Conceptualisations and pathways of evidence for policymaking: mapping evidence actors producing and mobilising evidence for policy in Latin America and the Caribbean

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In Latin America and the Caribbean (LAC), existing analyses often describe evidence-to-policy efforts as fragmented and sector-specific, with limited empirical understanding of who the key actors are, how they engage with policymakers, and how different forms of evidence circulate across policy sectors. Moreover, prevailing actor-mapping approaches rarely account for variation in evidence cultures and modes of engagement, constraining comparative analysis of evidence ecosystems. Methods We conducted a descriptive mapping study of actors producing or mobilising evidence for policy across LAC using a transparent, web-based methodology, hereafter called evidence actors. Publicly available information from institutional websites, policy networks, academia and grey literature was systematically extracted and cross-validated with the Latin America and Caribbean Evidence Hub. Actors were included if they were based in LAC, demonstrated explicit policy intent, and produced evidence-related outputs, nurtured evidence partnerships, or conducted engagement activities. We analysed actors’ mandates, evidence products, partnerships, and engagement practices, and developed an engagement typology based on observable interaction. Through iterative analysis, we derived a conceptual framework linking actor roles with evidence orientations and interaction pathways. Results The mapping identified 137 actors (95 institutions and 36 individual researchers) across multiple countries and policy sectors. Evidence production and mobilisation varied systematically by sector, revealing two dominant orientations: science-driven approaches prioritising methodological rigour and technical credibility, and problem-driven approaches emphasising contextual relevance and experiential knowledge. We identified three pathways through which evidence interacts with policymaking: top-down, bottom-up, and overlapping pathways. Reach of actor’s engagement ranged from strong, sustained interaction with policymakers to more limited, professional or society-oriented mobilisation. Intermediary actors played a central role in overlapping pathways, brokering evidence between technical and civic arenas. Conclusions By integrating evidence orientations, interaction pathways, and actor engagement, this study offers a transferable framework for analysing evidence ecosystems beyond linear or sector-specific models. The findings highlight the plural and context-dependent nature of evidence use and underscore the importance of intermediaries in strengthening durable connections between evidence, policy, and society across diverse settings. Evidence-informed policymaking Evidence use Evidence ecosystems Actor mapping Knowledge brokers Latin America and the Caribbean Knowledge translation Evidence for policy policy engagement Figures Figure 1 Figure 2 Background Evidence-informed policymaking (EIPM) has gained growing attention among governments, international organisations, and research funders as an approach intended to support the quality, legitimacy, and effectiveness of public decision-making. Yet decades of research show that the use of evidence in policymaking depends not only on the availability or quality of research, but on the actors who produce, translate, broker, and apply it, and on the relationships that connect them within specific institutional and political contexts ( 1 – 4 ). These actors collectively form what is increasingly described as an evidence ecosystem: a constellation of organisations, individuals, practices, and norms that shape how evidence circulates, gains authority, and influences policy decisions ( 5 ). In Latin America and the Caribbean (LAC), available analyses of the evidence ecosystem commonly describe evidence-to-policy efforts as fragmented and uneven across countries and sectors, with the most visible and institutionalised initiatives concentrated in the health sector ( 6 ). Evidence-to-policy work beyond health is less consistently documented and often described using heterogeneous terminology, which limits the ability to identify who produces, mediates, and uses evidence across policy sectors and to compare evidence ecosystems within the region. While studies such as Jessani et al. (2016) and Oliver and Boaz (2019) provide important insights into actor roles and knowledge-translation arrangements, they draw primarily on cases outside LAC and are therefore best understood as examples from the broader global literature on actor mapping, rather than as empirical evidence about actor configurations in the LAC context ( 7 , 8 ). These limitations are not only empirical but also methodological. Actor mapping in EIPM frequently relies on expert nomination, participation in established networks, or affiliation with high-profile initiatives, which shapes which actors become visible in analyses ( 9 , 10 ). While such approaches are often pragmatic, they tend to privilege well-resourced organisations, internationally connected institutions, and policy sectors with consolidated evidence infrastructures, while obscuring less formal, locally embedded, or cross-sector actors. As a result, mapping exercises may reproduce existing power asymmetries within evidence ecosystems and offer only a partial account of how evidence is produced, brokered, and used in practice ( 11 , 12 ). Beyond questions of visibility and methodology, a further challenge in analysing the evidence ecosystem lies in the diverse ways in which “evidence” itself is conceptualised and valued. The literature shows that evidence is not a singular or neutral category, but encompasses multiple forms of knowledge, including formal research, administrative data, professional expertise, and experiential or contextual knowledge ( 1 , 13 ). Different actors and policy sectors prioritise these forms of evidence in distinct ways, shaped by institutional mandates, professional norms, and decision-making contexts ( 14 , 15 ). As a result, what counts as relevant or credible evidence varies across settings, influencing how evidence is mobilised, whose knowledge is legitimised, and which actors gain influence within evidence ecosystems. Yet, existing actor-mapping approaches rarely make these epistemic differences explicit, limiting their ability to capture how evidence circulates and is used across heterogeneous policy sectors. These methodological and conceptual challenges highlight the limitations of existing approaches to mapping evidence actors. Actor mappings that do not account for variation in how evidence is defined, valued, and mobilised risk conflating fundamentally different forms of engagement and hiding important distinctions between actors’ roles within evidence ecosystems. This is particularly problematic in comparative and cross-sectoral analyses, where differences in evidence cultures, professional norms, and decision-making contexts shape how actors interact with evidence and policymakers. Addressing these challenges requires analytical tools that can move beyond simple identification of actors to capture how they engage with evidence and policymaking across heterogeneous settings. This study addresses these gaps by applying a systematic actor-mapping approach to identify organisations and individuals engaged in the elaboration and mobilisation of evidence for policy across LAC, and to examine how they engage with policymakers across different modes of evidence use. The analysis is guided by the following research question: Who are the main actors involved in the evidence ecosystem in LAC and how do they engage with policymakers across different modes of evidence use? To address this question, the study makes three contributions. First, it proposes a transparent and replicable web-based methodology for mapping evidence actors across countries and policy sectors. Second, it advances an engagement typology that distinguishes actors’ mode and reach of involvement while remaining sensitive to differences in how evidence is conceptualised and mobilised. And third, it introduces a conceptual framework that links different types of actors, their ways of engaging with policymakers, and the diverse forms of evidence used within evidence ecosystems. Methods Study design We conducted a descriptive mapping study of actors engaged with evidence for policy across LAC. Using publicly available online sources, we extracted information on actors’ mandates, outputs, partnerships, and evidence for policy activities and classified observable modes of engagement. The primary units of analysis were institutions and researchers. Inclusion criteria Actors were eligible if they met both core criteria: Location: Based in a country in LAC. Policy intent: Publicly stated that their work or research aims to inform policymaking or policy decisions through the use of evidence. Actors meeting both core criteria were included if they also met at least one additional criterion: Evidence products: Production or use of policy-relevant evidence products (e.g., evidence syntheses, evaluations, modelling, data analysis, qualitative insights, technology or cost-effectiveness assessments, guidelines, or other evidence outputs intended to inform policy). Networks/partnerships: Membership in, or partnership (formal or informal) with at least one of the following: knowledge mobilisation/knowledge translation platforms or networks; government departments or agencies; civil society organisations; advocacy networks; or organisations representing key populations. Engagement activities: Activities aimed at informing policymakers or civil society (e.g., deliberative dialogues, briefings/consultations, workshops/webinars, task forces or advisory groups, community mobilisation activities oriented to policy influence). Search strategy We identified actors using a structured, multi-source web-based search conducted in two stages, followed by snowball sampling. First, we screened membership lists and partner organisations of established knowledge mobilisation and evidence-to-policy networks relevant to LAC (e.g., EVIPNet, Campbell Collaboration, Cochrane, Health Systems Global, On Think Tanks, and regional evidence hubs). Second, we reviewed institutional websites of networks, public agencies, universities, research centres, think tanks, and civil society organisations to assess eligibility and identify additional actors. To complement this, we searched academic and grey literature on evidence use and knowledge translation, identifying actors through author affiliations and institutional references. Snowball sampling was then applied by tracing partnerships, collaborations, and acknowledgements reported on institutional websites and publications. The preliminary list of identified actors was cross-validated with the Latin America and Caribbean Evidence Hub (Hub LAC) (16), which conducted a parallel mapping exercise using complementary criteria and supported final verification of actor inclusion. The database was finalised on 31 March 2023. Figure 1 summarises the search strategy and actor identification process. Figure 1: Search strategy and actor identification process (separate file) Data analysis We analysed the extracted data in two steps. First, we conducted a structured descriptive analysis to classify evidence actors by type (institution or researcher) and to summarise their stated mandates, policy sectors, evidence products, partnerships, and engagement activities. Based on these observable features, we coded each actor’s mode of engagement based on how they produced or mobilised evidence for policymaking, applying a coding framework developed iteratively during extraction. Second, we developed an engagement typology to characterise variation in actor–policy interaction. Evidence actors’ reach was recognised by the consistency and intensity of their documented interactions with evidence in policymaking processes, drawing on publicly reported activities such as the production of policy-facing outputs (e.g., policy notes, reviews, etc.), participation in advisory spaces, convening or supporting deliberative dialogues, formal partnerships with government, and sustained capacity-building or implementation support. Using the typology and coded engagement features, we then amalgamated recurrent patterns into a conceptual framework describing actor interaction with policymakers across different evidence pathways. This framework was derived through iterative comparison across actor types and policy sectors, focusing on how different forms of evidence and engagement strategies aligned with either more research-led or more problem-driven modes of evidence