty of Social Work, University of Toronto, Toronto M5S 1A1, Canada;<email>jia.xue@utoronto.ca</email></nlm:aff></affiliation></author><author><name sortKey="Zhao, Nan" sort="Zhao, Nan" uniqKey="Zhao N" first="Nan" last="Zhao">Nan Zhao</name><affiliation><nlm:aff id="af1-ijerph-17-02032">Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;<email>lisj@psych.ac.cn</email> (S.L.);<email>1613118@mail.nankai.edu.cn</email> (Y.W.)</nlm:aff></affiliation></author><author><name sortKey="Zhu, Tingshao" sort="Zhu, Tingshao" uniqKey="Zhu T" first="Tingshao" last="Zhu">Tingshao Zhu</name><affiliation><nlm:aff id="af1-ijerph-17-02032">Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;<email>lisj@psych.ac.cn</email> (S.L.);<email>1613118@mail.nankai.edu.cn</email> (Y.W.)</nlm:aff></affiliation></author></analytic><series><title level="j">International Journal of Environmental Research and Public Health</title><idno type="ISSN">1661-7827</idno><idno type="eISSN">1660-4601</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people’s mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Huang, C L" uniqKey="Huang C">C.L. Huang</name></author><author><name sortKey="Wang, Y M" uniqKey="Wang Y">Y.M. Wang</name></author><author><name sortKey="Li, X W" uniqKey="Li X">X.W. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Int J Environ Res Public Health</journal-id><journal-id journal-id-type="iso-abbrev">Int J Environ Res Public Health</journal-id><journal-id journal-id-type="publisher-id">ijerph</journal-id><journal-title-group><journal-title>International Journal of Environmental Research and Public Health</journal-title></journal-title-group><issn pub-type="ppub">1661-7827</issn><issn pub-type="epub">1660-4601</issn><publisher><publisher-name>MDPI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32204411</article-id><article-id pub-id-type="pmc">7143846</article-id><article-id pub-id-type="doi">10.3390/ijerph17062032</article-id><article-id pub-id-type="publisher-id">ijerph-17-02032</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Li</surname><given-names>Sijia</given-names></name><xref ref-type="aff" rid="af1-ijerph-17-02032">1</xref><xref ref-type="aff" rid="af2-ijerph-17-02032">2</xref></contrib><contrib contrib-type="author"><name><surname>Wang</surname><given-names>Yilin</given-names></name><xref ref-type="aff" rid="af1-ijerph-17-02032">1</xref><xref ref-type="aff" rid="af3-ijerph-17-02032">3</xref></contrib><contrib contrib-type="author"><name><surname>Xue</surname><given-names>Jia</given-names></name><xref ref-type="aff" rid="af4-ijerph-17-02032">4</xref></contrib><contrib contrib-type="author"><name><surname>Zhao</surname><given-names>Nan</given-names></name><xref ref-type="aff" rid="af1-ijerph-17-02032">1</xref><xref rid="c1-ijerph-17-02032" ref-type="corresp">*</xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0003-0020-3812</contrib-id><name><surname>Zhu</surname><given-names>Tingshao</given-names></name><xref ref-type="aff" rid="af1-ijerph-17-02032">1</xref><xref rid="c1-ijerph-17-02032" ref-type="corresp">*</xref></contrib></contrib-group><aff id="af1-ijerph-17-02032"><label>1</label>Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;<email>lisj@psych.ac.cn</email> (S.L.);<email>1613118@mail.nankai.edu.cn</email> (Y.W.)</aff><aff id="af2-ijerph-17-02032"><label>2</label>Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China</aff><aff id="af3-ijerph-17-02032"><label>3</label>Department of Psychology, Nankai University, Tianjin 300071, China</aff><aff id="af4-ijerph-17-02032"><label>4</label>Factor Inwentash Faculty of Social Work, University of Toronto, Toronto M5S 1A1, Canada;<email>jia.xue@utoronto.ca</email></aff><author-notes><corresp id="c1-ijerph-17-02032"><label>*</label>Correspondence: <email>zhaonan@psych.ac.cn</email> (N.Z.); <email>tszhu@psych.ac.cn</email> (T.Z.)</corresp></author-notes><pub-date pub-type="epub"><day>19</day><month>3</month><year>2020</year></pub-date><pub-date pub-type="ppub"><month>3</month><year>2020</year></pub-date><volume>17</volume><issue>6</issue><elocation-id>2032</elocation-id><history><date date-type="received"><day>28</day><month>2</month><year>2020</year></date><date date-type="accepted"><day>17</day><month>3</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 by the authors.</copyright-statement><copyright-year>2020</copyright-year><license license-type="open-access"><license-p>Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>).</license-p></license></permissions><abstract><p>COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people’s mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.</p></abstract><kwd-group><kwd>public health emergencies</kwd><kwd>word frequency analysis</kwd><kwd>mental health</kwd><kwd>emotion</kwd><kwd>cognition</kwd></kwd-group></article-meta></front><floats-group><fig id="ijerph-17-02032-f001" orientation="portrait" position="float"><label>Figure 1</label><caption><p>Procedures of feature extraction from online Weibo data and psychological indicator predicted by dynamic features.