Abstract for: Preventing Adoption Instability for Foster Care Children: Understanding System Structure and Exploring Leverage Points

Achieving long-term stability for children adopted from foster care is a critical yet complex challenge in the United States. Adoption instability harms children’s emotional and developmental well-being while placing significant burdens on adoptive families and child welfare systems. However, current research focuses predominantly on linear predictors of "failure" rather than the systemic structures that perpetuate instability, hindering the identification of interventions needed to sustain long-term stability. This study uses system dynamics simulation modeling to analyze the structures and feedback loops driving adoption instability across five phases. Phase 0 defines dynamic problems using reference modes; Phase 1 conceptualizes the model through causal loop diagrams; Phase 2 converts these into stock-and-flow diagrams in Stella. Phase 3 builds model confidence through structure assessment, behavioral reproduction, and sensitivity analysis. Phase 4 conducts policy analysis using the "Loops that Matter" approach to identify high-leverage intervention points. This study is currently at Phase 1, model conceptualization. Initial conceptual modeling has identified key feedback loops, such as the "Caregiver Stress-Child Behavior" reinforcing loop, in which child behavioral challenges can lead to caregiver fatigue, thereby reducing parenting capacity and exacerbating adoption instability. The model also captures system delays in service delivery, which often result in families receiving support only after a crisis has escalated. This study will further generate model insights based on key reinforcing and feedback loops. This study aims to explore the underlying system structure that influences adoption instability, identify key feedback loops driving it over time, and test a set of structural interventions with high leverage for reducing or preventing such instability using system dynamics simulation modeling. Overall, the model seeks to shift the focus from reactive crisis management to preventive system-level interventions by identifying where and when additional services can be most effectively leveraged.