biostudies-literatureUnknownChen YEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNICHD NIH HHSDespite well-studied benefits of preventive parenting programs for early child development, various real-world barriers may impede families from engaging in those programs. The current study aims to provide new insights into family engagement by examining enrollment, retention, and involvement and their predictors in an evidence-based universal pediatric primary care parenting program for families with young children. Data (n = 204) were from an ongoing longitudinal randomized controlled trial of PlayReadVIP in Flint, MI. Families (66% Black, 35% White, low socioeconomic status) had high enrollment and retention in the program. As exploratory analyses, random forest models, a machine learning method, identified a multitude of sociodemographic, psychosocial, and contextual predictors of retPrevention science : the official journal of the Society for Prevention Researchhttps://www.ebi.ac.uk/biostudies/studies/S-EPMC12812042biostudies-literatureFamily Engagement in an Early Childhood Preventive Parenting Program: Innovative Methods for Examining Sociodemographic, Psychosocial, and Contextual Predictors.PMC12812042R01HD096909R01 HD096909Gutierrez JFinegood EDO'Connell LKChen YLoney KCanfield CFMendelsohn AMilton AfalseFamily Engagement in an Early Childhood Preventive Parenting Program: Innovative Methods for Examining Sociodemographic, Psychosocial, and Contextual Predictors.Despite well-studied benefits of preventive parenting programs for early child development, various real-world barriers may impede families from engaging in those programs. The current study aims to provide new insights into family engagement by examining enrollment, retention, and involvement and their predictors in an evidence-based universal pediatric primary care parenting program for families with young children. Data (n = 204) were from an ongoing longitudinal randomized controlled trial of PlayReadVIP in Flint, MI. Families (66% Black, 35% White, low socioeconomic status) had high enrollment and retention in the program. As exploratory analyses, random forest models, a machine learning method, identified a multitude of sociodemographic, psychosocial, and contextual predictors of ret2026-01-01T00:00:00Z2026 Jan2026-06-11T05:27:02.797Z2026-06-11T03:08:02.631ZS-EPMC128120424149907510.1007/s11121-025-01873-0