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
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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