mobilisation. Results The mapping exercise identified 357 actors engaged in activities related to evidence for policy across LAC. After applying the inclusion criteria, the final dataset comprised 137 evidence actors. Of these, 95 were institutions and 36 were individual researchers, where researchers are not necessarily are hosted in the institutions identified. These 137 evidence actors formed the sample for analysis. An additional six decision makers were identified as points of interaction with evidence but were not included as units of analysis. The identified evidence actors were distributed across multiple countries in the region and spanned a wide range of policy sectors. They varied substantially in their organisational form, mandates, and modes of engagement with evidence and policymaking, reflecting the heterogeneity of evidence ecosystems across LAC. The sections that follow examine how evidence is conceptualised and mobilised across sectors, how this shapes interaction pathways with policymakers, and how evidence actors engage within these configurations. Sectoral conceptions of evidence Across the mapped evidence actors, understandings of what constitutes relevant evidence varied systematically by policy sector. Evidence was not treated as a uniform category, but encompassed diverse forms, including formal scientific research, administrative and programme data, qualitative and traditional knowledge, and communication-oriented products intended for public engagement or advocacy. These differences became visible not only in actors’ self-descriptions, but also during actor identification: searches of academic and grey literature using common evidence terms (e.g. “evidence-informed decision-making”, “knowledge mobilisation”, “research use”) and subsequent snowball sampling surfaced additional researchers and institutions in policy sectors beyond health. Overall, sectoral variation was reflected both in the types of evidence produced and in the terminology evidence actors used to describe their work. In sectors with more established technical and administrative infrastructures—such as economics, energy, science and technology, and health (e.g. health technology assessment)—evidence was more often framed in terms of scientific rigour, methodological standards, and formal outputs such as evaluations, reviews, and quantitative analyses. By contrast, in sectors shaped by stronger civic mobilisation or weaker data systems— such as gender, environment and climate change, and development— evidence actors more often focused on the context, thus looking into more qualitative or experiential knowledge, frequently mobilised through less conventional formats (e.g. visuals, posters, radio programmes). More heterogeneous sectors, including education, political sciences, and security, showed mixed patterns, combining formal analysis with administrative and context-specific evidence. These sectoral differences shaped not only the forms of evidence prioritised, but also the audiences to whom evidence was directed and the ways in which it was mobilised in relation to policymaking. This variation provided the empirical basis for distinguishing between two broad approaches to evidence use identified across the mapping. Science-driven and problem-driven approaches to evidence use Analysis of sectoral evidence practices revealed two dominant, though not mutually exclusive, approaches to evidence use: science-driven and problem-driven. These approaches differed in their epistemic orientation, preferred methods, target audiences, and modes of engagement with policy processes. This science-driven orientation is reflected in evidence actors whose legitimacy rests primarily on methodological rigour, technical expertise, and the production of formal research outputs. In the health sector, this is exemplified by health technology assessment (HTA) bodies such as the Institute for Clinical Effectiveness and Health Policy (IECS) in Argentina, which produces systematic reviews, economic evaluations, and modelling studies to inform coverage decisions and optimise the implementation of health interventions. Evidence is mobilised through highly standardised technical products, supported by relatively strong health information systems, and primarily circulated within expert and bureaucratic policy communities. Similar science-driven logics were observed in other policy sectors. In economic and social development, the Economic Commission for Latin America and the Caribbean (ECLAC) produces quantitative analyses, macroeconomic models, and statistical projections that prioritise analytical consistency and technical credibility. Evidence is disseminated through flagship reports and datasets and used to inform policy design and optimisation at the executive level, reinforcing an expert-led model of evidence use with limited reliance on participatory or deliberative processes. The problem-driven orientation was evident among organisations working closely with communities, practitioners, and advocacy networks, where evidence use was shaped by specific social and policy challenges rather than by formal technical appraisal alone. For example, the Centro de Investigación y Promoción del Campesinado (Center for Research and Promotion of the Peasantry, CIPCA in spanish ) mobilises locally generated qualitative evidence, participatory research, and experiential knowledge to document rural livelihoods, land governance, and food security challenges in Bolivia. In contexts characterised by uneven or fragmented data systems, evidence is translated into policy briefs, community reports, and training materials aimed at problem framing, agenda-setting, and strengthening the capacity of peasantry organisations to engage in policy and rights-based advocacy. Similar problem-driven logics were observed among organisations addressing issues such as gender, security, and social inclusion, including Fundación Construir and Itaú Social, where evidence use frequently combined qualitative analysis, contextual knowledge, and capacity-building activities oriented towards advocacy, public debate, and policy responsiveness. While analytically distinct, these two approaches frequently coexisted within and across sectors. Table 1 summarises the defining characteristics of science-driven and problem-driven approaches to evidence use. Table 1: Science and Problem driven approach (separate file) Top-down, bottom-up, and overlapping pathways of evidence interaction Building on the sectoral approaches described above, we identified three broad pathways through which evidence is mobilised in relation to policymaking: top-down, bottom-up, and overlapping. These pathways are defined primarily by who evidence is produced for and how it is intended to influence policy outcomes. In top-down pathways, evidence is produced mainly for policymakers and technical decision-makers, with the aim of informing policy design, implementation, or optimisation from within formal institutional settings. These pathways tend to rely on quantitative methods and institutionalised data systems, and evidence is mobilised through policy-facing reports, commissioned analyses, and advisory processes. Key actors include research institutes, universities, and government-affiliated bodies. In contrast, bottom-up pathways direct evidence primarily towards society, including civil society organisations, communities, and specific population groups. Here, evidence is used to support advocacy, mobilisation, and public debate as a way of influencing policy agendas indirectly. These pathways commonly draw on qualitative, participatory, and context-specific knowledge and operate in settings where data systems are weaker or more fragmented. Civil society and grassroots organisations play a central role, particularly in sectors such as environment, gender, climate change, and human rights. Overlapping pathways combine both directions of evidence use. In these configurations, evidence circulates between policymakers and societal actors, often using mixed or selectively combined approaches. Their defining feature is a strong emphasis on brokering and mediation, with intermediary organisations translating and aligning different forms of evidence to respond simultaneously to policy demands and societal needs. We conceptualise these pathways as problem-driven science, where scientific robustness and contextual relevance are deliberately brought together. Table 2 summarises the main dimensions distinguishing these three pathways, including audiences, methods, data sources, focus, and key actors. Table 2: Dimensions of top-down, bottom-up, and overlapping approaches to evidence for policy (separate file) Empirical examples from the mapping illustrate how these pathways operate in practice. A top-down pathway is exemplified by the Fórum Brasileiro de Segurança Pública (Public Security Brazilian Forum, in Portuguese), which produces quantitative indicators and analytical reports on crime and violence that are explicitly addressed to government and legislative audiences. A bottom-up pathway is illustrated by Natura, which mobilises evidence through deliberative dialogues to engage civil society actors around environmental issues and support their interaction with policymakers. An overlapping pathway is reflected in the “Observatorio de políticas públicas en actividad física y alimentación” (Public Policy Observatory in physical activity and diet, in spanish), which combines research outputs with deliberative dialogues, enabling evidence to circulate between experts, decision-makers, and wider stakeholder groups. Reach of policy engagement by evidence actors We classified the reach of institutions and researchers based on whether they met three observable criteria: Partnerships (Criteria C): members or partners –formal or informal– of one or more of the following: Knowledge Mobilisation (KM)/Knowledge Translation Platforms (KTP) networks, Government departments or agencies, civil society groups, advocacy networks, key population (i.e. indigenous people, LGTB, etc). Products (Criteria D): systematic reviews, literature reviews, rapid reviews, scoping reviews, policy notes, and other type of documents to communicate evidence. Activities (Criteria E): Deliberative dialogues, Workshops/webinars to inform policymakers, conferences, presentations, and other activities that are focused on informing policymakers using evidence. Engagement reach reflected the combination of criteria achieved by each actor, distinguishing strong engagement (sustained relationships underpinning exchange or integrated efforts), professional evidence networks (circulating evidence while overlooking policy makers and communities), and indirect policy engagement (through evidence for advocacy and activism), as summarised in Table 3. All classifications were based on publicly available documentation of each evidence actor. Table 3: Evidence actors’ reach with evidence for policy (separate file) Strong engagement reflected exchange or integrated efforts, where evidence actors combined sustained relationships with decision makers with policy-facing products and active engagement activities. This pattern often appeared among international organisations and well-connected NGOs or academic institutions with established communication channels to government, participation in national or regional networks (e.g. EVIPNet or the Brazilian Coalition for Evidence), and, in some cases, institutionalised citizen consultations that supported co-creation alongside direct policy engagement. A pattern of networks circulating evidence included organisations with specific target populations and clear aims that could be participating in knowledge-translation networks but did not consistently produce policy-facing evidence products. It also included some development-oriented think tanks that generated substantial evidence but circulated it mainly through professional networks or academic outlets rather than through sustained, direct engagement with policymakers or communities. In addition, some institutions with partnerships with governments— such as government-affiliated or tertiary research organisations, produce isolated evidence products without getting involved in the process of the policymaking. For example, Aru Foundation explicitly frames its work around evidence-based policymaking and policy-relevant research, aiming to influence policy debates. Publicly available information highlights technical publications and locally organised academic seminars, while activities related to knowledge translation or direct policy engagement are not explicitly described. Indirect policy engagement reflected a pattern of evidence for advocacy efforts and commonly characterised smaller, locally embedded organisations—particularly in areas such