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

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.