Global health continues to grapple with a persistent tension between standardized, evidence-based interventions developed by international experts and the contextual, experiential local knowledge held by local health workers. This dichotomy – between global expertise and local knowledge – has become increasingly problematic as health systems face unprecedented complexity in addressing challenges from climate change to emerging diseases. The limitations of current approaches The dominant approach privileges global technical expertise, viewing local knowledge primarily through the lens of “implementation barriers” to be overcome. This framework assumes that if only local practitioners would correctly apply global guidance, health outcomes would improve. This assumption falls short in several critical ways: The hidden costs of privileging global expertise When we examine actual practice, we find that privileging global over local knowledge can actively harm health system performance: Evidence from practice Recent experiences from the COVID-19 pandemic provide compelling evidence for the importance of local …
How can we reliably spread evidence-based practices at the speed and scale modern health challenges demand?
At a symposium of the American Society for Tropical Medicine and Hygiene (ASTMH) Annual Meeting, I explored how peer learning could help us tackle five critical challenges that limit effectiveness in global health. For epidemiologists working on implementation science, peer learning provides a new path for solving one of global health’s most persistent challenges: how to reliably spread evidence-based practices at the speed and scale modern health challenges demand. The evidence suggests we should view peer learning not just as a training approach, but as a mechanism for viral spread of effective practices through health systems. How do we get to attribution? Of course, an epidemiologist will want to know if and how improved health outcomes can be attributed to peer learning interventions. The Geneva Learning Foundation (TGLF) addresses this fundamental challenge in implementation science – proving attribution – through a three-stage process that combines quantitative indicators with qualitative validation. …