as climate change or human rights—whose primary aim was to empower communities with evidence and scientific knowledge. For these evidence actors, the objective was not necessarily to communicate evidence directly to policymakers, but to strengthen civil society awareness, capacity, and mobilisation through public-facing communication and citizen-focused capacity-building, with limited emphasis on direct policymaker engagement. Engagement reach varied across actor types and across the evidence-use approaches described above. As shown in the Appendix, Brazil leads in the identification of actors with strong engagement, including both institutions and researchers. It is followed by Colombia and Chile, which display a higher number of actors with intermediate engagement. Argentina also plays an important role, although no actors were identified there with strong engagement. Overall, the actors working towards the institutionalisation of EIPM—whether formally or informally and with their different reach of engagement—operate in varied ways depending on country and sector. Within each country and policy domain, diverse evidence ecosystems and sub-ecosystems coexist. The levels of institutionalisation of EIPM, as well as the dynamics of these ecosystems, extend beyond the scope of this mapping. To understand how actors navigate their evidence ecosystems and how their actions influence the institutionalisation of EIPM, it is essential to examine the context-specific features of each ecosystem. Discussion This study examined how evidence is conceptualised, mobilised, and connected to policymaking across LAC through a systematic mapping of evidence actors and their engagement practices. By combining a sector-sensitive analysis of the evidence ecosystem with an analytical typology of interaction pathways and actor engagement, the study makes both a methodological and conceptual contribution to the literature on evidence for policy. Rather than treating evidence use as uniform or linear, the findings show that it is plural, context-dependent, and structured by intersecting orientations and pathways that shape how evidence enters political decision-making. Evidence use as plural and contextually embedded The findings confirm that what counts as “evidence” varies substantially across policy sectors and institutional settings ( 1 , 13 ). Across the mapped ecosystems, evidence encompassed scientific research, administrative and programme data, indigenous and experiential knowledge, and communication-oriented products designed for public engagement or advocacy. Their relative prominence differed systematically by sector, reflecting variations in data infrastructures, professional norms, and civic dynamics. These patterns reinforce critiques of narrow, technocratic conceptions of EIPM ( 2 , 3 , 13 , 17 ) and highlight evidence as socially embedded rather than methodologically neutral. In LAC contexts, characterised by uneven state capacity, political contestation, and strong civil society mobilisation, problem-driven forms of evidence use emerge not as deficits but as adaptive responses to contextual constraints and legitimacy demands. Pathways of interaction and the centrality of intermediaries By linking evidence orientations to top-down, bottom-up, and overlapping pathways of interaction, the study adds analytical clarity to how evidence reaches policymaking arenas. Top-down pathways reflect evidence directed primarily to policymakers through formal advisory and technical channels, while bottom-up pathways involve evidence directed first to society to influence policymaking indirectly through advocacy, mobilisation, and public debate. Focusing on pathways adds to our understanding of how evidence informs policy. The strong engagement observed in this study is direct mutual engagement between evidence producers and decision makers. It accommodates both the relationship model and systems model of knowledge translation distinguished by Best and Holmes (2010) ( 18 ). Circulating evidence primarily amongst professionals and academics, while overlooking both policy makers and communities, aligns with Best and Holmes’ linear ‘push’ model. A third pathway takes an indirect route to policy engagement by leveraging wider social system to strengthen advocacy efforts with evidence but not necessarily including all the key stakeholders as in Best and Holmes’ system model. Crucially, the identification of overlapping pathways highlights the central role of intermediary and boundary-spanning evidence actors. These evidence actors do not merely transmit evidence; they actively broker, translate, and align different forms of knowledge, audiences, and policy logics. In fragmented governance environments such as those observed across LAC, these brokerage functions are essential for sustaining evidence use across sectors and political cycles. An integrative framework of evidence-use pathways Bringing these insights together, we propose an integrative framework that links evidence orientations, interaction pathways, and actor roles (Fig. 2). The framework organises evidence use along two intersecting axes that capture different features of how evidence moves in policymaking. The vertical axis (top-down – bottom-up) captures who evidence is directed to first. Top-down pathways occur when evidence is produced or translated to be used directly by policymakers (e.g., ministries, agencies, advisory bodies) within formal decision processes such as agenda-setting, appraisal, implementation, or evaluation. Bottom-up pathways occur when evidence is produced or translated to be used directly by the public—citizens, communities, social movements, or key stakeholders—so they can shape policy indirectly through advocacy, mobilisation, accountability, and public debate. The horizontal axis (science-driven – problem-driven) captures how evidence is defined and legitimised. Science-driven approaches privilege methodological formalisation and technical credibility, typically relying on structured research outputs (e.g., evaluations, modelling, evidence syntheses) and stronger data infrastructures. Problem-driven approaches privilege contextual fit and responsiveness to lived problems, often drawing on qualitative, experiential, and local/indigenous knowledge and using communicative formats that travel well in civic spaces (e.g., visuals, media products, activist toolkits). These axes are analytically independent, even if they often align in practice. For example, policymakers frequently commission science-driven products for top-down use (e.g., technical evaluation to optimise implementation), while civil society actors often mobilise problem-driven evidence for bottom-up use (e.g., indigenous knowledge and community testimony to support rights-based claims). However, mismatches are also common: civil society may deploy science-driven evidence (e.g., quantified environmental risks) and governments may draw on problem-driven inputs (e.g., consultations, deliberative dialogues) when legitimacy and feasibility matter. Figure 2: The policy-driven science framework for evidence use in policymaking in LAC (separate file) Normatively, more legitimate and effective evidence use often emerges when these pathways converge. When evidence reaches policymakers through institutional channels and reaches communities through accessible, context-sensitive communication, the policy process can combine technical robustness with social legitimacy. In this convergent space, knowledge brokers and intermediary organisations can mediate between decision makers and civil society: aligning questions, translating findings into actionable formats, and carrying societal priorities back into formal policy arenas. Conceptually, this convergence also supports a “problem-driven science” orientation: science-driven standards strengthen credibility, while problem-driven priorities ensure relevance to lived realities and targeted populations, producing evidence that is both rigorous and publicly meaningful. Actor engagement and ecosystem maturity The engagement typology highlights cross-country variation in the durability and intensity of interactions between evidence actors and policy makers. In some countries, evidence actors more often engaged through formal advisory spaces, intermediary organisations, and routinised policy-facing outputs, which supported sustained interaction and greater use of overlapping pathways. In others, engagement was more episodic and project-based, with evidence actors relying primarily on bottom-up routes to introduce evidence into policy debates, such as public communication, advocacy, and ad hoc consultations. Brazil showed the highest concentration of highly engaged evidence actors and the clearest visibility of overlapping pathways, with intermediary organisations frequently linking technical communities, policymakers, and civil society. This configuration appeared relatively resilient to political turnover, suggesting a more institutionalised evidence ecosystem, than observed elsewhere in the mapped sample. Across other countries, like Chile, Colombia or Argentina, engagement patterns were more uneven, often concentrated in specific sectors (particularly health) or dependent on individual champions and externally supported initiatives rather than on routinised arrangements Strengths and limitations of the study This study is descriptive and analytical rather than evaluative: it develops a methodological approach, engagement typology, and integrative framework from recurring patterns observed across sectors and countries in a transparent, replicable web-based mapping (finalised 31 March 2023). A key strength is the inclusive operationalisation of “evidence”, which deliberately encompassed scientific research, administrative and programme data, qualitative and experiential knowledge, and communication-oriented outputs used in advocacy and public engagement. This breadth provided an inter-sectorial approach reducing the risk of technocratic definitions alone. Several limitations remain. Starting with publicly available online information likely favoured actors with stronger digital presence and communication capacity, under-representing informal, locally embedded organisations and undocumented interactions. Because terminology identification relied on a wide set of search terms and observable signals of evidence-for-policy intent (rather than a single shared label such as “EIPM” or “knowledge translation”), terminological diversity affected how easily actors could be located and coded in some sectors, even though the definition of evidence was intentionally broad. The dataset captures a cross-sectional snapshot and cannot assess change over time or responsiveness to political turnover. Finally, despite a detailed search strategy and cross-validation with the LAC Hub, some actors and relationships may have been missed where documentation was incomplete or inconsistent. Importantly, these constraints are unlikely to change the paper’s core contributions: the typology and framework are grounded in consistent patterns across a diverse set of actors and sectors, and they remain informative even under partial visibility. Conclusions This study makes four contributions to understanding evidence use in policymaking, demonstrated through a regional evidence actor mapping in Latin America and the Caribbean: - First, it identifies two sector-sensitive orientations to evidence use: science-driven and problem-driven. Both differ in how evidence actors define and legitimise evidence, which methods they prioritise, which audiences they address, and which evidence products they mobilise. - Second, it distinguishes three pathways through which evidence connects to policymaking: top-down, bottom-up, and overlapping; and systematise the dimensions that differentiates across sectors. - Third, it proposes an integrative framework linking evidence orientations, interaction pathways, and the role of intermediaries in connecting decision makers and civil society. - Fourth, it introduces a typology of actor engagement (high, intermediate, limited) that supports comparison of how consistently evidence actors combine partnerships, evidence products and activities. Together, these tools offer a transferable way to analyse evidence ecosystems beyond a single sector and context, beyond linear or single network models. Abbreviations EIPM – Evidence-informed policymaking KT – Knowledge translation LAC – Latin America and the Caribbean Declarations Acknowledgements This article is derived from a chapter of a PhD Social Sciences dissertation named “Institutionalising Evidence-Informed Policymaking: Social and Political Factors in Latin America”, in the University College London. The authors wish to express their gratitude to everyone who contributed to the study, especially the Latin American and Caribbean Hub for Evidence. Funding This study was conducted as part of the Partnership for Evidence and Equity in Social Systems (PEERSS), funded by the International Development Research Centre and the Hewlett Foundation. Author information Both authors are affiliated with the EPPI-Centre, UCL Social Research Institute, University College London, London, UK. Contributions VOC conceived and designed the study, led data collection, conducted the analysis, and drafted the manuscript. SO provided conceptual guidance, methodological input, and critical feedback throughout the research process. All authors contributed to revising the manuscript and approved the final version. Corresponding author Correspondence to [email protected] Ethics declarations Ethics approval and consent to participate: No ethical approval was sought as this study does not involve human subject data. