Psychological distress and coping during the COVID-19 pandemic: a structural topic modeling approach
Date
2023
Authors
Tran, Thao P., author
Henry, Kimberly L., advisor
Lucas-Thompson, Rachel G., committee member
Prasad, Joshua, committee member
Prince, Mark, committee member
Swaim, Randall C., committee member
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Abstract
In this study, I focused on two closely related phenomena, namely psychological distress and distress-coping mechanisms during the initial outbreak of the COVID-19 pandemic. I examined participants' voluntary written responses to two open-ended questions on psychological distress and coping in an online survey using an unsupervised machine learning approach called structural topic modeling. I chose to extract 17 topics from the collection of participants' responses. Among these topics, 11 were mostly about different factors contributing to participants' mental health during the COVID-19 pandemic, including but not limited to, uncertainty due to the coronavirus, financial/work-related concerns, living conditions, and concerns about personal health and safety as well as the well-being of loved ones and others in general. Besides, I also found 5 topics discussing many ways people took care of their mental health during this challenging time. Surprisingly, one topic revealed different feedback people had for researchers who designed and implemented the survey. I also found cross-country differences in terms of the prevalence of each of the resultant topics. In summary, I documented a number of findings that are congruent with the existing literature on psychological distress and coping during the COVID-19 pandemic while at the same time, pointed out some important nuances in the qualitative responses of participants. Implications, strengths, and limitations, as well as directions for future research were discussed in the study.
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Subject
COVID-19
psychological distress
structural topic modeling
mental health
coping
qualitative research