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Mental health and resilience during the coronavirus pandemic: A machine learning approach
Kristin W Samuelson1, Kelly Dixon2, Joshua T Jordan3
1Lyda Hill Institute for Human Resilience, University of Colorado at Colorado Springs, Colorado Springs, Colorado, USA.
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.
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