Exploring Machine Learning to Support Decision-Making for Placement Stabilization and Preservation in Child Welfare.
The Family First Prevention Services Act requires youth's placement in residential care to be clinically appropriate, time-limited, and only when youth's needs cannot be met in family-like settings in foster care. State child welfare agencies can benefit from upstream, empirical decision support to...
| Publicado en: | Journal of Child & Family Studies Vol. 34; no. 1; pp. 282 - 298 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Jan2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=182612115&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 182612115 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10621024 JFM jtl: Journal of Child & Family Studies issn: 10621024 maglogo: N pubinfo: dt: Jan2025 vid: 34 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 182612115 10.1007/s10826-024-02993-x ppf: 282 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 721KB tig: atl: Exploring Machine Learning to Support Decision-Making for Placement Stabilization and Preservation in Child Welfare. aug: au: Chor, Ka Ho Brian Luo, Zhidi Rodolfa, Kit T. Ghani, Rayid affil: https://ror.org/024mw5h28 Chapin Hall at the University of Chicago, Chicago, IL, USA https://ror.org/000e0be47 Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA https://ror.org/00f54p054 Regulation, Evaluation, and Governance Lab, Stanford Law School, Stanford, CA, USA https://ror.org/05x2bcf33 Machine Learning Department, Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburg, PA, USA su: Child welfare Foster home care Health planning Health care rationing Random forest algorithms Prediction models Research funding Logistic regression analysis Decision making in clinical medicine Descriptive statistics Machine learning Decision trees Residential care Regression analysis sug: subj: Child welfare Foster home care Health planning Health care rationing Other Residential Care Facilities All other residential care facilities Child and Youth Services Random forest algorithms Prediction models Research funding Logistic regression analysis Decision making in clinical medicine Descriptive statistics Machine learning Decision trees Residential care Regression analysis keyword: Casework practice Decision support Predictive model Studies in Human Society Policy and Administration Social Work Casework practice Decision support Predictive model Studies in Human Society Policy and Administration Social Work ab: The Family First Prevention Services Act requires youth's placement in residential care to be clinically appropriate, time-limited, and only when youth's needs cannot be met in family-like settings in foster care. State child welfare agencies can benefit from upstream, empirical decision support to preempt youth's placement disruption, coordinate proactive placement stabilization services, prevent unnecessary step-up to residential care, and improve outcomes for the youth. This statewide case study explores the potential benefit to child welfare decision support for placement stabilization and diversion from residential care, by comparing predictive machine learning (ML) models with conventional regression models. We analyzed child welfare spells of 12,621 youth in one large Midwestern state between January 2017 and January 2020. Caseworkers could refer youth to a placement stabilization and preservation program. To predict youth's monthly program need in the next 6 months, we developed and validated a wide grid of ML models—random forest, regularized logistic regression, decision tree, dummy classifier—and a conventional unregularized logistic regression model, using literature-informed predictors from child welfare administrative data. We retrained, retested, and compared all models over time using temporal hold-out sets. Based on anticipated program capacity, model evaluation focused on accuracy in identifying the 100 highest-need youth, fairness, and equity of resource allocation. Random forest models produced the best performance with a precision (positive predictive value) 10 times greater than baseline precision. Common important predictors across models included youth's age, history of placement changes, and emotional/behavioral needs. We discuss potential applications of ML to support preventive child welfare decisions, adapt to policy changes, and allocate limited resources. Highlights: Machine learning (ML) predictions can inform preventive services for youth at risk placement disruption in foster care. A wide grid of ML and regression predictive models predicted youth's need for a Midwestern state placement stabilization program. Random forest models consistently outperformed other models; all models were further compared on fairness and equity. Well-designed ML predictive models can support proactive casework decision-making and preventive resource allocation. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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