Mental Health and Resilience During the Coronavirus Pandemic: A Machine Learning Approach

Overview

Journal J Clin Psychol

Publisher Wiley

Specialty Psychology

Date 2021 Oct 11

PMID 34633661

Citations 6

Authors

Kristin W Samuelson

Kelly Dixon

Joshua T Jordan

Tyler Powers

Samantha Sonderman

Sophie Brickman

Affiliations

Soon will be listed here.

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.

Citing Articles

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The Association of Social Support and Loneliness with Symptoms of Depression, Anxiety, and Posttraumatic Stress during the COVID-19 Pandemic: A Meta-Analysis.

Gabarrell-Pascuet A, Garcia-Mieres H, Gine-Vazquez I, Moneta M, Koyanagi A, Haro J Int J Environ Res Public Health. 2023; 20(4).

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Qin L, Peng J, Shu M, Liao X, Gong H, Luo B Healthcare (Basel). 2023; 11(3).

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