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Data availability Data are provided within the manuscript or supplementary information files. References Weiss CH. The Many Meanings of Research Utilization. Public Administration Review [Internet]. 1979 [cited 2024 Dec 29];39(5):426–31. Available from: https://www.jstor.org/stable/3109916 Nutley SM, Walter I, Davies HTO. Using evidence: How research can inform public services [Internet]. 1st edn. Bristol University Press; 2007 [cited 2024 Mar 4]. 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Available from: https://www.sciencedirect.com/science/article/pii/S0149206303000874 Langer L, Tripney JS, Gough D. The science of using science: researching the use of research evidence in decision-making [Internet]. (EPPI-Centre reports 3504 ). EPPI-Centre, Social Science Research Unit, UCL Institute of Education: London, UK. London, UK: EPPI-Centre, Social Science Research Unit, UCL Institute of Education; 2016 Apr [cited 2025 Dec 14]. Report No.: 3504. Available from: https://eppi.ioe.ac.uk/cms/Default.aspx?tabid=3504 Head BW. Toward More “Evidence‐Informed” Policy Making? Public Administration Review [Internet]. 2016 May [cited 2024 May 9];76(3):472–84. Available from: https://onlinelibrary.wiley.com/doi/10.1111/puar.12475 De Marchi G, Lucertini G, Tsoukiàs A. From evidence-based policy making to policy analytics. Ann Oper Res [Internet]. 2016 Jan 1 [cited 2024 Aug 8];236(1):15–38. Available from: https://doi.org/10.1007/s10479-014-1578-6 Yu X, Wu S, Sun Y, Wang P, Wang L, Su R, et al. Exploring the diverse definitions of ‘evidence’: a scoping review. BMJ Evidence-Based Medicine [Internet]. 2024 Feb 1 [cited 2025 Jan 5];29(1):37–43. Available from: https://ebm.bmj.com/content/29/1/37 HubLAC. HubLAC. 2024 [cited 2024 Apr 18]. Hub de Evidencias de Latinoamérica y del Caribe. Available from: http://hublac.org/en/ Cairney P. Health and Advocacy: What Are the Barriers to the Use of Evidence in Policy? In: Cairney P, editor. The Politics of Evidence-Based Policy Making [Internet]. London: Palgrave Macmillan UK; 2016 [cited 2025 Dec 11]. p. 51–84. Available from: https://doi.org/10.1057/978-1-137-51781-4_3 Best A, Holmes B. Systems thinking, knowledge and action: towards better models and methods. 2010 May 1 [cited 2026 Jan 25]; Available from: https://bristoluniversitypressdigital.com/view/journals/evp/6/2/article-p145.xml Tables Table 1: Science and Problem driven approach Dimension Science-driven approach Problem-driven approach Primary purpose Inform policy design, implementation, or optimisation through scientifically robust evidence Frame, prioritise, and respond to policy problems through contextually relevant evidence Epistemological orientation Positivist or post-positivist; emphasis on objectivity, validity, and generalisability Constructivist or interpretative; emphasis on relevance, meaning, and situated knowledge Preferred methods Quantitative and experimental methods; modelling; systematic reviews; statistical analysis Qualitative, participatory, and mixed methods; experiential and locally generated knowledge Types of evidence prioritised Peer-reviewed research, evaluations, administrative and programme data, evidence syntheses Qualitative insights, case studies, community knowledge, contextual and experiential evidence Target audiences Policymakers, technical staff, scientific and expert communities Civil society organisations, communities, advocacy groups, and broader publics (often indirectly influencing policymakers) Data environment Relatively strong, institutionalised, and standardised data systems Weaker, fragmented, or uneven data systems; reliance on alternative or proxy evidence Modes of policy influence Formal advisory processes, technical inputs, evidence appraisal within institutional decision cycles Agenda-setting, problem framing, mobilisation, advocacy, and public debate Typical products and activities Evaluations, modelling, evidence syntheses, technical reports, policy briefs, advisory committee participation Visual and media products (e.g. infographics, radio), local surveys, community engagement, consultations, advocacy campaigns Key evidence actors Universities, research institutes, government agencies, technical units Civil society organisations, grassroots groups, advocacy networks, activist organisations Note: The two approaches are analytically distinct but often coexist within and across policy sectors. Table 2: Dimensions of top-down, bottom-up, and overlapping approaches to evidence for policy Dimension Top-down Science-driven Overlap Problem driven science Bottom-up Problem-driven Epistemological orientation Positivist Hybrid Constructivist and interpretative Methods Quantitative Mixed (selected) methods- Qualitative Audiences Academic and scientific community Policymakers, intermediaries, multi-stakeholder Civil Society or key populations Data Strong, institutionalised data systems Selective or combined sources Weak or fragmented data systems Focus Implementation, technical focus Brokering, mediation Advocacy, mobilisation, political influence Key actors Universities, research institutes, government agencies Intermediary organisations NGOs, grassroots organisations, activist groups Table 3 : Levels of evidence actors’ engagement in Evidence for policy Level Partnerships (a) Products (b) Activities (c) Strong engagement (Underpinning exchange or integrated efforts) ✔ ✔ ✔ Professional evidence networks (Circulating evidence while overlooking policy makers and communities) ✔ ✔ X ✔ X ✔ Indirect policy engagement (Evidence for advocacy or activism) X ✔ X X X ✔ ✔ X X Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8695811","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601341576,"identity":"e6d8df7c-47d9-4f75-83c3-79604115f823","order_by":0,"name":"Veronica Osorio-Calderon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie3RvWqDUBTA8SuB43Ko6xFLfIUjFwoFk7xAH8Ig2CWD0CXjBaEuCVnd+gqFQmYhYBa7dxXBRygZMjTapARqQ8YO97/cD/jB/RBCp/u38dkEzCQXIu6Wg+vIDRbBaecCOW9ID3yR8HZZ1XE8doWZVvU89gUQftKOhWsplNxHyq2UGYeewlJ6JUcCnOXaXrDwshxl0EPsLAIHOTcUzcBWvBFw+752DgczXgXKvI+8NB2ZKHpsvgnNmpZM/iIWQUemioK7E4GWTFvSdzALo4FEDsPn9i6KIwQs5P2CKcw28NR3fTALo8b9eLQ6vFil9v7QTZPqYzf3R6s0eaO+V/6xxxGPI139kTqdTqf73RdNNE0u0ZRRbwAAAABJRU5ErkJggg==","orcid":"","institution":"University College London","correspondingAuthor":true,"prefix":"","firstName":"Veronica","middleName":"","lastName":"Osorio-Calderon","suffix":""},{"id":601341577,"identity":"b58b2cf0-b1e4-4482-a71c-a085d08f0268","order_by":1,"name":"Sandy Oliver","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Sandy","middleName":"","lastName":"Oliver","suffix":""}],"badges":[],"createdAt":"2026-01-26 02:40:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8695811/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8695811/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104075463,"identity":"7ea1c2aa-c6b4-4994-9b30-0e4460f47899","added_by":"auto","created_at":"2026-03-06 12:56:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183252,"visible":true,"origin":"","legend":"\u003cp\u003eSearch strategy and actor identification process (separate file)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8695811/v1/9912c151543449581793b95e.png"},{"id":104075470,"identity":"241d4056-e51e-45e6-82b0-af63aba12050","added_by":"auto","created_at":"2026-03-06 12:56:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe policy-driven science framework for evidence use in policymaking in LAC (separate file)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8695811/v1/8461cf91a66f3c177a8dfb3a.png"},{"id":104408666,"identity":"ca5c7e9b-3559-4f89-9c16-ee0e551b3ea0","added_by":"auto","created_at":"2026-03-11 12:43:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1316892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8695811/v1/7d4ffcce-ac62-4aa3-89fa-254b09b432b0.pdf"},{"id":104402989,"identity":"fa3b67e3-4fad-4e33-b1f6-58c36aba96f2","added_by":"auto","created_at":"2026-03-11 12:17:05","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":146411,"visible":true,"origin":"","legend":"","description":"","filename":"Database.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8695811/v1/9ac2dda10df46f99108bc950.xlsx"},{"id":104403260,"identity":"4dff988e-178f-4faa-93dd-c959d4ee79c7","added_by":"auto","created_at":"2026-03-11 12:17:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19179,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8695811/v1/c3daae870db551951fa94487.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Conceptualisations and pathways of evidence for policymaking: mapping evidence actors producing and mobilising evidence for policy in Latin America and the Caribbean","fulltext":[{"header":"Background","content":"\u003cp\u003eEvidence-informed policymaking (EIPM) has gained growing attention among governments, international organisations, and research funders as an approach intended to support the quality, legitimacy, and effectiveness of public decision-making. Yet decades of research show that the use of evidence in policymaking depends not only on the availability or quality of research, but on the actors who produce, translate, broker, and apply it, and on the relationships that connect them within specific institutional and political contexts (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These actors collectively form what is increasingly described as an evidence ecosystem: a constellation of organisations, individuals, practices, and norms that shape how evidence circulates, gains authority, and influences policy decisions (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Latin America and the Caribbean (LAC), available analyses of the evidence ecosystem commonly describe evidence-to-policy efforts as fragmented and uneven across countries and sectors, with the most visible and institutionalised initiatives concentrated in the health sector (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Evidence-to-policy work beyond health is less consistently documented and often described using heterogeneous terminology, which limits the ability to identify who produces, mediates, and uses evidence across policy sectors and to compare evidence ecosystems within the region. While studies such as Jessani et al. (2016) and Oliver and Boaz (2019) provide important insights into actor roles and knowledge-translation arrangements, they draw primarily on cases outside LAC and are therefore best understood as examples from the broader global literature on actor mapping, rather than as empirical evidence about actor configurations in the LAC context (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese limitations are not only empirical but also methodological. Actor mapping in EIPM frequently relies on expert nomination, participation in established networks, or affiliation with high-profile initiatives, which shapes which actors become visible in analyses (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). While such approaches are often pragmatic, they tend to privilege well-resourced organisations, internationally connected institutions, and policy sectors with consolidated evidence infrastructures, while obscuring less formal, locally embedded, or cross-sector actors. As a result, mapping exercises may reproduce existing power asymmetries within evidence ecosystems and offer only a partial account of how evidence is produced, brokered, and used in practice (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond questions of visibility and methodology, a further challenge in analysing the evidence ecosystem lies in the diverse ways in which \u0026ldquo;evidence\u0026rdquo; itself is conceptualised and valued. The literature shows that evidence is not a singular or neutral category, but encompasses multiple forms of knowledge, including formal research, administrative data, professional expertise, and experiential or contextual knowledge (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Different actors and policy sectors prioritise these forms of evidence in distinct ways, shaped by institutional mandates, professional norms, and decision-making contexts (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). As a result, what counts as relevant or credible evidence varies across settings, influencing how evidence is mobilised, whose knowledge is legitimised, and which actors gain influence within evidence ecosystems. Yet, existing actor-mapping approaches rarely make these epistemic differences explicit, limiting their ability to capture how evidence circulates and is used across heterogeneous policy sectors.