22 research outputs found

    The wide-ranging life outcome correlates of a general psychopathology factor in adolescent psychopathology

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    Background: The structure of psychopathology has been much debated within the research literature. This study extends previous work by providing comparisons of the links between psychopathology and several life outcomes (temperamental, economic, social, psychological and health) using a three-correlated-factors model, a bifactor model, a revised-bifactor model and a higher-order model. Methods: Data from a sample of Dutch adolescents were used (n = 2 230), and psychopathology factors were modelled using self-reported and parent-reported longitudinal data from youth across four assessments during adolescence, from ages 11 to 19. Outcome variables were assessed at age 22 using adolescent-reports and parent-reports and more objective measures (e.g. body mass index). Results: While no measurement model was clearly superior, we found modest associations between the psychopathology factors and life outcomes. Importantly, after taking into account a general factor, the associations with life outcomes decreased for the residual parts of thought problems (across all domains) and internalizing problems (for temperamental and psychological outcomes), but not for externalizing problems, compared with the traditional three-correlated-factors model. Patterns were similar for adolescent-reported and parent-reported data. Conclusions: Findings suggest that a general factor is related to psychopathology and life outcomes in a meaningful way. Results are discussed in terms of individual differences in propensity to psychopathology and more broadly in light of recent developments concerning the structure of psychopathology

    Measurement Bias in Caregiver-Report of Early Childhood Behavior Problems across Demographic Factors in an Echo-Wide Diverse Sample

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    BACKGROUND: Research and clinical practice rely heavily on caregiver-report measures, such as the Child Behavior Checklist 1.5-5 (CBCL/1.5-5), to gather information about early childhood behavior problems and to screen for child psychopathology. While studies have shown that demographic variables influence caregiver ratings of behavior problems, the extent to which the CBCL/1.5-5 functions equivalently at the item level across diverse samples is unknown. METHODS: Item-level data of CBCL/1.5-5 from a large sample of young children ( RESULTS: Items with the most impactful DIF across child and caregiver groupings were identified for Internalizing, Externalizing, and total Problems. The robust item sets, excluding the high DIF items, showed good reliability and high correlation with the original Internalizing and total Problems scales, with lower reliability for Externalizing. Language version of CBCL administration, education level and sex of the caregiver respondent showed the most significant impact on MI, followed by child age. Sensitivity analyses revealed that child race has a unique impact on DIF over and above socioeconomic status. CONCLUSIONS: The CBCL/1.5-5, a caregiver-report measure of early childhood behavior problems, showed bias across demographic groups. Robust item sets with less DIF can measure Internalizing and total Problems equally as well as the full item sets, with slightly lower reliability for Externalizing, and can be crosswalked to the metric of the full item set, enabling calculation of normed T scores based on more robust item sets

    Life satisfaction for adolescents with developmental and behavioral disabilities during the COVID-19 pandemic

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    Background: This study aimed to identify contextual factors associated with life satisfaction during the COVID-19 pandemic for adolescents with mental, emotional, behavioral, and developmental (MEBD) disabilities. Methods: Data were collected from a sample of 1084 adolescents aged 11–21 years from April 2020 to August 2021. This cross-sectional study used a sequential machine learning workflow, consisting of random forest regression and evolutionary tree regression, to identify subgroups of adolescents in the Environmental influences on Child Health Outcomes (ECHO) consortium who demonstrated enhanced vulnerability to lower life satisfaction as described by intersecting risk factors, protective factors, and MEBD disabilities. Results: Adolescents with a history of depression, anxiety, autism, and attention-deficit/hyperactivity disorder were particularly susceptible to decreased life satisfaction in response to unique combinations of stressors experienced during the COVID-19 pandemic. These stressors included decreased social connectedness, decreased family engagement, stress related to medical care access, pandemic-related traumatic stress, and single-caregiver households. Conclusion: Findings from this study highlight the importance of interventions aimed specifically at increasing adolescent social connectedness, family engagement, and access to medical support for adolescents with MEBD disabilities, particularly in the face of stressors, such as a global pandemic. Impact: Through a machine learning process, we identified contextualized risks associated with life satisfaction among adolescents with neurodevelopmental disabilities during the COVID-19 pandemic.The COVID-19 pandemic resulted in large-scale social disruptions for children and families. Such disruptions were associated with worse mental health outcomes in the general pediatric population, but few studies have examined specific subgroups who may be at heightened risk. We endeavored to close that gap in knowledge.This study highlights the importance of social connectedness, family engagement, and access to medical support as contributing factors to life satisfaction during the COVID-19 pandemic for adolescents with neurodevelopmental disabilities

    The impact of COVID-19 school disruptions on children’s learning

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    IntroductionNational health policies to stop the spread of the COVID-19 virus in the US resulted in widespread school closures and disrupted learning in Spring 2020.MethodsThis study draws on unique individual-level data from n = 282 5–12 year olds enrolled in the NIH Environmental influences on Child Health Outcomes (ECHO) Research Program to investigate associations between caregiver-reported duration of Spring 2020 learning disruptions and academic achievement.ResultsLinear regression analyses estimated that children who experienced more than 4 weeks of instruction disruptions in Spring 2020 scored 4.5 points [95% CI: −8.77, −0.22] lower on age-normed math assessments compared to peers who had four or fewer weeks of disruption, adjusting for sociodemographic variables, pre-pandemic vocabulary, and COVID-19 family hardships and stress. No differences were found for reading. Children whose caregivers had higher levels of pandemic-related traumatic stress and lower educational attainment also had lower math scores, adjusting for all other covariates.DiscussionResults suggest educators and schools focus additional attention on supporting math instruction for children who experienced extended learning disruptions

    A Multiple Imputation Score Test for Model Modification in Structural Equation Models

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    Structural equation modeling (SEM) applications routinely employ a trilogy of significance tests that includes the likelihood ratio test, Wald test, and score test or modification index. Researchers use these tests to assess global model fit, evaluate whether individual estimates differ from zero, and identify potential sources of local misfit, respectively. This full cadre of significance testing options is not yet available for multiply imputed data sets, as methodologists have yet to develop a general score test for this context. Thus, the goal of this article is to outline a new score test for multiply imputed data. Consistent with its complete-data counterpart, this imputation-based score test provides an estimate of the familiar expected parameter change statistic. The new procedure is available in the R package semTools and naturally suited for identifying local misfit in SEM applications (i.e., a model modification index). The article uses a simulation study to assess the performance (Type I error rate, power) of the proposed score test relative to the score test produced by full information maximum likelihood (FIML) estimation. Due to the two-stage nature of multiple imputation, the score test exhibited slightly lower power than the corresponding FIML statistic in some situations but was generally well calibrated
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