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The Child and Adolescent Symptom Inventory (CASI) is a behavioral rating checklist created by Kenneth Gadow and Joyce Sprafkin that evaluates a range of behaviors related to common emotional and behavioral disorders identified in the Diagnostic and Statistical Manual of Mental Disorders (DSM), including attention deficit hyperactivity disorder, oppositional defiant disorder, conduct disorder ...
The Pediatric Symptom Checklist (PSC) is a 35-item parent-report questionnaire designed to identify children with difficulties in psychosocial functioning. Its primary purpose is to alert pediatricians at an early point about which children would benefit from further assessment. [1]
When children reach adolescence and start having more real-world experiences, it’s common for them to develop fears around social anxiety, particularly when facing the scrutiny of others.
The Child Behavior Checklist (CBCL) is a widely used caregiver report form identifying problem behavior in children. [ 1 ] [ 2 ] It is widely used in both research and clinical practice with youths. It has been translated into more than 90 languages, [ 3 ] and normative data are available integrating information from multiple societies.
Generalized anxiety disorder is "characterized by chronic excessive worry accompanied by three or more of the following symptoms: restlessness, fatigue, concentration problems, irritability, muscle tension, and sleep disturbance". [13] Generalized anxiety disorder is the most common anxiety disorder to affect older adults. [14]
Treatment often involves promoting better communication and socializing, and reducing behaviors that can be disruptive. Children with pervasive developmental disorders may be placed in special education classes, receive behavior modification training, speech, physical or occupational therapy, or medication.
According to the CDC, symptoms may include: Fever or chills. Cough. Shortness of breath or difficulty breathing. Sore throat. Congestion or runny nose. New loss of taste or smell. Fatigue. Muscle ...
Achenbach used machine learning and principal component analysis when developing the ASEBA in order to cluster symptoms together when forming the assessment's eight categories. This approach ignored the syndrome clusters found in the DSM-I, instead relying on patterns found in case records of children with identified psychopathologies.
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