A new study argues that health policymakers and the researchers who advise them have been thinking about “evidence” in far too narrow a term—and it proposes an elegant fix. Instead of sorting the information that informs health decisions into rigid boxes such as “scientific” or “not scientific,” a team of researchers from Kenya, Norway, Ghana, South Africa, and the United Kingdom has developed a “spectrum approach” that maps evidence along two continuous axes: how tacit or scientific it is, and how local or global its scope. The framework, described in a paper published in Health Research Policy and Systems, is designed to make visible the full breadth of knowledge that shapes real-world policy decisions, much of which currently goes unrecognized in formal evidence-informed decision-making frameworks.
The problem the authors set out to solve is deceptively simple. Research on evidence-informed decision-making has traditionally fixated on scientific evidence—systematic reviews, randomized trials, and other products of rigorous, transparent, reproducible inquiry. That focus, while defensible, fails to capture what actually happens in policy rooms. When a national immunization advisory group weighs whether to introduce a new vaccine, its members draw not only on published clinical data but on implementation experience, informal consultations, personal histories with similar programs, and knowledge of local health system constraints. This kind of knowledge—often described as “tacit” evidence, because it resides in individual experience rather than in documented, systematically generated records—can be decisive. Yet most frameworks either ignore it entirely or treat it as a lesser cousin to scientific evidence.
The team, led by D. Waithaka of the KEMRI Wellcome Trust Research Programme in Nairobi and including corresponding author U. Gopinathan of the Norwegian Institute of Public Health, began with a targeted literature search to identify key papers that define what “evidence” actually means. From this starting point, they initially tried a binary categorization scheme: evidence was classified as either tacit or scientific according to its nature, and either local or global according to its geographic scope. The intuition was straightforward—if decision-makers could label each piece of evidence they were using with one of four labels, they could quickly characterize the mix of inputs behind a given decision.
The binary approach did not survive contact with real policy data. The researchers stress-tested their categories against the findings of a global systematic review and against an empirical study of vaccine policy-making in Kenya, which included examination of decisions made through bodies such as the Kenya National Immunization Technical Advisory Group (KENITAG), including deliberations around human papillomavirus (HPV) vaccine introduction. What emerged was a mismatch. Evidence sources in actual decision processes did not fall neatly on one side or other of a dividing line. A piece of documented implementation experience from a single Kenyan county, for example, is partly scientific—it is written down, shared, and open to scrutiny—but partly tacit, since it is deeply bound to particular individuals and contexts. Likewise, “local” and “global” proved to be poles of a continuum rather than mutually exclusive classes: a World Health Organization guideline is global in scope, but its local applicability can vary enormously; district-level managerial know-how is local, yet it echoes patterns seen across many settings.
The failure of binary categories prompted the study’s central methodological move. Through iterative team deliberations about the limitations they were encountering, the researchers converged on a spectrum approach that maps evidence along two intersecting, continuous axes. The first axis runs from tacit to scientific and measures the extent to which evidence is independent of any individual’s personal experience, documented, and generated through systematic, transparent, and reproducible processes. At one extreme sits purely tacit knowledge—the judgment of a veteran program manager, the lived experience of a community health worker. At the other sits formal scientific evidence such as a systematic review. Between them lies a vast middle ground: evaluative reports, routine health information data, expert committee minutes, and documented implementation lessons, each of which exhibits some degree of systematization without meeting the full bar of scientific production.
The second axis runs from global to local, positioning each evidence source in relation to the specific decision setting at hand. Global evidence—international guidelines, multi-country studies, systematic reviews conducted by groups such as Cochrane or the World Health Organization—travels across borders but may require substantial adaptation to fit particular health systems. Local evidence—single-country studies, subnational program evaluations, stakeholder consultations—carries rich contextual fidelity but limited generalizability. By plotting evidence on both axes simultaneously, the framework produces a two-dimensional map of everything informing a decision.
The advantages of this continuous, two-dimensional approach over binary sorting are, according to the authors, substantial. First, positioning evidence along axes rather than in categories allows analysts to distinguish between evidence sources that vary in degree along these dimensions, rather than forcing dissimilar items into identical boxes. Second, the approach makes it possible to visualize which forms of global and local evidence are available for a given decision—and, crucially, where the gaps lie. A policymaker might discover, for instance, that a decision is richly supported by global scientific evidence but almost entirely lacking in local implementation experience, or vice versa. That gap analysis can directly shape the commissioning of new research, the solicitation of expert input, or the design of implementation pilots. Third, the framework prompts more explicit and transparent reflection on the applicability of each evidence source to the specific decision context, a step that is often left implicit in advisory processes.
