Framework for Reporting Adaptations and Modifications to Evidence-Based Implementation Strategies for Pediatric Early Warning Scores

Implementation strategies are adapted to improve fit of evidence-based practices for resource-limited settings, yet how they are adapted is poorly understood. We described the adaptation of a resource-adapted multilevel strategy to implement Pediatric Early Warning Scores in centers caring for children with cancer in Latin America for pilot predominantly in Sub-Saharan Africa. This exploratory, sequential mixed-methods study identified strategy adaptations through content analysis of (1) focus group discussions, (2) informal interviews, and (3) adaptation process documents. An external investigator developed a preliminary matrix of adaptations and then applied the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies. The implementation team iteratively reviewed and reached consensus through focus group discussions. The final matrix had 24 adaptations; most were proactively planned by the implementation team (67%, N = 16) to reduce implementation strategy content, timeline, and facilitation intensity. Adaptations (21%, N = 5) that reduced facilitation intensity reverted to the original strategy’s intensity due to poor fit. Context adaptations changed format, setting, and staffing (38%, N = 9). An adaptive, virtual platform for asynchronous learning was incorporated to streamline training and data collection (42%, N = 10). Minor changes were made to the framework’s language to best describe adaptations. This study underscores how strategies designed for resource-limited settings are adapted for novel use across contexts. Detailing adaptations may inform future strategy adaptation to improve evidence-based practice fit and sustainability. Additional research should evaluate how adapted strategies facilitate implementation and global scale-up of evidence-based practices like Pediatric Early Waring Scores for early identification of deteriorating children in resource-limited settings.