2011•Journal of korean Academy of Child and Adolescent PsychiatryOpen access

Screening Oppositional Defiant Disorder with the Korean Child Behavior Checklist : The Role of the Subscales of Aggressive and Delinquent Behavior

So-Young Irene Lee, Joon-Ho Park, Eunji Lim, Han‐Yong Jung

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Abstract

Objectives : This present study examined the power of the Korean Child Behavior Checklist (K-CBCL) subscales to predict a DSM-IV diagnosis of oppositional defiant disorder (ODD). Methods : The sample included 37 children and adolescents with ODD and 46 normal controls. The participants and their parents were interviewed for clinical diagnosis using the Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version (K-SADS-PL) and the parents completed the K-CBCL. Logistic regression analysis was used to predict the diagnosis of ODD. Results : Among the CBCL subscales, Delinquent and Aggressive Behavior scales significantly predicted ODD diagnosis. The means of these CBCL subscales were significantly higher in the ODD group when compared to the controls. Conclusion:Two CBCL subscales (Deliquent and Aggressive Behavior) displayed good diagnostic efficiency for assessing ODD in children and adolescents. Through combining information from the CBCL, an empirical-quantitative approach to psychopathology in children and the DSM-IV diagnostic criteria, the results demonstrated that a clinical diagnostic approach is an effective diagnostic paradigm for children with ODD.

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Objectives : This present study examined the power of the Korean Child Behavior Checklist (K-CBCL) subscales to predict a DSM-IV diagnosis of oppositional defiant disorder (ODD). Methods : The sample included 37 children and adolescents with ODD and 46 normal controls. The participants and their parents were interviewed for clinical diagnosis using the Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version (K-SADS-PL) and the parents completed the K-CBCL. Logistic regression analysis was used to predict the diagnosis of ODD. Results : Among the CBCL subscales, Delinquent and Aggressive Behavior scales significantly predicted ODD diagnosis. The means of these CBCL subscales were significantly higher in the ODD group when compared to the controls. Conclusion:Two CBCL subscales (Deliquent and Aggressive Behavior) displayed good diagnostic efficiency for assessing ODD in children and adolescents. Through combining information from the CBCL, an empirical-quantitative approach to psychopathology in children and the DSM-IV diagnostic criteria, the results demonstrated that a clinical diagnostic approach is an effective diagnostic paradigm for children with ODD.

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Available abstract

Objectives : This present study examined the power of the Korean Child Behavior Checklist (K-CBCL) subscales to predict a DSM-IV diagnosis of oppositional defiant disorder (ODD). Methods : The sample included 37 children and adolescents with ODD and 46 normal controls. The participants and their parents were interviewed for clinical diagnosis using the Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version (K-SADS-PL) and the parents completed the K-CBCL. Logistic regression analysis was used to predict the diagnosis of ODD. Results : Among the CBCL subscales, Delinquent and Aggressive Behavior scales significantly predicted ODD diagnosis. The means of these CBCL subscales were significantly higher in the ODD group when compared to the controls. Conclusion:Two CBCL subscales (Deliquent and Aggressive Behavior) displayed good diagnostic efficiency for assessing ODD in children and adolescents. Through combining information from the CBCL, an empirical-quantitative approach to psychopathology in children and the DSM-IV diagnostic criteria, the results demonstrated that a clinical diagnostic approach is an effective diagnostic paradigm for children with ODD.

Key concepts: CBCL, Child Behavior Checklist, Schedule for Affective Disorders and Schizophrenia, Psychology, Psychopathology, Conduct disorder, Clinical psychology, Logistic regression

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