Should Clinicians Split or Lump Psychiatric Symptoms? The Structure of Psychopathology in Two Large Pediatric Clinical Samples from England and Norway.

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Title: Should Clinicians Split or Lump Psychiatric Symptoms? The Structure of Psychopathology in Two Large Pediatric Clinical Samples from England and Norway.
Authors: Fernández de la Cruz, Lorena, Simonoff, Emily, Goodman, Robert, Vidal-Ribas, Pablo, Zahreddine, Nada, Stringaris, Argyris, Mathiassen, Børge, Brøndbo, Per Håkan
Source: Child Psychiatry & Human Development. Aug2018, Vol. 49 Issue 4, p607-620. 14p. 2 Diagrams, 4 Charts, 1 Graph.
Subjects: Pathological psychology, Child psychiatry, Strengths & Difficulties Questionnaire, Child development, Epidemiology
Abstract: It has been suggested that the structure of psychiatric phenomena can be reduced to a few symptom dimensions. These proposals, mainly based on epidemiological samples, may not apply to clinical populations. We tested the structure of psychiatric symptoms across two pediatric clinical samples from England (N = 8434) and Norway (N = 5866). Confirmatory factor analyses of the parent-reported Strengths and Difficulties Questionnaire (SDQ) evaluated the relative fit of several models, including a first-order model, a second-order model with the widely-established broad symptom dimensions of internalizing-externalizing, and two bi-factor models capturing a general psychopathology factor. Predictive value of the SDQ subscales for psychiatric disorders was examined. A first-order five-factor solution better fit the data. The expected SDQ subscale(s) related best to the corresponding psychiatric diagnosis. In pediatric clinical samples, a granular approach to psychiatric symptoms where several dimensions are considered seems to fit the data better than models based on lumping symptoms into internalizing/externalizing dimensions. [ABSTRACT FROM AUTHOR]
Copyright of Child Psychiatry & Human Development is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Child+Psychiatry+%26+Human+Development%22">Child Psychiatry & Human Development</searchLink>. Aug2018, Vol. 49 Issue 4, p607-620. 14p. 2 Diagrams, 4 Charts, 1 Graph.
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  Data: It has been suggested that the structure of psychiatric phenomena can be reduced to a few symptom dimensions. These proposals, mainly based on epidemiological samples, may not apply to clinical populations. We tested the structure of psychiatric symptoms across two pediatric clinical samples from England (N = 8434) and Norway (N = 5866). Confirmatory factor analyses of the parent-reported Strengths and Difficulties Questionnaire (SDQ) evaluated the relative fit of several models, including a first-order model, a second-order model with the widely-established broad symptom dimensions of internalizing-externalizing, and two bi-factor models capturing a general psychopathology factor. Predictive value of the SDQ subscales for psychiatric disorders was examined. A first-order five-factor solution better fit the data. The expected SDQ subscale(s) related best to the corresponding psychiatric diagnosis. In pediatric clinical samples, a granular approach to psychiatric symptoms where several dimensions are considered seems to fit the data better than models based on lumping symptoms into internalizing/externalizing dimensions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Child Psychiatry & Human Development is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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