\u003c/p\u003e \u003cp\u003eThese methodological and conceptual challenges highlight the limitations of existing approaches to mapping evidence actors. Actor mappings that do not account for variation in how evidence is defined, valued, and mobilised risk conflating fundamentally different forms of engagement and hiding important distinctions between actors\u0026rsquo; roles within evidence ecosystems. This is particularly problematic in comparative and cross-sectoral analyses, where differences in evidence cultures, professional norms, and decision-making contexts shape how actors interact with evidence and policymakers. Addressing these challenges requires analytical tools that can move beyond simple identification of actors to capture how they engage with evidence and policymaking across heterogeneous settings.\u003c/p\u003e \u003cp\u003eThis study addresses these gaps by applying a systematic actor-mapping approach to identify organisations and individuals engaged in the elaboration and mobilisation of evidence for policy across LAC, and to examine how they engage with policymakers across different modes of evidence use. The analysis is guided by the following research question: Who are the main actors involved in the evidence ecosystem in LAC and how do they engage with policymakers across different modes of evidence use? To address this question, the study makes three contributions. First, it proposes a transparent and replicable web-based methodology for mapping evidence actors across countries and policy sectors. Second, it advances an engagement typology that distinguishes actors\u0026rsquo; mode and reach of involvement while remaining sensitive to differences in how evidence is conceptualised and mobilised. And third, it introduces a conceptual framework that links different types of actors, their ways of engaging with policymakers, and the diverse forms of evidence used within evidence ecosystems.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe conducted a descriptive mapping study of actors engaged with evidence for policy across LAC. Using publicly available online sources, we extracted information on actors\u0026rsquo; mandates, outputs, partnerships, and evidence for policy activities and classified observable modes of engagement. The primary units of analysis were institutions and researchers.\u003c/p\u003e\n\u003ch2\u003eInclusion criteria\u003c/h2\u003e\n\u003cp\u003eActors were eligible if they met both core criteria:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eLocation:\u003c/strong\u003e Based in a country in LAC.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolicy intent:\u003c/strong\u003e Publicly stated that their work or research aims to inform policymaking or policy decisions through the use of evidence.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eActors meeting both core criteria were included if they also met at least one additional criterion:\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eEvidence products:\u003c/strong\u003e Production or use of policy-relevant evidence products (e.g., evidence syntheses, evaluations, modelling, data analysis, qualitative insights, technology or cost-effectiveness assessments, guidelines, or other evidence outputs intended to inform policy).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNetworks/partnerships:\u003c/strong\u003e Membership in, or partnership (formal or informal) with at least one of the following: knowledge mobilisation/knowledge translation platforms or networks; government departments or agencies; civil society organisations; advocacy networks; or organisations representing key populations.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEngagement activities:\u003c/strong\u003e Activities aimed at informing policymakers or civil society (e.g., deliberative dialogues, briefings/consultations, workshops/webinars, task forces or advisory groups, community mobilisation activities oriented to policy influence).\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003eSearch strategy\u003c/h2\u003e\n\u003cp\u003eWe identified actors using a structured, multi-source web-based search conducted in two stages, followed by snowball sampling. First, we screened membership lists and partner organisations of established knowledge mobilisation and evidence-to-policy networks relevant to LAC (e.g., EVIPNet, Campbell Collaboration, Cochrane, Health Systems Global, On Think Tanks, and regional evidence hubs). Second, we reviewed institutional websites of networks, public agencies, universities, research centres, think tanks, and civil society organisations to assess eligibility and identify additional actors.\u003c/p\u003e\n\u003cp\u003eTo complement this, we searched academic and grey literature on evidence use and knowledge translation, identifying actors through author affiliations and institutional references. Snowball sampling was then applied by tracing partnerships, collaborations, and acknowledgements reported on institutional websites and publications. The preliminary list of identified actors was cross-validated with the Latin America and Caribbean Evidence Hub (Hub LAC) (16), which conducted a parallel mapping exercise using complementary criteria and supported final verification of actor inclusion. The database was finalised on 31 March 2023. Figure 1 summarises the search strategy and actor identification process.\u003c/p\u003e\n\u003cp\u003eFigure 1: Search strategy and actor identification process (separate file)\u003c/p\u003e\n\u003ch2\u003eData analysis\u003c/h2\u003e\n\u003cp\u003eWe analysed the extracted data in two steps. First, we conducted a structured descriptive analysis to classify evidence actors by type (institution or researcher) and to summarise their stated mandates, policy sectors, evidence products, partnerships, and engagement activities. Based on these observable features, we coded each actor\u0026rsquo;s mode of engagement based on how they produced or mobilised evidence for policymaking, applying a coding framework developed iteratively during extraction.\u003c/p\u003e\n\u003cp\u003eSecond, we developed an engagement typology to characterise variation in actor\u0026ndash;policy interaction. Evidence actors\u0026rsquo; reach was recognised by the consistency and intensity of their documented interactions with evidence in policymaking processes, drawing on publicly reported activities such as the production of policy-facing outputs (e.g., policy notes, reviews, etc.), participation in advisory spaces, convening or supporting deliberative dialogues, formal partnerships with government, and sustained capacity-building or implementation support.\u003c/p\u003e\n\u003cp\u003eUsing the typology and coded engagement features, we then amalgamated recurrent patterns into a conceptual framework describing actor interaction with policymakers across different evidence pathways. This framework was derived through iterative comparison across actor types and policy sectors, focusing on how different forms of evidence and engagement strategies aligned with either more research-led or more problem-driven modes of evidence mobilisation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe mapping exercise identified 357 actors engaged in activities related to evidence for policy across LAC. After applying the inclusion criteria, the final dataset comprised 137 evidence actors. Of these, 95 were institutions and 36 were individual researchers, where researchers are not necessarily are hosted in the institutions identified. These 137 evidence actors formed the sample for analysis. An additional six decision makers were identified as points of interaction with evidence but were not included as units of analysis.\u003c/p\u003e\n\u003cp\u003eThe identified evidence actors were distributed across multiple countries in the region and spanned a wide range of policy sectors. They varied substantially in their organisational form, mandates, and modes of engagement with evidence and policymaking, reflecting the heterogeneity of evidence ecosystems across LAC. The sections that follow examine how evidence is conceptualised and mobilised across sectors, how this shapes interaction pathways with policymakers, and how evidence actors engage within these configurations.\u003c/p\u003e\n\u003ch2\u003eSectoral conceptions of evidence\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAcross the mapped evidence actors, understandings of what constitutes relevant evidence varied systematically by policy sector. Evidence was not treated as a uniform category, but encompassed diverse forms, including formal scientific research, administrative and programme data, qualitative and traditional knowledge, and communication-oriented products intended for public engagement or advocacy. These differences became visible not only in actors\u0026rsquo; self-descriptions, but also during actor identification: searches of academic and grey literature using common evidence terms (e.g. \u0026ldquo;evidence-informed decision-making\u0026rdquo;, \u0026ldquo;knowledge mobilisation\u0026rdquo;, \u0026ldquo;research use\u0026rdquo;) and subsequent snowball sampling surfaced additional researchers and institutions in policy sectors beyond health. Overall, sectoral variation was reflected both in the types of evidence produced and in the terminology evidence actors used to describe their work.\u003c/p\u003e\n\u003cp\u003eIn sectors with more established technical and administrative infrastructures\u0026mdash;such as economics, energy, science and technology, and health (e.g. health technology assessment)\u0026mdash;evidence was more often framed in terms of scientific rigour, methodological standards, and formal outputs such as evaluations, reviews, and quantitative analyses. By contrast, in sectors shaped by stronger civic mobilisation or weaker data systems\u0026mdash; such as gender, environment and climate change, and development\u0026mdash; evidence actors more often focused on the context, thus looking into more qualitative or experiential knowledge, frequently mobilised through less conventional formats (e.g. visuals, posters, radio programmes). More heterogeneous sectors, including education, political sciences, and security, showed mixed patterns, combining formal analysis with administrative and context-specific evidence.\u003c/p\u003e\n\u003cp\u003eThese sectoral differences shaped not only the forms of evidence prioritised, but also the audiences to whom evidence was directed and the ways in which it was mobilised in relation to policymaking. This variation provided the empirical basis for distinguishing between two broad approaches to evidence use identified across the mapping.\u003c/p\u003e\n\u003ch2\u003eScience-driven and problem-driven approaches to evidence use\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAnalysis of sectoral evidence practices revealed two dominant, though not mutually exclusive, approaches to evidence use: science-driven and problem-driven. These approaches differed in their epistemic orientation, preferred methods, target audiences, and modes of engagement with policy processes.\u003c/p\u003e\n\u003cp\u003eThis science-driven orientation is reflected in evidence actors whose legitimacy rests primarily on methodological rigour, technical expertise, and the production of formal research outputs. In the health sector, this is exemplified by health technology assessment (HTA) bodies such as the Institute for Clinical Effectiveness and Health Policy (IECS) in Argentina, which produces systematic reviews, economic evaluations, and modelling studies to inform coverage decisions and optimise the implementation of health interventions. Evidence is mobilised through highly standardised technical products, supported by relatively strong health information systems, and primarily circulated within expert and bureaucratic policy communities.