Abstract
Preventing child abuse is a unifying goal. Making decisions that affect the lives of children is an unenviable task assigned to social services in countries around the world. The consequences of incorrectly labelling children as being at risk of abuse or missing signs that children are unsafe are well-documented. Evidence-based decision-making tools are increasingly common in social services provision but few, if any, have used social network data. We analyse a child protection services dataset that includes a network of approximately 5 million social relationships collected by social workers between 1996 and 2016 in New Zealand. We test the potential of information about family networks to improve accuracy of models used to predict the risk of child maltreatment. We simulate integration of the dataset with birth records to construct more complete family network information by including information that would be available earlier if these databases were integrated. Including family network data can improve the performance of models relative to using individual demographic data alone. The best models are those that contain the integrated birth records rather than just the recorded data. Having access to this information at the time a child’s case is first notified to child protection services leads to a particularly marked improvement. Our results quantify the importance of a child’s family network and show that a better understanding of risk can be achieved by linking other commonly available datasets with child protection records to provide the most up-to-date information possible.
Highlights
Predictive risk models are increasingly used in child protection services to assess the risk of maltreatment[1,2,3]
Using data from child protection services in New Zealand, we show that information about a child’s family network can improve the performance of predictive risk models used by social workers to make an initial decision about whether to refer a case for further investigation
Each relationship connects a pair of individuals in the database and is categorised into one of 134 relationship types, for example A is the mother of C, B is the stepfather of C, C is the sibling of D, C was physically abused by B. (See sec 1.1 of S1 Text for details and Tables B-E in S1 Text a complete list of the 134 relationships types.) Relationships are timestamped with the date of their addition to the database rather than the date the relationship began, which may have been much earlier
Summary
Predictive risk models are increasingly used in child protection services to assess the risk of maltreatment[1,2,3] Such models have been operationalized in a number of administrative jurisdictions including in Allegheny County, Pennsylvania [4,5] and in Florida [6]. This mirrors an increase in the use of predictive risk models in other social service areas, such as health[7,8,9], justice[10,11,12] and social security[13]. Predictive risk models have been criticised as “individualising social problems, reifying risk and abuse and narrowly prescribing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
