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躯体症状报告具有维度潜结构:来自税式分析的结果。

Somatic symptom reporting has a dimensional latent structure: results from taxometric analyses.

机构信息

Department of Clinical Psychology and Psychotherapy, Johannes Gutenberg University, Mainz, Germany.

出版信息

J Abnorm Psychol. 2012 Aug;121(3):725-38. doi: 10.1037/a0028407. Epub 2012 May 28.

Abstract

Medically unexplained symptoms (MUS) are one of the key features of somatoform disorders. Although MUS are currently treated as both categorical (in terms of the diagnosis of somatoform disorders) and dimensional (in terms of quantitative measures of somatization/somatic symptom reporting), little is known about the empirical latent structure of MUS. Using taxometric analyses, the latent structure of somatic symptom reporting was analyzed with the Patient Health Questionnaire (PHQ)-15 in two student samples (N=782 and N=2,577) and a primary care sample (N=519). We applied three popular taxometric methods: Maximum Eigenvalue (MAXEIG), Mean Above Minus Below a Cut (MAMBAC) and Latent-Mode (L-Mode). Simulation data were created to evaluate the appropriateness of the data for our analyses and to create the comparison curve fit index (CCFI) as an objective outcome measure. The results of all taxometric methods in any of the three data sets were in favor of a dimensional solution (CCFI<.50). Simulated taxonic and dimensional datasets differed substantially and the samples were appropriate for taxometric analysis. Accordingly, the latent structure of somatization/somatic symptom reporting as assessed by the PHQ-15 is dimensional in both primary care and student samples. Implications regarding the practical application as well as models of etiology and pathogenesis of somatic symptom reporting are discussed.

摘要

未明原因的躯体症状(MUS)是躯体形式障碍的主要特征之一。尽管 MUS 目前被视为分类(躯体形式障碍的诊断)和维度(躯体化/躯体症状报告的定量测量),但对 MUS 的经验性潜在结构知之甚少。使用分类分析,使用患者健康问卷(PHQ-15)对两个学生样本(N=782 和 N=2,577)和一个初级保健样本(N=519)的躯体症状报告的潜在结构进行了分析。我们应用了三种流行的分类分析方法:最大特征值(MAXEIG)、均值在分界值上下(MAMBAC)和潜在模式(L-Mode)。创建模拟数据来评估数据是否适合我们的分析,并创建比较曲线拟合指数(CCFI)作为客观的结果衡量标准。任何三种数据集中的所有分类分析方法的结果都支持维度解决方案(CCFI<.50)。模拟的分类和维度数据集有很大的不同,样本适合进行分类分析。因此,PHQ-15 评估的躯体化/躯体症状报告的潜在结构在初级保健和学生样本中均为维度。讨论了关于躯体症状报告的实际应用以及病因和发病机制模型的影响。

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