جمعیت شناسی، سیاست و مراقبت های پرستار؛ یک رویکرد تجزیه و تحلیل پیش بینی کننده

Traditional statistical methods and much prior research have been unable to identify the combinatorial and nonlinear interaction effects of the many suggested factors. A data set of 104 state-level variables was constructed to help answer the question of what accounts for geographic differences in foster care entry rates and average lengths of stay in foster care. A predictive analytics approach (classification and regression trees) was used to sort through all the potential explanatory variables, their interactions, and combinations. The results show that state cultural orientations and socioeconomic facts together best explain foster care entry rates. In contrast, child welfare policy and practice differences together best explain average lengths of stay in foster care. Interventions aimed at goals relating to who goes into foster care and how many children go into foster care might be most effective if they focus on culture and socioeconomic facts. Interventions aimed to change lengths of time in care, on the other hand, might be most effective if targeted at state child welfare policies and practices.