Validating a Predictive Risk Model for Child Abuse and Neglect With Adolescent Outcomes.
Introduction: A predictive risk model (PRM) was trained to stratify risk among children investigated for alleged maltreatment based on the likelihood of future child protection involvement. In the current brief, we assess the model's ability to differentiate risk of adverse events not used to build...
| Publicado en: | Journal of Adolescence Vol. 98; no. 6; pp. 2150 - 2155 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Wiley-Blackwell
Aug2026
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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=195930398&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 195930398 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 01401971 JAA jtl: Journal of Adolescence issn: 01401971 maglogo: N pubinfo: dt: Aug2026 vid: 98 iid: 6 pid: 480 pub: Wiley-Blackwell artinfo: ui: 195930398 10.1002/jad.70195 ppf: 2150 ppct: 5 formats: tig: atl: Validating a Predictive Risk Model for Child Abuse and Neglect With Adolescent Outcomes. aug: au: Prindle, John Ahn, Eunhye Palmer, Lindsey Eastman, Andrea Lane Putnam‐Hornstein, Emily affil: University of Southern California, Los Angeles California,, USA University of Wisconsin‐Madison, Madison Wisconsin,, USA University of Utah, Salt Lake City Utah,, USA University of North Carolina at Chapel Hill, Chapel Hill North Carolina,, USA su: Arrest Adolescent development Mortality Child abuse Child welfare Prognostic models Random forest algorithms sug: subj: Arrest Adolescent development Mortality Child abuse Child welfare Prognostic models Random forest algorithms keyword: arrest dual system involvement juvenile justice predictive risk model probation supervised foster care risk assessment arrest dual system involvement juvenile justice predictive risk model probation supervised foster care risk assessment ab: Introduction: A predictive risk model (PRM) was trained to stratify risk among children investigated for alleged maltreatment based on the likelihood of future child protection involvement. In the current brief, we assess the model's ability to differentiate risk of adverse events not used to build the model (i.e., arrest, death) among adolescent populations investigated following reported maltreatment to guide prevention‐oriented services. Methods: Child welfare and vital statistics records were obtained through a data use agreement. Among adolescents born in 2000 and 2001 and investigated for alleged maltreatment between ages 11 and 17 (n = 72,340), risk scores were calculated using a random forest algorithm based on information available at the time of maltreatment report. The records of these adolescents were then linked to arrest and death records. Results: Among adolescents investigated for maltreatment, 5.8% experienced a juvenile arrest or death before age 21. Of those who experienced an arrest or death, 43.9% fell in the highest risk decile. Conclusions: A PRM trained to predict foster care placement had strong external validity in predicting both future arrests and deaths. The average time from investigation to adverse event indicates a meaningful window for interventions to be delivered focused on supporting and stabilizing adolescents and their families. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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