Designing and Implementing the Adaptive Learning Meeting Cycle: The (re)solve Project Experience in Burkina Faso

Abstract

Through the collection and utilization of timely data, program implementers can review feedback and make rapid adjustments and adaptations to interventions while they are in progress.Responsive feedback mechanisms (RFMs), which emphasize flexibility and iterative adaptation, provide opportunities for improving the fit and feasibility of a program, as well as its effectiveness. The (re)solve project-a 5-year, multicountry project testing new, context-specific solutions to address unmet need for family planning-used responsive feedback to ensure products and services designed and implemented were responsive to the context and preferences of health care workers, women, and girls. The adaptive learning meeting (ALM) cycle was designed as an RFM during implementation. This frequent series of rapid, actionable, cross-team meetings builds on several existing frameworks and practices. The ALM cycle used rapid, close to real-time data and observations from a wide range of stakeholders; routine monitoring data; and a structured, facilitated process to examine and act on feedback. Each cycle was repeated every 2 weeks for 3 months in Burkina Faso. During each cycle, the team interpreted data and feedback from multiple sources; assessed pragmatic, actionable options to address the feedback; and identified and agreed upon short- and long-loop adaptations that could improve the implementation process, coordination, efficiency, and outputs of the project. The ALM cycle proved helpful in engendering practices of surfacing and checking assumptions, careful interpretation of data, options analysis, and decision-making. The emphasis was on actionable feedback for improvement of the intervention rather than rigor of results and findings. The (re)solve project's experience with designing, structuring, and implementing the ALM cycle to address challenges and gaps in implementation can be informative for similar programs seeking to implement RFMs in complex and dynamic settings, especially where technology-based RFMs are not an option.