{"title":"用认知诊断模型开发新的抑郁症诊断工具。","authors":"Daxun Wang, Xuliang Gao, Yan Cai, Dongbo Tu","doi":"10.3389/fpsyg.2019.01306","DOIUrl":null,"url":null,"abstract":"<p><p>Most existing instruments for depression are developed based on classical test theory, factor analysis, or sometimes, item response theory, and focus on the accurate measurement of the severity of depressive disorder. Nevertheless, they tend to be less useful in supporting the decision based on <i>ICD-10</i> or <i>DSM-5</i> because of the lack of detailed information for symptoms. To gain rich and valid information at the symptom level, this article developed a depression test under the framework of cognitive diagnosis models (CDMs), referred to as CDMs-D. A total of 1,181 individuals were finally recruited and their responses were used to examine the psychometric properties of CDMs-D. After excluding poor items for statistical reasons (e.g., low discrimination, poor model-fit or having DIF), 56 items were included in the CDMs-D. The CDMs-D measures all ten symptom criteria for depression defined in <i>ICD-10</i> and covers five domains of depression defined by Gibbons et al. (2012). Comparing with the existing self-report measures (such as PHQ-9, SDS, CES-D and so on), a distinguishing feature of the CDMs-D is that it can provide both overall information about the severity of depressive disorder and the assessment information about specific symptoms, which could be useful for diagnostic and interventional purposes.</p>","PeriodicalId":12525,"journal":{"name":"Frontiers in Psychology","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2019-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3389/fpsyg.2019.01306","citationCount":"4","resultStr":"{\"title\":\"Development of a New Instrument for Depression With Cognitive Diagnosis Models.\",\"authors\":\"Daxun Wang, Xuliang Gao, Yan Cai, Dongbo Tu\",\"doi\":\"10.3389/fpsyg.2019.01306\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Most existing instruments for depression are developed based on classical test theory, factor analysis, or sometimes, item response theory, and focus on the accurate measurement of the severity of depressive disorder. Nevertheless, they tend to be less useful in supporting the decision based on <i>ICD-10</i> or <i>DSM-5</i> because of the lack of detailed information for symptoms. To gain rich and valid information at the symptom level, this article developed a depression test under the framework of cognitive diagnosis models (CDMs), referred to as CDMs-D. A total of 1,181 individuals were finally recruited and their responses were used to examine the psychometric properties of CDMs-D. After excluding poor items for statistical reasons (e.g., low discrimination, poor model-fit or having DIF), 56 items were included in the CDMs-D. The CDMs-D measures all ten symptom criteria for depression defined in <i>ICD-10</i> and covers five domains of depression defined by Gibbons et al. (2012). Comparing with the existing self-report measures (such as PHQ-9, SDS, CES-D and so on), a distinguishing feature of the CDMs-D is that it can provide both overall information about the severity of depressive disorder and the assessment information about specific symptoms, which could be useful for diagnostic and interventional purposes.</p>\",\"PeriodicalId\":12525,\"journal\":{\"name\":\"Frontiers in Psychology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2019-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3389/fpsyg.2019.01306\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.3389/fpsyg.2019.01306\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2019/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHOLOGY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3389/fpsyg.2019.01306","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
Development of a New Instrument for Depression With Cognitive Diagnosis Models.
Most existing instruments for depression are developed based on classical test theory, factor analysis, or sometimes, item response theory, and focus on the accurate measurement of the severity of depressive disorder. Nevertheless, they tend to be less useful in supporting the decision based on ICD-10 or DSM-5 because of the lack of detailed information for symptoms. To gain rich and valid information at the symptom level, this article developed a depression test under the framework of cognitive diagnosis models (CDMs), referred to as CDMs-D. A total of 1,181 individuals were finally recruited and their responses were used to examine the psychometric properties of CDMs-D. After excluding poor items for statistical reasons (e.g., low discrimination, poor model-fit or having DIF), 56 items were included in the CDMs-D. The CDMs-D measures all ten symptom criteria for depression defined in ICD-10 and covers five domains of depression defined by Gibbons et al. (2012). Comparing with the existing self-report measures (such as PHQ-9, SDS, CES-D and so on), a distinguishing feature of the CDMs-D is that it can provide both overall information about the severity of depressive disorder and the assessment information about specific symptoms, which could be useful for diagnostic and interventional purposes.
期刊介绍:
Frontiers in Psychology is the largest journal in its field, publishing rigorously peer-reviewed research across the psychological sciences, from clinical research to cognitive science, from perception to consciousness, from imaging studies to human factors, and from animal cognition to social psychology. Field Chief Editor Axel Cleeremans at the Free University of Brussels is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. The journal publishes the best research across the entire field of psychology. Today, psychological science is becoming increasingly important at all levels of society, from the treatment of clinical disorders to our basic understanding of how the mind works. It is highly interdisciplinary, borrowing questions from philosophy, methods from neuroscience and insights from clinical practice - all in the goal of furthering our grasp of human nature and society, as well as our ability to develop new intervention methods.