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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 ...
PTSD Symptom Scale – Self-Report Version (PSS-SR) is a 17-item self-reported questionnaire to assess symptoms of posttraumatic stress disorder. [1] Each of the 17 items describe PTSD symptoms which respondents rate in terms of their frequency or severity using a Likert-type scale ranging from 0 (not at all or only one time) to 3 (almost always or five or more times per week).
The Child PTSD Symptom Scale (CPSS) is a free checklist designed for children and adolescents to report traumatic events and symptoms that they might feel afterward. [1] The items cover the symptoms of posttraumatic stress disorder , specifically, the symptoms and clusters used in the DSM-IV. Although relatively new, there has been a fair ...
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 ]
The My Mood Monitor Screen (aka M3 Checklist) is a quick, validated, self-rated, multi-dimensional mental health symptom checklist that screens for and monitors changes in potential mood and anxiety symptoms.
The Somatic Symptom Scale - 8 (SSS-8) [1] is a brief self-report questionnaire used to assess somatic symptom burden. It measures the perceived burden of common somatic symptoms. These symptoms were originally chosen to reflect common symptoms in primary care but they are relevant for a large number of diseases and mental disorders.
Total symptom severity is calculated by summing up all the individual item severity scores. For example, in the CAPS-IV scoring, to meet criteria for a symptom, the symptom must have an intensity score of 2 (on a scale or 0-4) or greater and a frequency score of 1 (on a scale of 0–4) or greater.
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.