</p></caption><graphic xlink:href="ijerph-17-02032-g001"></graphic></fig><table-wrap id="ijerph-17-02032-t001" orientation="portrait" position="float"><object-id pub-id-type="pii">ijerph-17-02032-t001_Table 1</object-id><label>Table 1</label><caption><p>Demographic characteristics of selected participants.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1"></th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1"></th><th align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1" colspan="1"><italic>n</italic> (%)</th></tr></thead><tbody><tr><td rowspan="2" align="center" valign="middle" colspan="1">Gender</td><td align="center" valign="middle" rowspan="1" colspan="1">male</td><td align="center" valign="middle" rowspan="1" colspan="1">4507 (25.23)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">female</td><td align="center" valign="middle" rowspan="1" colspan="1">13,358 (74.77)</td></tr><tr><td rowspan="6" align="center" valign="middle" style="border-top:solid thin" colspan="1">Age</td><td align="center" valign="middle" style="border-top:solid thin" rowspan="1" colspan="1">–9</td><td align="center" valign="middle" style="border-top:solid thin" rowspan="1" colspan="1">110 (0.62)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">10–19</td><td align="center" valign="middle" rowspan="1" colspan="1">20 (0.11)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">20–29</td><td align="center" valign="middle" rowspan="1" colspan="1">2035 (11.39)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">30–39</td><td align="center" valign="middle" rowspan="1" colspan="1">1598 (8.94)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">40–</td><td align="center" valign="middle" rowspan="1" colspan="1">393 (2.20)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">missing data</td><td align="center" valign="middle" rowspan="1" colspan="1">13,709 (76.74)</td></tr><tr><td rowspan="3" align="center" valign="middle" style="border-top:solid thin" colspan="1">Region of location</td><td align="center" valign="middle" style="border-top:solid thin" rowspan="1" colspan="1">Eastern China</td><td align="center" valign="middle" style="border-top:solid thin" rowspan="1" colspan="1">13,925 (77.95)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Central China</td><td align="center" valign="middle" rowspan="1" colspan="1">1644 (9.20)</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Western China</td><td align="center" valign="middle" rowspan="1" colspan="1">2296 (12.85)</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"></td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"></td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Total</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"></td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">17,865 (100)</td></tr></tbody></table></table-wrap><table-wrap id="ijerph-17-02032-t002" orientation="portrait" position="float"><object-id pub-id-type="pii">ijerph-17-02032-t002_Table 2</object-id><label>Table 2</label><caption><p>Word frequency analysis before and after 20 January.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin" rowspan="1" colspan="1"></th><th colspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">T-Before</th><th colspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">T-After</th><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1">t</th><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1">df</th><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1"><italic>p</italic></th></tr><tr><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"></th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">M</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">SD</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">M</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">SD</th></tr></thead><tbody><tr><td colspan="3" align="center" valign="middle" rowspan="1"><bold>Words of emotions</bold></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Positive emotion</td><td align="center" valign="middle" rowspan="1" colspan="1">2.58</td><td align="center" valign="middle" rowspan="1" colspan="1">1.46</td><td align="center" valign="middle" rowspan="1" colspan="1">2.86</td><td align="center" valign="middle" rowspan="1" colspan="1">1.47</td><td align="center" valign="middle" rowspan="1" colspan="1">−24.411</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Negative emotion</td><td align="center" valign="middle" rowspan="1" colspan="1">0.71</td><td align="center" valign="middle" rowspan="1" colspan="1">0.63</td><td align="center" valign="middle" rowspan="1" colspan="1">0.79</td><td align="center" valign="middle" rowspan="1" colspan="1">0.59</td><td align="center" valign="middle" rowspan="1" colspan="1">−15.273</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Anxiety</td><td align="center" valign="middle" rowspan="1" colspan="1">0.09</td><td align="center" valign="middle" rowspan="1" colspan="1">0.17</td><td align="center" valign="middle" rowspan="1" colspan="1">0.12</td><td align="center" valign="middle" rowspan="1" colspan="1">0.17</td><td