The development process also incorporated external perspectives. Stakeholders were invited to comment on the clarity, relevance, and applicability of the spectrum approach, providing feedback that informed its final form. This grounding in both empirical observation of real vaccine policy decisions in Kenya and structured feedback distinguishes the framework from purely theoretical contributions to the evidence-informed policy literature. The authors note that the empirical study informing the work received ethical approval from the KEMRI Scientific and Ethics Review Committee, with informed consent obtained from all participants in the original research, and that no new primary data were collected for the conceptual paper itself.
The implications extend well beyond immunization policy. Health systems decisions everywhere—from pandemic response planning to universal health coverage reforms—are made under conditions where the available evidence is heterogeneous, incomplete, and unevenly matched to the local context. The authors explicitly position their contribution as a response to that reality: binary categorizations, they conclude, inadequately reflect the variation in how evidence is generated and what it can usefully contribute to decision-making. By mapping evidence across intersecting tacit–scientific and global–local continua, the spectrum approach offers what they describe as 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.
The work was carried out under the SUPPORT-SYSTEMS project (Supporting inclusive and accountable health systems decisions for universal health coverage), funded by the Research Council of Norway, with additional support for Kenyan co-authors through the KEMRI-Wellcome Trust Research Programme. Published open access, the paper arrives at a moment when global health institutions are increasingly acknowledging that decisions of consequence are rarely informed by systematic reviews alone. Whether the spectrum approach becomes a practical tool for advisory bodies—helping them chart, and defend, the full landscape of knowledge behind each decision—will depend on uptake; but the framework offers something the field has lacked: a way to talk about all the evidence, not just the kind that comes with a methods section.
Subject of Research: Development and testing of a spectrum approach for mapping and characterizing the diverse types of evidence—spanning tacit to scientific and local to global—used in health policy and systems decision-making.
Subject of Research: Medicine
Article Title: Mapping evidence for health policy and systems decision-making: a spectrum approach bridging tacit and scientific knowledge across local and global contexts
Article References: Waithaka, D., Tsofa, B., Glenton, C., Nzinga, J., Koduah, A., Barasa, E., Lewin, S., & Gopinathan, U. (2026). Mapping evidence for health policy and systems decision-making: a spectrum approach bridging tacit and scientific knowledge across local and global contexts. Health Research Policy and Systems. https://doi.org/10.1186/s12961-026-01502-4
Image Credits: AI Generated
DOI: 10.1186/s12961-026-01502-4
Keywords: evidence-informed policy, health policy and systems, spectrum approach, tacit knowledge, scientific evidence, local evidence, global evidence, decision-making approach, evidence mapping, vaccine policy, Kenya, policy analysis
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Timothy Lambert. (September 6, 2026). A spectrum approach links tacit and scientific knowledge for health policy mapping. Scienmag. https://scienmag.com/a-spectrum-approach-links-tacit-and-scientific-knowledge-for-health-policy-mapping/
Timothy Lambert. “A spectrum approach links tacit and scientific knowledge for health policy mapping.” Scienmag, 6 September 2026, https://scienmag.com/a-spectrum-approach-links-tacit-and-scientific-knowledge-for-health-policy-mapping/. Accessed 6 September 2026.
Timothy Lambert. “A spectrum approach links tacit and scientific knowledge for health policy mapping.” Scienmag. September 6, 2026. https://scienmag.com/a-spectrum-approach-links-tacit-and-scientific-knowledge-for-health-policy-mapping/
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Tags: and contextual knowledge. The spectrum approach aims to integrate these diverse sources by positioning evidence along axes of tacitness vs. explicitness and local vs. global relevanceand local contextual knowledgehealth interventionspromoting a more comprehensive understanding of evidence in health decision-making.recognizing their complementary roles in shaping effective health policies. This framework emphasizes the importance of both tacit and scientific knowledge across local and global contextsthereby providing a more holistic view of what informs health policy decisions.which are often tacit or less formalized. The spectrum approach aims to integrate these diverse types of evidence
