Methods: The evaluation assesses core domains of the Consolidated Framework for Implementation Research (CFIR): innovation, outer setting, inner setting, individual characteristics, and implementation process. Data come from: meeting notes of the research team; discussions with site project coordinators, their teams, and leadership; cross-site debriefings; and workshop feedback forms completed by P2W program facilitators and parent/guardian participants. Data were collected weekly or biweekly from the initial project planning phases through the first two cycles of implementing P2W, providing a lens into real-time implementation challenges and solutions.
Results: Initially, a pressing implementation challenge arose in the CFIR process domain: recruitment and retention. The urban Indian centers had post-pandemic problems in hiring program staff, and in recruiting and retaining eligible parents/guardians for an in-person program. Within the CFIR innovation domain, concerns emerged about adequate cultural tailoring of P2W to tribally specific practices, and use of a non-culturally tailored comparison intervention. In the CFIR outer domain, centers faced major disruptions: discovery of asbestos contamination at their facility, and COVID-19 resurgence, leading to center lockdowns and rescheduled workshops. Under CFIR’s inner setting domain, sites confronted technology problems in electronic recruitment reporting and survey administration. Lastly, within the CFIR individuals domain, issues of staff training and workload allocation and coordination complicated the implementation launch. The presentation will highlight how cross-site meetings helped the urban Indian centers share troubleshooting solutions to implementation challenges.
Conclusions and Implications: This study offers insights into the first attempt to test parenting programs like P2W across sites nationwide. Despite unexpected barriers in the implementation, the urban Indian centers had the capacity to solve issues with help from each other and the research team. By documenting these challenges, the project aims to contribute knowledge to implementation science which will be of use to urban AI communities.