文献类型：期刊文献

英文题名：Development of the psychopathological vulnerability index for screening at-risk youths: a Rasch model approach

作者：Liao, Yujing[1];Shen, Haitao[2];Duan, Wenjie[1];Cui, Shanshan[3];Zheng, Chunxiu[3];Liu, Rong[4];Jia, Yawen[5]

机构：[1]East China Univ Sci & Technol, Sch Social & Publ Adm, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Inst Marxism, Shanghai, Peoples R China;[3]East China Univ Sci & Technol, Off Student Serv, Shanghai, Peoples R China;[4]East China Univ Sci & Technol, Student Counseling & Mental Hlth Ctr, Shanghai, Peoples R China;[5]Nanchang Univ, Sch Journalism & Commun, Nanchang, Peoples R China

年份：2024

卷号：3

期号：1

外文期刊名：NPJ MENTAL HEALTH RESEARCH

收录：WOS:【ESCI(收录号:WOS:001592604800001)】；

基金：We would like to thank Yao Li from the Student Counseling and Mental Health Center at East China University of Science and Technology for his assistance in collecting the survey data. This study was funded by the Natural Science Foundation of Shanghai (grant 23ZR1415000). The funder played no role in the study design, data collection, analysis, and interpretation of data, or the writing of this manuscript.

语种：英文

摘要：Accumulating research on mental health emphasizes the general factor of psychopathology (p-factor) that unites various mental health issues. This study develops a psychopathological vulnerability assessment for youths, evaluating its psychometric properties and clinical utility. An umbrella review conceptualized multifactor psychopathological vulnerability, leading to a 57-item pool. A total of 11,224 individuals participated in this study. The resulting 22-item psychopathological vulnerability index (PVI) fitted the unidimensional Rasch model, demonstrating a person separation reliability of 0.78 and a Cronbach's alpha of 0.84. Cut-off points of 11 and 5, derived from latent class analysis, were used to distinguish vulnerable and high-protection populations. The PVI's concurrent and predictive hit rates ranged from 36.00% to 53.57% in clinical samples. The PVI concretized the vulnerability-stress model for identifying at-risk youths and may facilitate universal interventions by integrating the theoretical foundations of bifactor S-1 models with key symptoms from network models for theoretically grounded approaches.