Higher-order dimensions of psychopathology in a neurodevelopmental transdiagnostic sample.

Hierarchical dimensional models of psychopathology derived for adult and child community populations offer more informative and efficient methods for assessing and treating symptoms of mental ill health than traditional diagnostic approaches. It is not yet clear how many dimensions should be include...

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Publicado en:Journal of Abnormal Psychology Vol. 130; no. 8; pp. 909 - 923
Autores principales: Holmes, Joni, Mareva, Silvana, Bennett, Marc P., Black, Melissa J., Guy, Jacalyn
Formato: journal article
Publicado: American Psychological Association Nov2021
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Nov2021
      vid: 130
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      pub: American Psychological Association
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        10.1037/abn0000710
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        atl: Higher-order dimensions of psychopathology in a neurodevelopmental transdiagnostic sample.
      aug:
        au:
          Holmes, Joni
          Mareva, Silvana
          Bennett, Marc P.
          Black, Melissa J.
          Guy, Jacalyn
        affil:
          Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge
          Cambridgeshire and Peterborough National Health Service Foundation Trust, Cambridge, United Kingdom
      su:
        Mental illness
        Executive function
        Teenagers
        Mental health
        Pathological psychology
        Anxiety disorders
        Anxiety
        Neural development
        Child psychopathology
        Educational outcomes
        Questionnaires
      sug:
        subj:
          Mental illness
          Executive function
          Teenagers
          Mental health
          Pathological psychology
          Anxiety disorders
          Anxiety
          Offices of Mental Health Practitioners (except Physicians)
          Neural development
          Child psychopathology
          Educational outcomes
          Questionnaires
      keyword:
        childhood psychopathology
        general p factor
        hierarchical dimensional model
        neurodevelopmental
        transdiagnostic
        childhood psychopathology
        general p factor
        hierarchical dimensional model
        neurodevelopmental
        transdiagnostic
      ab: Hierarchical dimensional models of psychopathology derived for adult and child community populations offer more informative and efficient methods for assessing and treating symptoms of mental ill health than traditional diagnostic approaches. It is not yet clear how many dimensions should be included in models for youth with neurodevelopmental conditions. The aim of this study was to delineate the hierarchical dimensional structure of psychopathology in a transdiagnostic sample of children and adolescents with learning-related problems, and to test the concurrent predictive value of the model for clinically, socially, and educationally relevant outcomes. A sample of N = 403 participants from the Centre for Attention Learning and Memory (CALM) cohort were included. Hierarchical factor analysis delineated dimensions of psychopathology from ratings on the Conner's Parent Rating Short Form, the Revised Children's Anxiety and Depression Scale, and the Strengths and Difficulties Questionnaire. A hierarchical structure with a general p factor at the apex, broad internalizing and broad externalizing spectra below, and three more specific factors (specific internalizing, social maladjustment, and neurodevelopmental) emerged. The p factor predicted all concurrently measured social, clinical, and educational outcomes, but the other dimensions provided incremental predictive value. The neurodevelopmental dimension, which captured symptoms of inattention, hyperactivity, and executive function and emerged from the higher-order externalizing factor, was the strongest predictor of learning. This suggests that in struggling learners, cognitive and affective behaviors may interact to influence learning outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
      pubtype: Academic Journal
      doctype: journal article
      src: R
    language: English
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