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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 ...
Behavior analysis in child development takes a mechanistic, contextual, and pragmatic approach. [6] [7] From its inception, the behavioral model has focused on prediction and control of the developmental process. [8] [9] The model focuses on the analysis of a behavior and then synthesizes the action to support the original behavior. [10]
Each form of the BRIEF parent- and teacher- rating form contains 86 items in eight non-overlapping clinical scales and two validity scales.These theoretically and statistically derived scales form two indexes: Behavioral Regulation (three scales) and Metacognition (five scales), as well as a Global Executive Composite [6] score that takes into account all of the clinical scales and represents ...
The study collects data on the behavior and brain development of over 11,500 children beginning at age 9-10 and continuing through young adulthood. [2] The study collected data from youth in seven primary domains: physical health, mental health, brain imaging, biospecimens, neurocognition, substance use, and culture and environment.
The Swanson, Nolan and Pelham Teacher and Parent Rating Scale (SNAP), developed by James Swanson, Edith Nolan and William Pelham, is a 90-question self-report inventory designed to measure attention deficit hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) symptoms in children and young adults.
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.
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.
Adolescent exposure to pornography is divided into two different sections: unintentional exposure and intentional exposure. The unintentional exposure rate for US adolescents was found to be from 19% [27] to 32% [28] from 2007 to 2013. Intentional pornography adolescent viewers from 10 to 17 years old take up 34% of the population at that age ...