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A Qualitative Review of Barriers and Facilitators Identified While Implementing the Native Students Together Against
Caitlin Donald1, Kavita Rajani2, Michelle Singer2
1Oregon Health & Science University, Portland, OR, USA.
Abstract:
Culturally-adapted evidence-based programs (EBPs) are needed to promote healthy behaviors among Native teens and young adults. Little is known about the facilitators and barriers of implementing and sustaining EBPs in Native communities. This paper aims to identify those factors described by educators who implemented the Native Students Together Against Negative Decisions (STAND) curriculum.
Methods:
We conducted qualitative, semi-structured interviews with 44 Native STAND educators from 48 sites throughout the United States. We used a modified grounded theory approach to explore barriers, facilitators, and sustainability factors related to implementing Native STAND.
Results:
We learned that disruptions to staffing, coordination, and organizational factors were the most common barriers. Factors that improved implementation success included: tailoring the program to local needs/constraints, having a supportive Project Manager, improved fidelity due to check-in calls, and participation in summer training. Factors that improved sustainability included: access to needed infrastructure, administrative support, community support, and student interest.
Discussion:
The delivery of Native STAND was further improved by person-to-person communication and resource sharing across sites. Sustaining EBPs in AI/AN settings requires culturally-tailored technical assistance, sufficient implementation funds for materials and staffing, and a community of peer educators to inspire forward progress.
Conclusion:
EBPs that reflect the needs and experiences of American Indian and Alaska Native (AI/AN) youth are necessary to address systemic inequities in adolescent health outcomes. The Native STAND Dissemination and Implementation study is among the first to assess facilitators and barriers to program delivery in diverse AI/AN settings.
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