Mapping evidence for health policy and systems decision-making: a spectrum approach bridging tacit and scientific knowledge across local and global contexts

BACKGROUND: Research on evidence-informed decision-making has commonly focused on scientific evidence. However, this does not reflect the diversity of evidence used in real-world policy settings or give decision-makers a clear way to characterize and compare different evidence sources. This article describes the development of a spectrum approach to map the types of evidence used in health policy decisions and support more systematic and transparent judgements about their nature and applicability to different decisions. METHODS: The approach was developed in four stages. First, we conducted a targeted literature search to identify key papers defining the concept of "evidence". From these, we initially categorized evidence as binary according to its nature (either tacit or scientific) and its geographic scope (either global or local). Second, we tested these categorizations using findings from a global systematic review and an empirical study on vaccine policy-making in Kenya. This showed that binary categorizations were inadequate for capturing the range of evidence informing decisions and transparently presenting how evidence sources vary in their generation and contextual applicability. Third, iterative team deliberations about these limitations led us to develop a spectrum approach that maps evidence along two intersecting axes: tacit-scientific and local-global. Finally, stakeholders provided feedback on the clarity, relevance and applicability of this approach. RESULTS: The spectrum approach positions evidence along two axes: tacit to scientific (the extent to which evidence is independent of individual experience, documented and generated through systematic, transparent and reproducible processes) and global to local (in relation to the decision setting). Positioning evidence along axes rather than in binary categories allowed us to distinguish evidence that varies along these dimensions, visualize the forms of global and local evidence available and where gaps exist, and reflect more explicitly on the applicability of different evidence sources to specific decision contexts. CONCLUSIONS: Binary categorizations inadequately reflect variation in how evidence is generated and what evidence can be useful for decision-making. By mapping evidence across intersecting tacit-scientific and global-local continua, the spectrum approach offers a clear and inclusive framework that can support researchers and decision-makers in drawing more fully on the breadth of evidence available to inform health systems decisions.