Predictive and prescriptive analytics, machine learning and child welfare risk assessment: The Broward County experience

Ira M. Schwartz, Peter York, Eva Nowakowski-Sims and Ana Ramos-Hernandez

Children and Youth Services Review, 2017, vol. 81, issue C, 309-320

Abstract: This paper presents the findings from a study designed to explore whether predictive analytics and machine learning could improve the accuracy and utility of the child welfare risk assessment instrument used in Broward County (Ft. Lauderdale, Florida). The findings from this study indicate that, indeed, predictive analytics and machine learning would significantly improve the accuracy and utility of the child welfare risk assessment instrument being used. If the predictive analytic and machine learning algorithms developed in this study would be deployed, there would be improved accuracy in identifying low, moderate and high risk cases, better matching between the needs of children and families and available services and improved child and family outcomes. This paper also identifies further areas of research and study.

Keywords: Child Welfare; Analytics; Machine Learning (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0190740917303523
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:cysrev:v:81:y:2017:i:c:p:309-320

DOI: 10.1016/j.childyouth.2017.08.020

Access Statistics for this article

Children and Youth Services Review is currently edited by Duncan Lindsey

More articles in Children and Youth Services Review from Elsevier
Bibliographic data for series maintained by Catherine Liu ().