Using family network data in child protection services
Autor: | S J Nik, Kip Marks, Shaun C. Hendy, Alex James, Jeanette C. McLeod, Michael J. Plank, Delia Rusu |
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Rok vydání: | 2019 |
Předmět: |
Child abuse
Male Epidemiology Poison control Social Sciences Social Welfare Criminology Suicide prevention Pediatrics Geographical locations Families 0302 clinical medicine Mathematical and Statistical Techniques Cognition Sociology Medicine and Health Sciences Psychology Ethnicities Public and Occupational Health Child Abuse Human Families Child Children Multidisciplinary Social work 05 social sciences Traumatic Injury Risk Factors Statistics Records Child protection Birth Certificates Child Preschool Physical Sciences Medicine Female Crime 050104 developmental & child psychology Research Article Social Work Adolescent Science Internet privacy Decision Making Oceania Child Welfare Research and Analysis Methods 03 medical and health sciences Social support 030225 pediatrics Humans 0501 psychology and cognitive sciences Family Statistical Methods Social network business.industry Child Protective Services Cognitive Psychology Infant Social Support Biology and Life Sciences Age Groups Medical Risk Factors People and Places Cognitive Science Population Groupings business Mathematics New Zealand Forecasting Neuroscience |
Zdroj: | PLoS ONE PLoS ONE, Vol 14, Iss 10, p e0224554 (2019) |
ISSN: | 1932-6203 |
Popis: | 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. |
Databáze: | OpenAIRE |
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