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

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Corresponding Author
Lyda Hill Institute for Human Resilience, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA
Correspondence Kristin W. Samuelson, Lyda Hill Institute for Human Resilience, University of Colorado at Colorado Springs, 1420 Austin Bluffs Pkwy, Colorado Springs, CO 80918, USA.
Email: ksamuel3@uccs.edu
Department of Psychology, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA
Department of Psychology, Dominican University of California, San Rafael, California, USA
Department of Psychology, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA
Department of Psychology, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA
Department of Psychology, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA
Corresponding Author
Lyda Hill Institute for Human Resilience, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA
Correspondence Kristin W. Samuelson, Lyda Hill Institute for Human Resilience, University of Colorado at Colorado Springs, 1420 Austin Bluffs Pkwy, Colorado Springs, CO 80918, USA.
Email: ksamuel3@uccs.edu
Department of Psychology, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA
Department of Psychology, Dominican University of California, San Rafael, California, USA
Department of Psychology, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA
Department of Psychology, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA
Department of Psychology, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA
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This study explored risk and resilience factors of mental health functioning during the coronavirus disease (COVID-19) pandemic.
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.
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.
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.
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