克朗巴赫阿尔法
心理学
验证性因素分析
组内相关
临床心理学
评定量表
接收机工作特性
蒙哥马利-奥斯伯格抑郁评定量表
心情
萧条(经济学)
精神科
重性抑郁障碍
心理测量学
医学
内科学
结构方程建模
统计
发展心理学
宏观经济学
经济
数学
作者
Yanli Du,Jianbo Hu,Tingting Huang,Jianbo Lai,Chee H. Ng,Weihua Zhang,Chao Li,Zhong-ya Xu,Hetong Zhou,Liemin Ruan,Yi Xu,Shaohua Hu
标识
DOI:10.1016/j.jad.2020.09.117
摘要
With the modification of DSM-5 mixed features specifier, a brief scale to screen mixed features in patients with mood disorders is needed in clinical practice. This study aimed to explore the psychometric properties of the Chinese version of the Clinically Useful Depression Outcome Scale supplemented with DSM-5 Mixed subtype (CUDOS-M-C) for the Chinese patients with mood disorders.Overall, 300 patients with major depressive episode were recruited. All participants were assessed using CUDOS-M-C, Young Mania Rating Scale, Hamilton Anxiety Scale and Montgomery-Asberg Depression Rating Scale. The receiver operating characteristic (ROC) curve analysis was used to calculate the optimal cut-off values of CUDOS-M-C score. The reliability and validity of CUDOS-M-C were examined using Cronbach's alpha, intraclass correlation coefficient (ICC) and principal component analysis (PCA).The results of PCA indicated two-factor structure as the best solution for CUDOS-M-C, which explained 54.82% of cumulative variance. The Cronbach's alpha was 0.892 and the ICC was 0.853. The area under the ROC curve of the CUDOS-M-C for participants with mixed depression was 0.927 (p<0.001) and the suitable cut-off value was 8, with a sensitivity of 91.6% and specificity of 79.9%.Most of the patients were recruited from eastern China and further research with larger sample is warranted. And this study did not perform confirmatory factor analysis to identify the generalization of factor structure of CUDOS-M-C. Besides, the study performed the test-retest reliability of CUDOS-M-C and further analysis is needed to ascertain the patient's post-treatment changes.The CUDOS-M-C demonstrated to have satisfactory psychometric properties as a self-report scale, and could be applied to screen patients with mixed depression in clinical practice.
科研通智能强力驱动
Strongly Powered by AbleSci AI