Treatment outcomes for children receiving mental health services at community-based clinics have been inconsistent. There is an urgent need to study treatment effectiveness and to identify factors that influence symptom changes. Previous research has demonstrated the impact of environmental risks on children’s functioning, and research is needed to understand the effects of risks on mental health treatment outcomes. In partnership with a community-based mental health clinic, this study aimed to (a) determine whether a racially-diverse sample of children (N = 1176; 59.4% male; ages 4-17 years) demonstrated post-treatment symptom reduction, (b) identify environmental risk subgroups of children through latent class analysis, and (c) assess for subgroup differences on outcome change scores. Paired sample t-tests were used to test for significant change over time between pre- and post- treatment symptoms levels and between high- and post- treatment symptoms levels. Significant symptom reduction was observed over time, as assessed by the Strengths and Difficulties Questionnaire. Latent class analysis was used to identify and define environmental risk subgroups. Fit indices and theoretical constructs conjointly endorsed four parsimonious latent classes: Low-Risk, High-Poverty, High-Risk, and Low-Poverty with Maltreatment. An ANCOVA was used to test whether the four classes differed on their respective change scores; no significant differences were found. Findings indicate meaningful symptom reduction after treatment and the existence of meaningful subgroups of children based upon risks. However, there is inadequate evidence that symptom changes vary based upon environmental risk classes. Implications of findings for clinical practice and future research are discussed.
University of Minnesota Ph.D. dissertation. October 2017. Major: Family Social Science. Advisors: Elizabeth Wieling, Lindsey Weiler. 1 computer file (PDF); vii, 106 pages.
Environmental Risks and Children’s Mental Health Treatment Outcomes: A Person-Centered Analysis.
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