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Individual and provider effects on mental health outcomes in child welfare: A three level growth curve approach

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  • Sieracki, Jeffrey H.
  • Leon, Scott C.
  • Miller, Steven A.
  • Lyons, John S.

Abstract

Approaches for treating children and adolescents with emotional and behavioral disorders within their communities have been implemented in counties, cities, and states throughout the United States. The goal of this study was to model course of improvement for individuals enrolled in a statewide community treatment program. Five hundred and sixty three children and adolescents (mean = 11.6 years at time of initial contact) receiving community-based services from 26 different agencies throughout Illinois were evaluated using the Child and Adolescent Needs and Strengths (CANS) measure. Hierarchical linear modeling (HLM) was applied to three levels of data: time (months in care), child-level (clinical, demographic data), and provider agency; the problem behaviors factor score of the CANS served as the measure of outcome. The results indicated that months in care, time 1 problem behavior score, caregiver needs and strengths, youth strengths, and school problems predicted course of improvement at the child level. Results also indicated that agencies (level 3) differed in client problem behavior reduction; however, this effect was much smaller than has been observed in other populations. Implications for service organization and delivery are discussed.

Suggested Citation

  • Sieracki, Jeffrey H. & Leon, Scott C. & Miller, Steven A. & Lyons, John S., 2008. "Individual and provider effects on mental health outcomes in child welfare: A three level growth curve approach," Children and Youth Services Review, Elsevier, vol. 30(7), pages 800-808, July.
    • Handle: RePEc:eee:cysrev:v:30:y:2008:i:7:p:800-808
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    Cited by:

    1. Troy, Jesse D. & Torrie, Ryan M. & Warner, Daniel N., 2021. "A machine learning approach for identifying predictors of success in a Medicaid-funded, community-based behavioral health program using the Child and Adolescent Needs and Strengths (CANS)," Children and Youth Services Review, Elsevier, vol. 126(C).
    2. Kraus, David R. & Baxter, Elizabeth E. & Alexander, Pamela C. & Bentley, Jordan H., 2015. "The Treatment Outcome Package (TOP): A multi-dimensional level of care matrix for child welfare," Children and Youth Services Review, Elsevier, vol. 57(C), pages 171-178.
    3. Summersett, Faith C. & Jordan, Neil & Griffin, Gene & Kisiel, Cassandra & Goldenthal, Hayley & Martinovich, Zoran, 2019. "An examination of youth protective factors and caregiver parenting skills at entry into the child welfare system and their association with justice system involvement," Children and Youth Services Review, Elsevier, vol. 99(C), pages 23-35.
    4. Trudeau, Kimberlee J. & Yang, Jichen & Di, Jiaming & Lu, Yi & Kraus, David R., 2023. "Predicting successful placements for youth in child welfare with machine learning," Children and Youth Services Review, Elsevier, vol. 153(C).
    5. Jennifer Pickett & Joeri Hofmans & Jonas Debusscher & Filip Fruyt, 2020. "Counterdispositional Conscientiousness and Wellbeing: How Does Acting Out of Character Relate to Positive and Negative Affect At Work?," Journal of Happiness Studies, Springer, vol. 21(4), pages 1463-1485, April.
    6. Dunleavy, Alison M. & Leon, Scott C., 2011. "Predictors for resolution of antisocial behavior among foster care youth receiving community-based services," Children and Youth Services Review, Elsevier, vol. 33(11), pages 2347-2354.
    7. Rosanbalm, Katie D. & Snyder, Elizabeth H. & Lawrence, C. Nicole & Coleman, Kanisha & Frey, Joseph J. & van den Ende, Johanna B. & Dodge, Kenneth A., 2016. "Child wellbeing assessment in child welfare: A review of four measures," Children and Youth Services Review, Elsevier, vol. 68(C), pages 1-16.
    8. Jonas Debusscher & Joeri Hofmans & Filip De Fruyt, 2014. "The Curvilinear Relationship between State Neuroticism and Momentary Task Performance," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-16, September.

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