\u003c/p\u003e\n\u003cp\u003eSimilar science-driven logics were observed in other policy sectors. In economic and social development, the Economic Commission for Latin America and the Caribbean (ECLAC) produces quantitative analyses, macroeconomic models, and statistical projections that prioritise analytical consistency and technical credibility. Evidence is disseminated through flagship reports and datasets and used to inform policy design and optimisation at the executive level, reinforcing an expert-led model of evidence use with limited reliance on participatory or deliberative processes.\u003c/p\u003e\n\u003cp\u003eThe problem-driven orientation was evident among organisations working closely with communities, practitioners, and advocacy networks, where evidence use was shaped by specific social and policy challenges rather than by formal technical appraisal alone. For example, the Centro de Investigaci\u0026oacute;n y Promoci\u0026oacute;n del Campesinado (Center for Research and Promotion of the Peasantry, CIPCA in spanish ) mobilises locally generated qualitative evidence, participatory research, and experiential knowledge to document rural livelihoods, land governance, and food security challenges in Bolivia. In contexts characterised by uneven or fragmented data systems, evidence is translated into policy briefs, community reports, and training materials aimed at problem framing, agenda-setting, and strengthening the capacity of peasantry organisations to engage in policy and rights-based advocacy.\u003c/p\u003e\n\u003cp\u003eSimilar problem-driven logics were observed among organisations addressing issues such as gender, security, and social inclusion, including Fundaci\u0026oacute;n Construir and Ita\u0026uacute; Social, where evidence use frequently combined qualitative analysis, contextual knowledge, and capacity-building activities oriented towards advocacy, public debate, and policy responsiveness.\u003c/p\u003e\n\u003cp\u003eWhile analytically distinct, these two approaches frequently coexisted within and across sectors. Table 1 summarises the defining characteristics of science-driven and problem-driven approaches to evidence use.\u003c/p\u003e\n\u003cp\u003eTable 1: Science and Problem driven approach (separate file)\u003c/p\u003e\n\u003ch2\u003eTop-down, bottom-up, and overlapping pathways of evidence interaction\u003c/h2\u003e\n\u003cp\u003eBuilding on the sectoral approaches described above, we identified three broad pathways through which evidence is mobilised in relation to policymaking: top-down, bottom-up, and overlapping. These pathways are defined primarily by who evidence is produced for and how it is intended to influence policy outcomes.\u003c/p\u003e\n\u003cp\u003eIn top-down pathways, evidence is produced mainly for policymakers and technical decision-makers, with the aim of informing policy design, implementation, or optimisation from within formal institutional settings. These pathways tend to rely on quantitative methods and institutionalised data systems, and evidence is mobilised through policy-facing reports, commissioned analyses, and advisory processes. Key actors include research institutes, universities, and government-affiliated bodies.\u003c/p\u003e\n\u003cp\u003eIn contrast, bottom-up pathways direct evidence primarily towards society, including civil society organisations, communities, and specific population groups. Here, evidence is used to support advocacy, mobilisation, and public debate as a way of influencing policy agendas indirectly. These pathways commonly draw on qualitative, participatory, and context-specific knowledge and operate in settings where data systems are weaker or more fragmented. Civil society and grassroots organisations play a central role, particularly in sectors such as environment, gender, climate change, and human rights.\u003c/p\u003e\n\u003cp\u003eOverlapping pathways combine both directions of evidence use. In these configurations, evidence circulates between policymakers and societal actors, often using mixed or selectively combined approaches. Their defining feature is a strong emphasis on brokering and mediation, with intermediary organisations translating and aligning different forms of evidence to respond simultaneously to policy demands and societal needs. We conceptualise these pathways as problem-driven science, where scientific robustness and contextual relevance are deliberately brought together.\u003c/p\u003e\n\u003cp\u003eTable 2 summarises the main dimensions distinguishing these three pathways, including audiences, methods, data sources, focus, and key actors.\u003c/p\u003e\n\u003cp\u003eTable 2: Dimensions of top-down, bottom-up, and overlapping approaches to evidence for policy (separate file)\u003c/p\u003e\n\u003cp\u003eEmpirical examples from the mapping illustrate how these pathways operate in practice. A top-down pathway is exemplified by the F\u0026oacute;rum Brasileiro de Seguran\u0026ccedil;a P\u0026uacute;blica (Public Security Brazilian Forum, in Portuguese), which produces quantitative indicators and analytical reports on crime and violence that are explicitly addressed to government and legislative audiences. A bottom-up pathway is illustrated by Natura, which mobilises evidence through deliberative dialogues to engage civil society actors around environmental issues and support their interaction with policymakers. An overlapping pathway is reflected in the \u0026ldquo;Observatorio de pol\u0026iacute;ticas p\u0026uacute;blicas en actividad f\u0026iacute;sica y alimentaci\u0026oacute;n\u0026rdquo; (Public Policy Observatory in physical activity and diet, in spanish), which combines research outputs with deliberative dialogues, enabling evidence to circulate between experts, decision-makers, and wider stakeholder groups.\u003c/p\u003e\n\u003ch2\u003eReach of policy engagement by evidence actors\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe classified the reach of institutions and researchers based on whether they met three observable criteria:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003ePartnerships (Criteria C):\u0026nbsp;\u003c/strong\u003emembers or partners \u0026ndash;formal or informal\u0026ndash; of one or more of the following: Knowledge Mobilisation (KM)/Knowledge Translation Platforms (KTP) networks, Government departments or agencies, civil society groups, advocacy networks, key population (i.e. indigenous people, LGTB, etc).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eProducts (Criteria D):\u003c/strong\u003e systematic reviews, literature reviews, rapid reviews, scoping reviews, policy notes, and other type of documents to communicate evidence.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eActivities (Criteria E):\u003c/strong\u003e Deliberative dialogues, Workshops/webinars to inform policymakers, conferences, presentations, and other activities that are focused on informing policymakers using evidence.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eEngagement reach reflected the combination of criteria achieved by each actor, distinguishing strong engagement (sustained relationships underpinning exchange or integrated efforts), professional evidence networks (circulating evidence while overlooking policy makers and communities), and indirect policy engagement (through evidence for advocacy and activism), as summarised in Table 3. All classifications were based on publicly available documentation of each evidence actor.\u003c/p\u003e\n\u003cp\u003eTable 3: Evidence actors\u0026rsquo; reach with evidence for policy\u0026nbsp;(separate file)\u003c/p\u003e\n\u003cp\u003eStrong engagement reflected exchange or integrated efforts, where evidence actors combined sustained relationships with decision makers with policy-facing products and active engagement activities. This pattern often appeared among international organisations and well-connected NGOs or academic institutions with established communication channels to government, participation in national or regional networks (e.g. EVIPNet or the Brazilian Coalition for Evidence), and, in some cases, institutionalised citizen consultations that supported co-creation alongside direct policy engagement.\u003c/p\u003e\n\u003cp\u003eA pattern of networks circulating evidence included organisations with specific target populations and clear aims that could be participating in knowledge-translation networks but did not consistently produce policy-facing evidence products. It also included some development-oriented think tanks that generated substantial evidence but circulated it mainly through professional networks or academic outlets rather than through sustained, direct engagement with policymakers or communities. In addition, some institutions with partnerships with governments\u0026mdash; such as government-affiliated or tertiary research organisations, produce isolated evidence products without getting involved in the process of the policymaking. For example, Aru Foundation explicitly frames its work around evidence-based policymaking and policy-relevant research, aiming to influence policy debates. Publicly available information highlights technical publications and locally organised academic seminars, while activities related to knowledge translation or direct policy engagement are not explicitly described.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndirect policy engagement reflected a pattern of evidence for advocacy efforts and commonly characterised smaller, locally embedded organisations\u0026mdash;particularly in areas such as climate change or human rights\u0026mdash;whose primary aim was to empower communities with evidence and scientific knowledge. For these evidence actors, the objective was not necessarily to communicate evidence directly to policymakers, but to strengthen civil society awareness, capacity, and mobilisation through public-facing communication and citizen-focused capacity-building, with limited emphasis on direct policymaker engagement.\u003c/p\u003e\n\u003cp\u003eEngagement reach varied across actor types and across the evidence-use approaches described above. As shown in the Appendix, Brazil leads in the identification of actors with strong engagement, including both institutions and researchers. It is followed by Colombia and Chile, which display a higher number of actors with intermediate engagement. Argentina also plays an important role, although no actors were identified there with strong engagement.\u003c/p\u003e\n\u003cp\u003eOverall, the actors working towards the institutionalisation of EIPM\u0026mdash;whether formally or informally and with their different reach of engagement\u0026mdash;operate in varied ways depending on country and sector. Within each country and policy domain, diverse evidence ecosystems and sub-ecosystems coexist. The levels of institutionalisation of EIPM, as well as the dynamics of these ecosystems, extend beyond the scope of this mapping. To understand how actors navigate their evidence ecosystems and how their actions influence the institutionalisation of EIPM, it is essential to examine the context-specific features of each ecosystem.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined how evidence is conceptualised, mobilised, and connected to policymaking across LAC through a systematic mapping of evidence actors and their engagement practices. By combining a sector-sensitive analysis of the evidence ecosystem with an analytical typology of interaction pathways and actor engagement, the study makes both a methodological and conceptual contribution to the literature on evidence for policy. Rather than treating evidence use as uniform or linear, the findings show that it is plural, context-dependent, and structured by intersecting orientations and pathways that shape how evidence enters political decision-making.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEvidence use as plural and contextually embedded\u003c/h2\u003e \u003cp\u003eThe findings confirm that what counts as \u0026ldquo;evidence\u0026rdquo; varies substantially across policy sectors and institutional settings (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Across the mapped ecosystems, evidence encompassed scientific research, administrative and programme data, indigenous and experiential knowledge, and communication-oriented products designed for public engagement or advocacy. Their relative prominence differed systematically by sector, reflecting variations in data infrastructures, professional norms, and civic dynamics.\u003c/p\u003e \u003cp\u003eThese patterns reinforce critiques of narrow, technocratic conceptions of EIPM (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and highlight evidence as socially embedded rather than methodologically neutral. In LAC contexts, characterised by uneven state capacity, political contestation, and strong civil society mobilisation, problem-driven forms of evidence use emerge not as deficits but as adaptive responses to contextual constraints and legitimacy demands.