Producing ultra-rapid contextualized evidence syntheses to inform pressing climate-change challenges

Addressing the climate change crisis requires global cooperation to implement solutions that are informed by the best-available evidence. As emphasized by the Global Commission on Evidence, this requires investing in the global evidence architecture for a suite of living evidence syntheses (LESs) on priority questions (including climate change), and leveraging technology to enable LESs to be harnessed to address climate change priorities. This is particularly important in situations where decision-makers must determine how best to urgently respond to climate-related emergencies such as wildfires that recently affected many countries in 2023. Addressing urgent priorities such as this requires domestic evidence-support units to be able produce ultra-rapid evidence syntheses that draw on what is known from routinely updated LESs and contextualized with insights from local evidence, specific interventions, priority populations and/or experiences from other countries. Our team has developed an approach for providing ultra-rapid contextualized evidence syntheses that: 1) engage subject-matter experts to scope the question and consider relevant context; 2) identify highly relevant evidence from existing evidence syntheses and key single studies; 2) assess evidence for relevance (using a mutually exclusive and collectively exhaustive framework of decision-relevant considerations about the challenge faced) and methodological quality; 3) document experiences from other countries that can be used to contextualize the findings; and 4) provide a profile of evidence and experiences that focuses on identifying coverage by and gaps in existing evidence syntheses, key insights from the most relevant evidence documents and priorities for next steps. We will present our model for evidence support, and insights about how we were able to respond to a series of urgent climate change priorities in Canada in a way that wouldn’t have been possible without harnessing the global evidence architecture in the form of a machine-learning enabled evidence map of global research on climate and health.