Variables incorporated in statistical analysis.

Studies have investigated various aspects of how the COVID-19 pandemic has impacted college students’ well-being. However, the complex relationships between stress and its correlates have received limited attention. Thus, the main objective of this study is to evaluate multiplicative associations between stress and demographic, lifestyle, and other negative emotion factors during the pandemic. We used data from a survey with 2,534 students enrolled in seven U.S. universities and analyzed such data with generalized additive Tobit models and pairwise interaction terms. The results highlighted associations and interactions between myriad factors such as students’ social class, income, parental education, body mass index (BMI), amount of exercise, and knowing infected people in the student’s communities. For instance, we found that the associations between feeling irritable and sad due to the pandemic were interactive, resulting in higher associated stress for students with higher levels of parents’ education. Furthermore, associations between taking precautionary actions (i.e., avoiding travel and large gatherings) and stress varied with the intensity of negative feelings (i.e., sadness and irritability). Considering these interaction terms, the results highlighted a great inequality in pandemic-related stress within low income, lower social class, and higher BMI students. This study is among the earliest that employed a stratified approach with numerous interaction terms to better understand the multiplicative associations between different factors during the COVID-19 pandemic.

现有研究已围绕新冠疫情对大学生福祉的多维度影响展开了诸多探讨，但压力与其相关变量间的复杂关联却鲜有深入关注。因此，本研究的核心目标为评估疫情期间压力与人口统计学特征、生活方式及其他负性情绪因素之间的乘性关联。本研究使用了来自美国7所高校2534名在校大学生的调查数据，并采用广义加性Tobit模型（generalized additive Tobit models）与两两交互项对数据进行建模分析。研究结果揭示了诸多因素间的关联与交互效应，包括学生社会阶层、家庭收入、父母受教育程度、体重指数（BMI）、日常运动量以及所在社区内存在新冠感染者情况等。例如，本研究发现，疫情引发的烦躁与悲伤情绪间的关联存在交互效应：父母受教育程度越高的学生，其烦躁与悲伤情绪对应的压力水平也显著更高。此外，采取防疫举措（即避免出行与大型聚集）与压力水平间的关联，会随负性情绪强度（即悲伤与烦躁程度）的变化而呈现异质性差异。结合上述交互项分析结果，本研究进一步揭示了疫情相关压力在不同群体间的显著不平等：低收入群体、社会阶层较低者以及体重指数较高的学生承受了更为严重的疫情相关压力。本研究属于较早采用纳入大量交互项的分层分析方法，以深入解析新冠疫情期间多因素间乘性关联的相关研究之一。