align="center" valign="middle" rowspan="1" colspan="1">−15.294</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Anger</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.19</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.26</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.19</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.23</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−0.347</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.792</td></tr><tr><td colspan="3" align="center" valign="middle" rowspan="1"><bold>Words of concerns</bold></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Health</td><td align="center" valign="middle" rowspan="1" colspan="1">0.37</td><td align="center" valign="middle" rowspan="1" colspan="1">0.43</td><td align="center" valign="middle" rowspan="1" colspan="1">0.72</td><td align="center" valign="middle" rowspan="1" colspan="1">0.63</td><td align="center" valign="middle" rowspan="1" colspan="1">−72.392</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Leisure</td><td align="center" valign="middle" rowspan="1" colspan="1">1.77</td><td align="center" valign="middle" rowspan="1" colspan="1">1.28</td><td align="center" valign="middle" rowspan="1" colspan="1">1.60</td><td align="center" valign="middle" rowspan="1" colspan="1">1.19</td><td align="center" valign="middle" rowspan="1" colspan="1">21.963</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Family</td><td align="center" valign="middle" rowspan="1" colspan="1">0.22</td><td align="center" valign="middle" rowspan="1" colspan="1">0.30</td><td align="center" valign="middle" rowspan="1" colspan="1">0.25</td><td align="center" valign="middle" rowspan="1" colspan="1">0.30</td><td align="center" valign="middle" rowspan="1" colspan="1">−12.571</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Friend</td><td align="center" valign="middle" rowspan="1" colspan="1">0.11</td><td align="center" valign="middle" rowspan="1" colspan="1">0.20</td><td align="center" valign="middle" rowspan="1" colspan="1">0.10</td><td align="center" valign="middle" rowspan="1" colspan="1">0.16</td><td align="center" valign="middle" rowspan="1" colspan="1">6.202</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Money</td><td align="center" valign="middle" rowspan="1" colspan="1">0.71</td><td align="center" valign="middle" rowspan="1" colspan="1">0.77</td><td align="center" valign="middle" rowspan="1" colspan="1">0.71</td><td align="center" valign="middle" rowspan="1" colspan="1">0.75</td><td align="center" valign="middle" rowspan="1" colspan="1">1.353</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.176</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Death</td><td align="center" valign="middle" rowspan="1" colspan="1">0.14</td><td align="center" valign="middle" rowspan="1" colspan="1">0.27</td><td align="center" valign="middle" rowspan="1" colspan="1">0.15</td><td align="center" valign="middle" rowspan="1" colspan="1">0.24</td><td align="center" valign="middle" rowspan="1" colspan="1">−6.707</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Religion</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.28</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.46</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.32</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.45</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−13.816</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.000 ***</td></tr></tbody></table><table-wrap-foot><fn><p>T-before represents the word frequency during 13–19 January, 2020; T-after represents the word frequency during 20–26 January, 2020; M = mean; SD = standard deviation; df = degrees of freedom. *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id="ijerph-17-02032-t003" orientation="portrait" position="float"><object-id pub-id-type="pii">ijerph-17-02032-t003_Table 3</object-id><label>Table 3</label><caption><p>Emotional indicators before and after 20 January.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin" rowspan="1" colspan="1"></th><th colspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">T-Before</th><th colspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">T-After</th><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1">t</th><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1">df</th><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1"><italic>p</italic></th></tr><tr><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"></th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">M</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">SD</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">M</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">SD</th></tr></thead><tbody><tr><td colspan="3" align="center" valign="middle" rowspan="1">Negative emotions</td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Anxiety</td><td align="center" valign="middle" rowspan="1" colspan="1">11.69</td><td align="center" valign="middle" rowspan="1" colspan="1">4.61</td><td align="center" valign="middle" rowspan="1" colspan="1">12.79</td><td align="center" valign="middle" rowspan="1" colspan="1">4.66</td><td align="center" valign="middle" rowspan="1" colspan="1">−35.962</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" rowspan="1" colspan="1">Depression</td><td align="center" valign="middle" rowspan="1" colspan="1">14.87</td><td