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Julia Chavez1, BA, Jessica Cheng1, BA, Amelie Liu2, HSD, Alexis Hansen3, SWLC, Jesse Boring3, PhD, Irwin Sandler5,6, PhD, and Dana Suskind7, MD
1Pritzker School of Medicine, University of Chicago, Chicago, Illinois
2University of Chicago Laboratory Schools, University of Chicago, Chicago, Illinois
3Crown Family School of Social Work, Policy, and Practice, University of Chicago, Chicago, Illinois
4Psychology and Human Services Department, SUNY Broome Community College, Binghamton, New York
5REACH Institute, Arizona State University, Tempe, Arizona
6Department of Psychology, Arizona State University, Tempe, Arizona
7Department of Surgery, University of Chicago Medicine, Chicago, Illinois, United States of America
Chapter DOI: https://doi.org/10.52305/PCWD6734
Parent loss during childhood is a profoundly stressful life event affecting one in 25 individuals in the United States. Early parent loss places children at risk for both short- and long-term negative health and life outcomes, including mental health issues (e.g., depression, anxiety, post traumatic stress disorder) and lower academic success. Given the potential negative sequelae across the lifespan, timely intervention is critical. In this study, we used human-centered design (HCD) to develop an evidence-based intervention for teens who have lost a parent, using the Family Bereavement Program (FBP) as a model. A new peer-led delivery model was introduced to the curriculum, as well as the incorporation of technology and mindfulness strategies. We recruited researchers and members of the target population to provide feedback on the curriculum in focus groups. The results of focus group testing indicated that there are theory-practice gaps within post-bereavement interventions that can be identified and redressed through humancentered design framework. The focus group feedback provided preliminary support for the peer-led curriculum for parentally-bereaved teens as well as suggestions for adaptation and modifications. Future work will be centered around further refining the model ensuring fidelity and scalability of the intervention.
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