Abstract
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.
Highlights
IntroductionBennett – substantial contributions to study design and interpretation of data, and final approval of the version to be published
General Scientific Summary This study identifies dimensions of psychopathology in a sample of children with neurodevelopmental difficulties who are at increased risk for mental health problems
The maximum number of factors to extract was determined with parallel analyses (extraction was stopped when eigenvalues fell within the 95% confidence interval (CI) of eigenvalues from simulated data)
Summary
Bennett – substantial contributions to study design and interpretation of data, and final approval of the version to be published. Jacalyn Guy – substantial contributions to study design and conception, acquisition, analysis and interpretation of data, and drafting results, revising and approving the final version to be published. The traditional diagnostic rubric endorsed by international classification systems such as the Diagnostic Statistical Manual of Mental Disorders-Fifth Edition (DSM–5; American Psychiatric Association, 2013) defines mental disorders as distinct and discrete categories This categorical approach runs counter to a wealth of clinical and research evidence showing that disorders are highly comorbid, heterogeneous, variable across development and the lifespan, explained by multiple causes, and not captured by a cardinal set of symptoms (Dalgleish et al, 2020). Dimensional, approach emphasizes the importance of continuous factors that span the full range of functioning, from adaptive to maladaptive, that can cut across traditional categories of mental ill health (e.g., Caspi et al, 2014; Caspi & Moffitt, 2018; Lahey et al, 2012, 2017; Martel et al, 2017; Patalay et al, 2015)
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