IDEAS home Printed from https://ideas.repec.org/a/eee/cysrev/v153y2023ics0190740923003122.html
   My bibliography  Save this article

Predicting successful placements for youth in child welfare with machine learning

Author

Listed:
  • Trudeau, Kimberlee J.
  • Yang, Jichen
  • Di, Jiaming
  • Lu, Yi
  • Kraus, David R.

Abstract

Out-of-home placement decisions have extremely high stakes for the present and future well-being of children in care because some placement types, and multiple placements, are associated with poor outcomes. We propose that a clinical decision support system (CDSS) using existing data about children and their previous placement success could inform future placement decision-making for their peers. The objective of this study was to test the feasibility of developing machine learning models to predict the best level of care placement (i.e., the placement with the highest likelihood of doing well in treatment) based on each youth’s behavioral health needs and characteristics. We developed machine learning models to predict the probability of each youth’s treatment success in psychiatric residential care (i.e., Psychiatric Residential Treatment Facility [PRTF]) versus any other placement (AUROCs > 0.70) using data collected in standard care at a behavioral health organization. Placement recommendations based on these machine learning models distinguished between youth who did well in residential care versus non-residential care (e.g., 80% of those who received care in the recommended setting with the highest predicted likelihood of success had above average risk-adjusted outcomes). Then we developed and validated machine learning models to predict the probability of each youth’s treatment success across specific placement types in a state-wide system, achieving an average AUROC score of >0.75. Machine learning models based on risk-adjusted behavioral health and functional data show promise in predicting positive placement outcomes and informing future placement decisions for youth in care. Related ethical considerations are discussed.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:cysrev:v:153:y:2023:i:c:s0190740923003122
    DOI: 10.1016/j.childyouth.2023.107117
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0190740923003122
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.childyouth.2023.107117?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    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. Hee Yun Seol & Pragya Shrestha & Joy Fladager Muth & Chung-Il Wi & Sunghwan Sohn & Euijung Ryu & Miguel Park & Kathy Ihrke & Sungrim Moon & Katherine King & Philip Wheeler & Bijan Borah & James Moriar, 2021. "Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-16, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. Childs, Kristina K. & Bryson, Sara L. & Soderstrom, Melanie F.P. & Reed, April, 2024. "An Assessment of the Internal Structure of the Child and Adolescent Needs and Strengths (CANS) Using Two Samples of High-Risk Adolescents," Children and Youth Services Review, Elsevier, vol. 156(C).
    7. 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.
    8. Mora Ringle, Vanesa A. & Scott Hickey, J. & Jensen-Doss, Amanda, 2019. "Patterns and predictors of compliance with utilization management guidelines supporting a state policy to improve the quality of youth mental health services," Children and Youth Services Review, Elsevier, vol. 96(C), pages 194-203.
    9. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:cysrev:v:153:y:2023:i:c:s0190740923003122. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/childyouth .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.