Print Close

Understanding Post-Pandemic Mental Health via Statistical Learning

Abstract Number:

2437

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Speed

Participants:

Xiankui Yang (1), Lu Lu (1)

Institutions:

(1) University of South Florida, Tampa, FL

Co-Author:

Lu Lu
University of South Florida

First Author:

Xiankui Yang
University of South Florida

Presenting Author:

Xiankui Yang
N/A

Abstract Text:

Understanding the prevalence and impact of anxiety and depressive symptoms is crucial in recognizing the global pandemic aftermath. This research will explore the mental health complexity in the post-pandemic landscape of 2022 by analyzing data from the General Social Survey (https://gss.norc.org/). The research will focus on studying two primary mental health measures, Generalized Anxiety Disorder (GAD) and Patient Health Questionnaire (PHQ) scores to identify individual well-being subtleties that affect these scores and offer insights into the evolving mental health relationship. We propose a two-step approach for this research: first, employing machine learning algorithms to analyze and identify distinct subgroups and structure patterns within the individual mental health data, and second, based on findings from first step, utilizing advanced statistical models to explore the joint impact of individual and societal factors on mental health. The ultimate goal of this study is to identify key factors influencing post-pandemic mental health and to provide actionable insights for policymakers, clinicians, and mental health practitioners.

Keywords:

Sponsors:

Section on Statistical Computing

Tracks:

Data Science

Can this be considered for alternate subtype?

Are you interested in volunteering to serve as a session chair?

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand