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E-Poster

Student Presentations

Pathways to Thrive: Mapping Multisystemic Resilience Across Youth, Families and Communities for Equitable Health

Using a nationally representative dataset, this study uses machine learning to examine how family structure, resilience factors, and discrimination-related adverse childhood experiences (ACEs) interact with children’s health outcomes. Higher resilience—family cohesion, responsive healthcare, and community resources—reduced ACE effects, highlighting key targets for culturally responsive interventions to promote health equity.

Learning Objectives:

At the end of this session, attendees should be able to:
  1. Define how discrimination-related ACEs and family structure interact to impact children’s mental, physical, and socioemotional health.
  2. Analyze hierarchical regression and value-added modeling findings to identify key resilience factors that buffer against adverse outcomes.
  3. Evaluate and propose culturally responsive intervention strategies that strengthen family cohesion, healthcare responsiveness, and community resources.