Dynamic Structure in Diagnostic Structured Interviewing: A Comparative Test of Accuracy and Efficiency

The incorporation of Bayesian logic into diagnostic interviewing may assist with empirically based diagnostic assessment strategies in practice settings, balancing cost effectiveness, administration demands, and accuracy, yet few demonstrations of such a system have been undertaken in the context of mental health diagnosis. The present study represented an initial feasibility demonstration of whether a simplified Bayesian approach offered comparative advantages in interview accuracy and efficiency against a standard assessment procedure. Two different diagnostic algorithms were compared targeting three selected diagnoses: generalized anxiety disorder (GAD), major depressive disorder (MDD), and social phobia (SP). The first algorithm was from a standard semi-structured diagnostic interview, and the second was from a dynamic system using diagnostic base rate information to select interview content. The dynamic algorithm reduced administration time and uniformly matched or improved accuracy over standard procedures. Preparation of this article was supported in part by National Institute of Mental Health Grant R03 MH60134, an award from the University of Hawai‘i Research Council, and awards from the Hawaii Departments of Health and Education to the first author.     

内田─克雷佩林心理测验在我国的试用研究

对１５２２名大、中学生进行内田—克雷佩林心理测验的结果表明：该测验是一种跨文化的心理测量工具,具有较好的信度和效度。还对内田—克雷凤林心理的测验的特点做了剖析,并为进一步的研究提出了建议。