align="center" valign="middle" rowspan="1" colspan="1">4.81</td><td align="center" valign="middle" rowspan="1" colspan="1">15.27</td><td align="center" valign="middle" rowspan="1" colspan="1">5.08</td><td align="center" valign="middle" rowspan="1" colspan="1">−10.717</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Indignation</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">1.83</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.43</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">1.86</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.45</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">−11.415</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td colspan="3" align="center" valign="middle" rowspan="1">Positive emotions</td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" rowspan="1" colspan="1"></td><td align="center" valign="middle" style="border-top:solid thin" rowspan="1" colspan="1"></td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Oxford happiness</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">89.91</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">9.48</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">89.71</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">8.84</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">3.120</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.002 **</td></tr></tbody></table><table-wrap-foot><fn><p>T-before represents the predicted emotional indicators during 13–19 January, 2020; T-after represents the predicted emotional indicators during 20–26 January, 2020; M = mean; SD = standard deviation; df = degrees of freedom. ** <italic>p</italic> < 0.01, *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap><table-wrap id="ijerph-17-02032-t004" orientation="portrait" position="float"><object-id pub-id-type="pii">ijerph-17-02032-t004_Table 4</object-id><label>Table 4</label><caption><p>Cognitive indicators before and after 20 January.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="middle" style="border-top:solid thin" rowspan="1" colspan="1"></th><th colspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">T-Before</th><th colspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" rowspan="1">T-After</th><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1">t</th><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1">df</th><th rowspan="2" align="center" valign="middle" style="border-top:solid thin;border-bottom:solid thin" colspan="1"><italic>p</italic></th></tr><tr><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1"></th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">M</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">SD</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">M</th><th align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">SD</th></tr></thead><tbody><tr><td align="center" valign="middle" rowspan="1" colspan="1">Social risk judgment</td><td align="center" valign="middle" rowspan="1" colspan="1">4.10</td><td align="center" valign="middle" rowspan="1" colspan="1">0.27</td><td align="center" valign="middle" rowspan="1" colspan="1">4.12</td><td align="center" valign="middle" rowspan="1" colspan="1">0.25</td><td align="center" valign="middle" rowspan="1" colspan="1">−8.832</td><td align="center" valign="middle" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" rowspan="1" colspan="1">0.000 ***</td></tr><tr><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">Life satisfaction</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">14.33</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">2.47</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">14.24</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">2.28</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">5.500</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">17,747</td><td align="center" valign="middle" style="border-bottom:solid thin" rowspan="1" colspan="1">0.000 ***</td></tr></tbody></table><table-wrap-foot><fn><p>T-before represents the predicted cognitive indicators during 13–19 January, 2020; T-after represents the predicted cognitive indicators during 20–26 January, 2020; M = mean; SD = standard deviation; df = degrees of freedom. *** <italic>p</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap></floats-group></pmc><affiliations><list><country><li>République populaire de Chine</li></country><settlement><li>Pékin</li><li>Tianjin</li></settlement></list><tree><noCountry><name sortKey="Xue, Jia" sort="Xue, Jia" uniqKey="Xue J" first="Jia" last="Xue">Jia Xue</name><name sortKey="Zhao, Nan" sort="Zhao, Nan" uniqKey="Zhao N" first="Nan" last="Zhao">Nan Zhao</name><name sortKey="Zhu, Tingshao" sort="Zhu, Tingshao" uniqKey="Zhu T" first="Tingshao" last="Zhu">Tingshao Zhu</name></noCountry><country name="République populaire de Chine"><noRegion><name sortKey="Li, Sijia" sort="Li, Sijia" uniqKey="Li S" first="Sijia" last="Li">Sijia Li</name></noRegion><name sortKey="Wang, Yilin" sort="Wang, Yilin" uniqKey="Wang Y" first="Yilin" last="Wang">Yilin Wang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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