 Exploring the protective factors of children and families identified at highest risk of adverse childhood experiences by a predictive risk model: An analysis of the growing up in New Zealand cohortMatthew C. Walsh, Sophie Joyce, Tim Maloney and Rhema Vaithianathan Children and Youth Services Review, 2020, vol. 108, issue C Abstract: With increasing access to integrated administrative data, and advances in predictive analytics, it is both theoretically possible and practically feasible to use predictive risk models (PRMs) to automatically risk stratify entire birth-cohorts as to their risk of experiencing multiple adversities in childhood (Vaithianathan et al., 2013, 2018; Rouland & Vaithianathan, 2018). Such automated screening tools allow agencies to identify families at highest risk and offer them preventive services in a timely fashion. However, little is known about what protective factors might exist amongst families who are identified as high risk by PRMs. Identifying protective factors is an important step in designing preventive services for families identified by PRM tools as well as helping social workers take a strengths-based approach to these families. Keywords: Adverse childhood experience; Child welfare policy and practice; Protective factors predictive analytics; Administrative data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:cysrev:v:108:y:2020:i:c:s0190740919302968 DOI: 10.1016/j.childyouth.2019.104556 Access Statistics for this article Children and Youth Services Review is currently edited by Duncan Lindsey More articles in Children and Youth Services Review from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.