Predicting Psychological Distress Amid the COVID-19 Pandemic by Machine Learning: Discrimination and Coping Mechanisms of Korean Immigrants in the U.S.




Choi, Shinwoo
Hong, Joo Young
Kim, Yong Je
Park, Hyejoon

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Multidisciplinary Digital Publishing Institute


The current study examined the predictive ability of discrimination-related variables, coping mechanisms, and sociodemographic factors on the psychological distress level of Korean immigrants in the U.S. amid the COVID-19 pandemic. Korean immigrants (both foreign-born and U.S.-born) in the U.S. above the age of 18 were invited to participate in an online survey through purposive sampling. In order to verify the variables predicting the level of psychological distress on the final sample from 42 states (n = 790), the Artificial Neural Network (ANN) analysis, which is able to examine complex non-linear interactions among variables, was conducted. The most critical predicting variables in the neural network were a person’s resilience, experiences of everyday discrimination, and perception that racial discrimination toward Asians has increased in the U.S. since the beginning of the COVID-19 pandemic.



racism, mental health, Korean immigrants, United States, artificial neural network, COVID-19, Social Work


Choi, S., Hong, J. Y., Kim, Y. J., & Park, H. (2020). Predicting psychological distress amid the COVID-19 pandemic by machine learning: Discrimination and coping mechanisms of Korean immigrants in the U.S. International Journal of Environmental Research and Public Health, 17(17), 6057.


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