Xianghua Cai
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Xianghua Cai has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Bin Gao
School of Education, Shanghai Normal University, Shanghai 200234, China
1 shared publication - Chong Zhao
School of Education, Shanghai Normal University, Shanghai 200234, China
1 shared publication - Lu Bai
College of Education, Jiangsu University of Technology, Changzhou 212003, China
1 shared publication - Xiaoqi Yan
School of Education, Shanghai Normal University, Shanghai 200234, China
1 shared publication - Yongsheng Fan
School of Education, Shanghai Normal University, Shanghai 200234, China
1 shared publication
Latest Publications
- Open Access
- 1 November 2024
- Published by MDPI AG in Behavioral Sciences
- Vol. 14 (11) , 1020
Latent Profile Analysis of Acute Stress Disorder Symptoms and Their Links to Individual Characteristics and Mental Health Among College Students During the Early COVID-19 Pandemic
Objectives: acute stress disorder (ASD) became prevalent among various populations during the COVID-19 pandemic, yet little research has examined the heterogeneity of ASD symptoms among college students. The purpose of this research was to explore subgroups of ASD symptoms using latent profile analysis (LPA) and to explore the predictors and mental health outcomes associated with these profiles. Methods: Using the person-centered method, we recruited 1198 college students (71.7% female) who self-reported their ASD, perceived social support, anxiety, depression, and life satisfaction from two Chinese universities following the COVID-19 outbreak. Results: The LPA results found three ASD symptom severity profiles: low (56.7%), moderate (31.6%), and high (11.7%), particularly characterized by reexperiencing and arousal symptoms. This study found that students in the moderate and high ASD subgroups were more likely to be female, have lower socioeconomic status, belong to minority groups, report lower self-rated health, and perceive less social support compared to those in the low ASD subgroup. Furthermore, compared to the low and moderate ASD subgroups, the high ASD subgroup was linked to elevated anxiety and depression and lower life satisfaction. Conclusions: These findings underscore the significance of identifying specific ASD symptom subgroups to effectively target prevention and intervention efforts.
- 1 November 2024
- Published by MDPI AG in Behavioral Sciences
- Vol. 14 (11) , 1020
Open AccessLatent Profile Analysis of Acute Stress Disorder Symptoms and Their Links to Individual Characteristics and Mental Health Among College Students During the Early COVID-19 Pandemic
Objectives: acute stress disorder (ASD) became prevalent among various populations during the COVID-19 pandemic, yet little research has examined the heterogeneity of ASD symptoms among college students. The purpose of this research was to explore subgroups of ASD symptoms using latent profile analysis (LPA) and to explore the predictors and mental health outcomes associated with these profiles. Methods: Using the person-centered method, we recruited 1198 college students (71.7% female) who self-reported their ASD, perceived social support, anxiety, depression, and life satisfaction from two Chinese universities following the COVID-19 outbreak. Results: The LPA results found three ASD symptom severity profiles: low (56.7%), moderate (31.6%), and high (11.7%), particularly characterized by reexperiencing and arousal symptoms. This study found that students in the moderate and high ASD subgroups were more likely to be female, have lower socioeconomic status, belong to minority groups, report lower self-rated health, and perceive less social support compared to those in the low ASD subgroup. Furthermore, compared to the low and moderate ASD subgroups, the high ASD subgroup was linked to elevated anxiety and depression and lower life satisfaction. Conclusions: These findings underscore the significance of identifying specific ASD symptom subgroups to effectively target prevention and intervention efforts.