Titlebook: Learning Disorders Across the Lifespan; A Mental Health Fram Amy E. Margolis,Jessica Broitman Book 2023 The Editor(s) (if applicable) and T

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Integrating Psychology, Psychiatry, and Neuroscience into Clinical Assessment of Specific Learning Dacademic skills, and specifically reading skills, may inform a personalized approach to treatment. We use cognitive control as a model system and propose tools that may help move SLD treatment and research forward by identifying novel cognitive and neural treatment targets.

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The Need for Integrated Supports in Schools and the Role of School Psychologists explains how cognition and emotion impact students’ functioning and a brief description of some common disorders that affect students within the domains of cognition, affect, and their interactions. We argue that interventions for learning disabilities require faculty and psychologists who can addr

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Adapting DBT for Youth with Learning Disordersacademic skills deficit, and provide considerations for individual, family, and group therapists. We then offer specific suggestions for adapting DBT for clinicians working with individuals diagnosed with each of the specific learning disorders across treatment modalities. Finally, we close with an

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A Contemporary Psychoanalytic Consideration of Nonverbal Learning Disabilitiesoffer a brief overview of the psychoanalytic treatment of the young man to illustrate how holding and coherence emerged such that he experienced an enhanced ability to organize and utilize his mind and his thoughts with agency, purpose, and efficacy.

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Book 2023nents of SLDs and addresses the need for a mental health framework within which to approach theory, treatment, and assessment of SLDs..Key areas of coverage include:.Examining different theoretical orientations to learning disorders (e.g., cognitive, behavioral, neuropsychoeducational, psychoanalyti

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cells. Additionally, we compare attribution maps with the annotations of a medical expert to see how the model’s decision-making differs from the human standard. Our study addresses the challenge of implementing pixel-level explainability in multiple instance learning models and provides insights f