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Mental health and resilience during the coronavirus pandemic: A machine learning approach.

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Title: Mental health and resilience during the coronavirus pandemic: A machine learning approach.
Authors: Samuelson, Kristin W. (AUTHOR), Dixon, Kelly (AUTHOR), Jordan, Joshua T. (AUTHOR), Powers, Tyler (AUTHOR), Sonderman, Samantha (AUTHOR), Brickman, Sophie (AUTHOR)
Source: Journal of Clinical Psychology. May2022, Vol. 78 Issue 5, p821-846. 26p. 4 Charts, 8 Graphs.
Subjects: Mental health, COVID-19 pandemic, Supervised learning, Machine learning, Health services accessibility, Psychological resilience, Social anxiety
Abstract: Objective: This study explored risk and resilience factors of mental health functioning during the coronavirus disease (COVID‐19) pandemic. Methods: A sample of 467 adults (M age = 33.14, 63.6% female) reported on mental health (depression, anxiety, posttraumatic stress disorder [PTSD], and somatic symptoms), demands and impacts of COVID‐19, resources (e.g., social support, health care access), demographics, and psychosocial resilience factors. Results: Depression, anxiety, and PTSD rates were 44%, 36%, and 23%, respectively. Supervised machine learning models identified psychosocial factors as the primary significant predictors across outcomes. Greater trauma coping self‐efficacy and forward‐focused coping, but not trauma‐focused coping, were associated with better mental health. When accounting for psychosocial resilience factors, few external resources and demographic variables emerged as significant predictors. Conclusion: With ongoing stressors and traumas, employing coping strategies that emphasize distraction over trauma processing may be warranted. Clinical and community outreach efforts should target trauma coping self‐efficacy to bolster resilience during a pandemic. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Clinical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Mental health and resilience during the coronavirus pandemic: A machine learning approach.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Clinical+Psychology%22">Journal of Clinical Psychology</searchLink>. May2022, Vol. 78 Issue 5, p821-846. 26p. 4 Charts, 8 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Mental+health%22">Mental health</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19+pandemic%22">COVID-19 pandemic</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Health+services+accessibility%22">Health services accessibility</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+resilience%22">Psychological resilience</searchLink><br /><searchLink fieldCode="DE" term="%22Social+anxiety%22">Social anxiety</searchLink>
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  Data: Objective: This study explored risk and resilience factors of mental health functioning during the coronavirus disease (COVID‐19) pandemic. Methods: A sample of 467 adults (M age = 33.14, 63.6% female) reported on mental health (depression, anxiety, posttraumatic stress disorder [PTSD], and somatic symptoms), demands and impacts of COVID‐19, resources (e.g., social support, health care access), demographics, and psychosocial resilience factors. Results: Depression, anxiety, and PTSD rates were 44%, 36%, and 23%, respectively. Supervised machine learning models identified psychosocial factors as the primary significant predictors across outcomes. Greater trauma coping self‐efficacy and forward‐focused coping, but not trauma‐focused coping, were associated with better mental health. When accounting for psychosocial resilience factors, few external resources and demographic variables emerged as significant predictors. Conclusion: With ongoing stressors and traumas, employing coping strategies that emphasize distraction over trauma processing may be warranted. Clinical and community outreach efforts should target trauma coping self‐efficacy to bolster resilience during a pandemic. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Clinical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
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        Value: 10.1002/jclp.23254
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        Text: English
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        PageCount: 26
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      – SubjectFull: Mental health
        Type: general
      – SubjectFull: COVID-19 pandemic
        Type: general
      – SubjectFull: Supervised learning
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      – SubjectFull: Machine learning
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      – SubjectFull: Health services accessibility
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      – SubjectFull: Psychological resilience
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      – SubjectFull: Social anxiety
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              M: 05
              Text: May2022
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              Y: 2022
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