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePathways of interaction and the centrality of intermediaries\u003c/h2\u003e \u003cp\u003eBy linking evidence orientations to top-down, bottom-up, and overlapping pathways of interaction, the study adds analytical clarity to how evidence reaches policymaking arenas. Top-down pathways reflect evidence directed primarily to policymakers through formal advisory and technical channels, while bottom-up pathways involve evidence directed first to society to influence policymaking indirectly through advocacy, mobilisation, and public debate.\u003c/p\u003e \u003cp\u003eFocusing on pathways adds to our understanding of how evidence informs policy. The strong engagement observed in this study is direct mutual engagement between evidence producers and decision makers. It accommodates both the relationship model and systems model of knowledge translation distinguished by Best and Holmes (2010) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Circulating evidence primarily amongst professionals and academics, while overlooking both policy makers and communities, aligns with Best and Holmes\u0026rsquo; linear \u0026lsquo;push\u0026rsquo; model. A third pathway takes an indirect route to policy engagement by leveraging wider social system to strengthen advocacy efforts with evidence but not necessarily including all the key stakeholders as in Best and Holmes\u0026rsquo; system model.\u003c/p\u003e \u003cp\u003eCrucially, the identification of overlapping pathways highlights the central role of intermediary and boundary-spanning evidence actors. These evidence actors do not merely transmit evidence; they actively broker, translate, and align different forms of knowledge, audiences, and policy logics. In fragmented governance environments such as those observed across LAC, these brokerage functions are essential for sustaining evidence use across sectors and political cycles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAn integrative framework of evidence-use pathways\u003c/h2\u003e \u003cp\u003eBringing these insights together, we propose an integrative framework that links evidence orientations, interaction pathways, and actor roles (Fig.\u0026nbsp;2). The framework organises evidence use along two intersecting axes that capture different features of how evidence moves in policymaking.\u003c/p\u003e \u003cp\u003eThe vertical axis (top-down \u0026ndash; bottom-up) captures who evidence is directed to first. Top-down pathways occur when evidence is produced or translated to be used directly by policymakers (e.g., ministries, agencies, advisory bodies) within formal decision processes such as agenda-setting, appraisal, implementation, or evaluation. Bottom-up pathways occur when evidence is produced or translated to be used directly by the public\u0026mdash;citizens, communities, social movements, or key stakeholders\u0026mdash;so they can shape policy indirectly through advocacy, mobilisation, accountability, and public debate.\u003c/p\u003e \u003cp\u003eThe horizontal axis (science-driven \u0026ndash; problem-driven) captures how evidence is defined and legitimised. Science-driven approaches privilege methodological formalisation and technical credibility, typically relying on structured research outputs (e.g., evaluations, modelling, evidence syntheses) and stronger data infrastructures. Problem-driven approaches privilege contextual fit and responsiveness to lived problems, often drawing on qualitative, experiential, and local/indigenous knowledge and using communicative formats that travel well in civic spaces (e.g., visuals, media products, activist toolkits).\u003c/p\u003e \u003cp\u003eThese axes are analytically independent, even if they often align in practice. For example, policymakers frequently commission science-driven products for top-down use (e.g., technical evaluation to optimise implementation), while civil society actors often mobilise problem-driven evidence for bottom-up use (e.g., indigenous knowledge and community testimony to support rights-based claims). However, mismatches are also common: civil society may deploy science-driven evidence (e.g., quantified environmental risks) and governments may draw on problem-driven inputs (e.g., consultations, deliberative dialogues) when legitimacy and feasibility matter.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2: The policy-driven science framework for evidence use in policymaking in LAC (separate file)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNormatively, more legitimate and effective evidence use often emerges when these pathways converge. When evidence reaches policymakers through institutional channels \u003cem\u003eand\u003c/em\u003e reaches communities through accessible, context-sensitive communication, the policy process can combine technical robustness with social legitimacy. In this convergent space, knowledge brokers and intermediary organisations can mediate between decision makers and civil society: aligning questions, translating findings into actionable formats, and carrying societal priorities back into formal policy arenas. Conceptually, this convergence also supports a \u0026ldquo;problem-driven science\u0026rdquo; orientation: science-driven standards strengthen credibility, while problem-driven priorities ensure relevance to lived realities and targeted populations, producing evidence that is both rigorous and publicly meaningful.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eActor engagement and ecosystem maturity\u003c/h2\u003e \u003cp\u003eThe engagement typology highlights cross-country variation in the durability and intensity of interactions between evidence actors and policy makers. In some countries, evidence actors more often engaged through formal advisory spaces, intermediary organisations, and routinised policy-facing outputs, which supported sustained interaction and greater use of overlapping pathways. In others, engagement was more episodic and project-based, with evidence actors relying primarily on bottom-up routes to introduce evidence into policy debates, such as public communication, advocacy, and ad hoc consultations.\u003c/p\u003e \u003cp\u003eBrazil showed the highest concentration of highly engaged evidence actors and the clearest visibility of overlapping pathways, with intermediary organisations frequently linking technical communities, policymakers, and civil society. This configuration appeared relatively resilient to political turnover, suggesting a more institutionalised evidence ecosystem, than observed elsewhere in the mapped sample. Across other countries, like Chile, Colombia or Argentina, engagement patterns were more uneven, often concentrated in specific sectors (particularly health) or dependent on individual champions and externally supported initiatives rather than on routinised arrangements\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations of the study\u003c/h2\u003e \u003cp\u003eThis study is descriptive and analytical rather than evaluative: it develops a methodological approach, engagement typology, and integrative framework from recurring patterns observed across sectors and countries in a transparent, replicable web-based mapping (finalised 31 March 2023). A key strength is the inclusive operationalisation of \u0026ldquo;evidence\u0026rdquo;, which deliberately encompassed scientific research, administrative and programme data, qualitative and experiential knowledge, and communication-oriented outputs used in advocacy and public engagement. This breadth provided an inter-sectorial approach reducing the risk of technocratic definitions alone.\u003c/p\u003e \u003cp\u003eSeveral limitations remain. Starting with publicly available online information likely favoured actors with stronger digital presence and communication capacity, under-representing informal, locally embedded organisations and undocumented interactions. Because terminology identification relied on a wide set of search terms and observable signals of evidence-for-policy intent (rather than a single shared label such as \u0026ldquo;EIPM\u0026rdquo; or \u0026ldquo;knowledge translation\u0026rdquo;), terminological diversity affected how easily actors could be located and coded in some sectors, even though the definition of evidence was intentionally broad. The dataset captures a cross-sectional snapshot and cannot assess change over time or responsiveness to political turnover. Finally, despite a detailed search strategy and cross-validation with the LAC Hub, some actors and relationships may have been missed where documentation was incomplete or inconsistent.\u003c/p\u003e \u003cp\u003eImportantly, these constraints are unlikely to change the paper\u0026rsquo;s core contributions: the typology and framework are grounded in consistent patterns across a diverse set of actors and sectors, and they remain informative even under partial visibility.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study makes four contributions to understanding evidence use in policymaking, demonstrated through a regional evidence actor mapping in Latin America and the Caribbean:\u003c/p\u003e \u003cp\u003e- First, it identifies two sector-sensitive orientations to evidence use: science-driven and problem-driven. Both differ in how evidence actors define and legitimise evidence, which methods they prioritise, which audiences they address, and which evidence products they mobilise.\u003c/p\u003e \u003cp\u003e- Second, it distinguishes three pathways through which evidence connects to policymaking: top-down, bottom-up, and overlapping; and systematise the dimensions that differentiates across sectors.\u003c/p\u003e \u003cp\u003e- Third, it proposes an integrative framework linking evidence orientations, interaction pathways, and the role of intermediaries in connecting decision makers and civil society.\u003c/p\u003e \u003cp\u003e- Fourth, it introduces a typology of actor engagement (high, intermediate, limited) that supports comparison of how consistently evidence actors combine partnerships, evidence products and activities.\u003c/p\u003e \u003cp\u003eTogether, these tools offer a transferable way to analyse evidence ecosystems beyond a single sector and context, beyond linear or single network models.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEIPM \u0026ndash; Evidence-informed policymaking\u003c/p\u003e\n\u003cp\u003eKT \u0026ndash; Knowledge translation\u003c/p\u003e\n\u003cp\u003eLAC \u0026ndash; Latin America and the Caribbean\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is derived from a chapter of a PhD Social Sciences dissertation named \u0026ldquo;Institutionalising Evidence-Informed Policymaking: Social and Political Factors in Latin America\u0026rdquo;, in the University College London. The authors wish to express their gratitude to everyone who contributed to the study, especially the Latin American and Caribbean Hub for Evidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as part of the Partnership for Evidence and Equity in Social Systems (PEERSS), funded by the International Development Research Centre and the Hewlett Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth authors are affiliated with the EPPI-Centre, UCL Social Research Institute, University College London, London, UK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVOC conceived and designed the study, led data collection, conducted the analysis, and drafted the manuscript. SO provided conceptual guidance, methodological input, and critical feedback throughout the research process. All authors contributed to revising the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo ethical approval was sought as this study does not involve human subject data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeiss CH. 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Available from: https://doi.org/10.1007/s10479-014-1578-6\u003c/li\u003e\n\u003cli\u003eYu X, Wu S, Sun Y, Wang P, Wang L, Su R, et al. Exploring the diverse definitions of \u0026lsquo;evidence\u0026rsquo;: a scoping review. BMJ Evidence-Based Medicine [Internet]. 2024 Feb 1 [cited 2025 Jan 5];29(1):37\u0026ndash;43. Available from: https://ebm.bmj.com/content/29/1/37\u003c/li\u003e\n\u003cli\u003eHubLAC. HubLAC. 2024 [cited 2024 Apr 18]. Hub de Evidencias de Latinoam\u0026eacute;rica y del Caribe. Available from: http://hublac.org/en/\u003c/li\u003e\n\u003cli\u003eCairney P. Health and Advocacy: What Are the Barriers to the Use of Evidence in Policy? In: Cairney P, editor. The Politics of Evidence-Based Policy Making [Internet]. London: Palgrave Macmillan UK; 2016 [cited 2025 Dec 11]. p. 51\u0026ndash;84. Available from: https://doi.org/10.1057/978-1-137-51781-4_3\u003c/li\u003e\n\u003cli\u003eBest A, Holmes B. Systems thinking, knowledge and action: towards better models and methods. 