 Machine learning and public policy: Early detection of physical violence against childrenMaría Edo, Victoria Oubiña and Marcela Svarc Children and Youth Services Review, 2024, vol. 166, issue C Abstract: Physical violence against children is a widespread and grossly underreported phenomenon with substantial short and long-term negative consequences. In Latin America and the Caribbean, 43% of children under the age of 15 experience corporal punishment at home, yet reporting rates are alarmingly low. This paper aims to demonstrate how household data can be considered for a future predictive analytics model in Argentina. Based on the 2019–20 MICS survey we apply machine learning techniques to predict physical violence against children (understood as physical discipline) at the household level in Argentina. The scope and potential benefits of using predictive models in this context are assessed, as well as the main limitations and risks. The results suggest that, by analyzing the situation of the 30% of households with the highest risk scores, 43 out of 100 households in which children experience physical violence could be identified at an early stage. Keywords: Physical punishment; Child protection; Predictive models; Interventions (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:166:y:2024:i:c:s0190740924005048 DOI: 10.1016/j.childyouth.2024.107932 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.