The Implications of Leveraging Administrative Data for Public Health Approaches to Protecting Children: Sleepwalking into Quicksand?

被引:0
作者
Lonne B. [1 ]
Herrenkohl T.I. [2 ]
Higgins D.J. [3 ]
Scott D. [4 ]
机构
[1] Queensland University of Technology, Brisbane, QLD
[2] School of Social Work, University of Michigan, Ann Arbor, MI
[3] Institute of Child Protection Studies, Australian Catholic University, Melbourne, VIC
[4] Monash University, VIC
关键词
Administrative data; Equity; Ethics; Predictive analytics; Predictive risk modeling; Privacy; Public health;
D O I
10.1007/s42448-022-00126-9
中图分类号
学科分类号
摘要
Critics are raising serious questions about who is “served” by statutory child protection systems if they utilize an intervention model based on reporting, investigation, and removal. Public health approaches present an innovative alternative, but how to get the right support and interventions to the right people at the right time remains challenging. The power of predictive analytics and big data is seductive, yet the risks of bolting on such tools to existing statutory services may serve only to reify or increase inequity and exclusion if they are used to target “vulnerable” children and families for interventions. The use of such new techniques within the framework of statutory child protection services may be like putting new wine into old wineskins. In keeping with a public health approach, the focus, in keeping with a public health approach, should be on the use of population-based data to deliver interventions of variable intensity, aimed at reducing the exposure of the population to risk factors for each of the forms of child abuse and neglect. The use of integrated systems of administrative data with associated sophisticated predictive analytics offers a panoptic view of the causes, complex interactions, consequences, and complications of child maltreatment and our responses to deal with it. Data linkage and predictive analytics have an important and useful role to play in public health approaches to child maltreatment and service delivery but require us to be mindful of amplifying increasing existing inequalities and not making matters worse for those we are trying to assist. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
引用
收藏
页码:501 / 517
页数:16
相关论文
共 66 条
  • [51] Public Trust in Government: 1958–2021, (2022)
  • [52] Risse M., Human rights and artificial intelligence: An urgently needed agenda, Human Rights Quarterly, 41, pp. 1-16, (2019)
  • [53] Roberts D., How the child welfare system destroys Black families–and how abolition can build a safer world, (2022)
  • [54] Roberts Y., O'Brien K., Pecora P., Considerations for implementing predictive analytics in child welfare, Casey Family Programs., (2018)
  • [55] Roger C., The truth about public trust, Open Democracy, (2010)
  • [56] Rowe R., Social determinants of health in the big data mode of population health risk calculation, Big Data & Society, (2021)
  • [57] Scott D., Lonne B., Higgins D., Public health models for preventing child maltreatment: Applications from the field of injury prevention, Trauma, Violence, & Abuse, 17, 4, pp. 408-419, (2016)
  • [58] Sexton A., Shepherd E., Duke-Williams O., Eveleigh A., The role and nature of consent in government administrative data, Big Data & Society, (2018)
  • [59] Sidebotham P., Brandon M., Bailey S., Belderson P., Dodsworth J., Garstang J., Sorenson A., Pathways to harm, pathways to protection: A triennial analysis of serious case reviews 2011 to 2014, (2016)
  • [60] Slack K.S., Berger L.M., Who Is and Is Not Served by Child Protective Services Systems? Implications for a Prevention Infrastructure to Reduce Child Maltreatment, The ANNALS of the American Academy of Political and Social Science, 692, 1, pp. 182-202, (2020)