Unveiling the Potential of Diagnostic Classification Models for Precise Diagnosis in Emotional-Behavioral Disorders: Evidence from Iran.

This study uses diagnostic classification models (DCMs) to improve how emotional and behavioral disorders (EBD) are assessed in children and adolescents in Iran. Data from 1,437 Iranian students aged 6-18 were analyzed using the DSM-oriented Child Behavior Checklist, focusing on six symptom areas: depression, anxiety, somatic complaints, attention-deficit/hyperactivity, oppositional defiant behavior, and conduct problems. Instead of relying on total scores, DCMs examine patterns of symptoms across multiple domains at the item level. A theory-based Q-matrix aligned with DSM criteria was developed, validated by experts, and evaluated using modern model-fit tools, including lens plots and RMSD indices. Results showed that simpler (reduced) models fit the data better than the fully saturated model. Strong links were observed between anxiety and depression, as well as between oppositional defiant and conduct problems. Some symptom areas (such as anxiety and oppositional defiant problems) showed greater uncertainty in classification, while others (such as somatic and conduct problems) were more stable. Importantly, children with the same total symptom scores often had very different symptom profiles, highlighting the limits of traditional scoring methods. Overall, the findings suggest that DCMs can provide a more precise and informative view of children's mental health symptoms, especially in culturally specific contexts. Further research is needed to confirm how well these classifications relate to real-world functioning and clinical outcomes.