2010 May 1 [cited 2026 Jan 25]; Available from: https://bristoluniversitypressdigital.com/view/journals/evp/6/2/article-p145.xml\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Science and Problem driven approach\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScience-driven approach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProblem-driven approach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003ePrimary purpose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 240px;\"\u003e\n \u003cp\u003eInform policy design, implementation, or optimisation through scientifically robust evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eFrame, prioritise, and respond to policy problems through contextually relevant evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eEpistemological orientation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 240px;\"\u003e\n \u003cp\u003ePositivist or post-positivist; emphasis on objectivity, validity, and generalisability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eConstructivist or interpretative; emphasis on relevance, meaning, and situated knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003ePreferred methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 240px;\"\u003e\n \u003cp\u003eQuantitative and experimental methods; modelling; systematic reviews; statistical analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eQualitative, participatory, and mixed methods; experiential and locally generated knowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eTypes of evidence prioritised\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 240px;\"\u003e\n \u003cp\u003ePeer-reviewed research, evaluations, administrative and programme data, evidence syntheses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eQualitative insights, case studies, community knowledge, contextual and experiential evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eTarget audiences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 240px;\"\u003e\n \u003cp\u003ePolicymakers, technical staff, scientific and expert communities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eCivil society organisations, communities, advocacy groups, and broader publics (often indirectly influencing policymakers)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eData environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 240px;\"\u003e\n \u003cp\u003eRelatively strong, institutionalised, and standardised data systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eWeaker, fragmented, or uneven data systems; reliance on alternative or proxy evidence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eModes of policy influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 240px;\"\u003e\n \u003cp\u003eFormal advisory processes, technical inputs, evidence appraisal within institutional decision cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eAgenda-setting, problem framing, mobilisation, advocacy, and public debate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eTypical products and activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 240px;\"\u003e\n \u003cp\u003eEvaluations, modelling, evidence syntheses, technical reports, policy briefs, advisory committee participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eVisual and media products (e.g. infographics, radio), local surveys, community engagement, consultations, advocacy campaigns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eKey evidence actors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 240px;\"\u003e\n \u003cp\u003eUniversities, research institutes, government agencies, technical units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003eCivil society organisations, grassroots groups, advocacy networks, activist organisations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: The two approaches are analytically distinct but often coexist within and across policy sectors.\u003c/p\u003e\n\u003cp\u003eTable 2: Dimensions of top-down, bottom-up, and overlapping approaches to evidence for policy\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTop-down\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eScience-driven\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverlap\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProblem driven science\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBottom-up\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eProblem-driven\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpistemological orientation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePositivist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eConstructivist and interpretative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eQuantitative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eMixed (selected) methods-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eQualitative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAudiences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAcademic and scientific community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePolicymakers, intermediaries, multi-stakeholder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eCivil Society or key populations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eStrong, institutionalised data systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSelective or combined sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWeak or fragmented data systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFocus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eImplementation, technical focus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eBrokering, mediation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAdvocacy, mobilisation, political influence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey actors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eUniversities, research institutes, government agencies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eIntermediary organisations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNGOs, grassroots organisations, activist groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e: Levels of evidence actors\u0026rsquo; engagement in Evidence for policy\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartnerships \u003csup\u003e(a)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProducts \u003csup\u003e(b)\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivities \u003csup\u003e(c)\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrong engagement\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(Underpinning exchange or integrated efforts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfessional evidence networks\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(Circulating evidence while overlooking policy makers and communities)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect policy engagement\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(Evidence for advocacy or activism)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e✔\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"health-research-policy-and-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hrps","sideBox":"Learn more about [Health Research Policy and Systems](http://health-policy-systems.biomedcentral.com/)","snPcode":"12961","submissionUrl":"https://submission.nature.com/new-submission/12961/3","title":"Health Research Policy and Systems","twitterHandle":"@HarpsJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Evidence-informed policymaking, Evidence use, Evidence ecosystems, Actor mapping, Knowledge brokers, Latin America and the Caribbean, Knowledge translation, Evidence for policy, policy engagement","lastPublishedDoi":"10.21203/rs.3.rs-8695811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8695811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEvidence-informed policymaking (EIPM) depends not only on the availability of research, but on how evidence is conceptualised, mobilised, and connected to decision-making through diverse actors and institutional arrangements. In Latin America and the Caribbean (LAC), existing analyses often describe evidence-to-policy efforts as fragmented and sector-specific, with limited empirical understanding of who the key actors are, how they engage with policymakers, and how different forms of evidence circulate across policy sectors. Moreover, prevailing actor-mapping approaches rarely account for variation in evidence cultures and modes of engagement, constraining comparative analysis of evidence ecosystems.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a descriptive mapping study of actors producing or mobilising evidence for policy across LAC using a transparent, web-based methodology, hereafter called evidence actors. Publicly available information from institutional websites, policy networks, academia and grey literature was systematically extracted and cross-validated with the Latin America and Caribbean Evidence Hub. Actors were included if they were based in LAC, demonstrated explicit policy intent, and produced evidence-related outputs, nurtured evidence partnerships, or conducted engagement activities. We analysed actors\u0026rsquo; mandates, evidence products, partnerships, and engagement practices, and developed an engagement typology based on observable interaction. Through iterative analysis, we derived a conceptual framework linking actor roles with evidence orientations and interaction pathways.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mapping identified 137 actors (95 institutions and 36 individual researchers) across multiple countries and policy sectors. Evidence production and mobilisation varied systematically by sector, revealing two dominant orientations: science-driven approaches prioritising methodological rigour and technical credibility, and problem-driven approaches emphasising contextual relevance and experiential knowledge. We identified three pathways through which evidence interacts with policymaking: top-down, bottom-up, and overlapping pathways. Reach of actor\u0026rsquo;s engagement ranged from strong, sustained interaction with policymakers to more limited, professional or society-oriented mobilisation. Intermediary actors played a central role in overlapping pathways, brokering evidence between technical and civic arenas.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBy integrating evidence orientations, interaction pathways, and actor engagement, this study offers a transferable framework for analysing evidence ecosystems beyond linear or sector-specific models. The findings highlight the plural and context-dependent nature of evidence use and underscore the importance of intermediaries in strengthening durable connections between evidence, policy, and society across diverse settings.\u003c/p\u003e","manuscriptTitle":"Conceptualisations and pathways of evidence for policymaking: mapping evidence actors producing and mobilising evidence for policy in Latin America and the Caribbean","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 12:55:01","doi":"10.21203/rs.3.rs-8695811/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-19T19:15:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T21:55:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242041290176149173963192344617662843073","date":"2026-03-25T13:27:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T00:03:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229252920092848795510912281209009746068","date":"2026-03-02T02:53:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-01T22:32:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T11:46:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T03:29:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Health Research Policy and Systems","date":"2026-01-26T02:25:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"health-research-policy-and-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hrps","sideBox":"Learn more about [Health Research Policy and Systems](http://health-policy-systems.biomedcentral.com/)","snPcode":"12961","submissionUrl":"https://submission.nature.com/new-submission/12961/3","title":"Health Research Policy and Systems","twitterHandle":"@HarpsJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a0183e0-5f99-4a02-8b57-f8f9e2e1d4ee","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T19:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 12:55:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8695811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8695811","identity":"rs-8695811","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}