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Predictive modeling: potential application in prevention services.

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This summary is machine-generated.

Predictive risk models (PRMs) show promise in identifying children at risk of maltreatment for early intervention. While effective, PRMs should complement, not replace, professional judgment in child protection services.

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Area of Science:

  • Child welfare research
  • Public health informatics
  • Social policy analysis

Background:

  • New Zealand proposed predictive risk models (PRMs) in 2012 for early intervention in child abuse/neglect.
  • The proposal aimed to identify at-risk children using population-wide linked administrative data.

Purpose of the Study:

  • To examine the technical feasibility and predictive validity of a PRM for identifying newborn children needing intensive preventive services.
  • To assess the PRM's effectiveness in predicting child maltreatment.

Main Methods:

  • Developed a PRM using linked administrative data for children born in 2010.
  • Externally validated the PRM using data for children born in 2007, tracking outcomes to age 5.
  • Data analysis was conducted in 2013 on data from 2000-2012.

Main Results:

  • The PRM demonstrated good performance in predicting administratively recorded child maltreatment.
  • Performance was effective both overall and specifically for indigenous Māori children.

Conclusions:

  • PRMs can identify some children at risk of maltreatment early, but not all.
  • PRMs should augment, not replace, professional judgment in child protection.
  • Further trials are necessary to evaluate PRM impact on practice, services, and child outcomes, considering ethical and privacy risks.