Advancing Evidence-Informed Policy: Outcomes of the Global Research Agenda on Knowledge Translation

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This study outlines the methods and results of a multi-stage process to develop WHO’s first Global Research Agenda (GRA) on Knowledge Translation and Evidence-informed Policy-making (KT/EIP), aimed at improving research efficiency, guiding funding, increasing evidence use, fostering collaboration, and raising awareness of KT research. Methods From October 2023 to March 2025, a structured five-step approach was undertaken, starting with synthesizing existing evidence on KT strategies and priorities and complemented by primary data from a global survey. These inputs were used to develop a conceptual framework to organize research priority areas. This framework guided a global consultative process, which engaged diverse interest-holders through online consultations and Delphi surveys to jointly identify research gaps, opportunities, and priority areas for inclusion in the final research agenda. Results The initial step of evidence synthesis identified 120 research areas. Through the global consultative process, these were refined to 19 priority research areas organized into three domains: 1) Research on KT/EIP interventions, 2) Research on barriers, facilitators, and opportunities for KT/EIP, and 3) Research on KT/EIP methods, standards, measurement, theories, and frameworks. Specific research areas include strategies to institutionalize KT, targeted approaches for public health emergencies, contextual factors influencing KT/EIP uptake, and the exploration of innovative technologies like Artificial Intelligence. Conclusions This study proposes a prioritized research agenda to guide future KT/EIP research and inform funding decisions. This resource for researchers, policy-makers, and funders requires sustained engagement with interest-holders to maximize its impact. Future research should validate and refine this agenda, and ensure relevance, utility, and effective implementation across diverse settings. Research Priority-setting Agenda setting Policy-Making Knowledge Translation Evidence informed policy Delphi technique Health policy and systems research Figures Figure 1 Introduction Knowledge translation (KT) ( 1 ) research – the scientific study of methods to promote the uptake of research findings by patients, healthcare providers, managers, healthcare users and policy-makers ( 2 , 3 ) – has gained prominence as a means to bridge the evidence-policy-practice gap in health and related sectors ( 4 – 6 ). Over the past decade, numerous KT initiatives have sought to connect evidence producers and users, who often hold diverse priorities, values, and perspectives, to advance evidence-informed policy-making (EIP) ( 7 , 8 ). Among these initiatives, the WHO Evidence-informed Policy Network (EVIPNet) has played a pivotal role as both a user and generator of research on what works in KT and EIP, fostering learning through real-world application, cross-country exchange, and assessment of KT mechanisms. Despite these efforts, significant challenges remain. Only a small portion of relevant research leads to measurable impact, and the process of translating new evidence into improved public policy and health interventions remains slow and complex. This gap prompts scientific inquiry into its root causes ( 2 , 9 , 10 ). A further barrier is the limited culture of EIP among decision-makers globally ( 11 , 12 ). Translating evidence into public health policy is particularly challenging. Oliver and Boaz emphasize the need for dialogue to facilitate evidence uptake ( 13 ). However, barriers such as poor coordination and inadequate funding continue to lead to research duplication and leave critical knowledge gaps underfunded. Current research efforts aim to optimize KT strategies and produce actionable knowledge for policy-makers ( 3 , 14 , 15 ). Yet, empirical evidence regarding the effectiveness of specific KT interventions and their underlying mechanisms remains limited ( 3 ). Inconsistent funding for KT impedes the development of robust evidence, further compounded by the unequal global distribution of KT research, which is predominantly concentrated in high-income countries ("Global North"), resulting in regional underrepresentation ( 16 ). The complex and interdisciplinary nature of the evidence ecosystem makes it difficult to identify gaps and set comprehensive research priorities ( 14 ). Addressing these challenges requires strategic collaboration among evidence producers, policy-makers, intermediaries, and funders to jointly define knowledge gaps and establish relevant research agendas ( 14 , 17 ). Strong partnerships, supported by appropriate frameworks, are essential to enhance KT activities and improve health policies and outcomes ( 18 – 20 ). In response to these challenges, WHO launched a joint initiative in 2023 to identify research priorities and develop a comprehensive GRA for KT and EIP – one that spans geographies, sectors, and disciplines. Leveraging its global health leadership ( 1 ), extensive KT expertise, and convening power, WHO is uniquely positioned to lead this effort. The organization’s commitment to research agenda-setting reflects its longstanding role in supporting Member States to prioritize health research and information ( 21 , 22 ). Its leadership in KT – through normative guidance and capacity-building at national, regional and global levels – remains central to its mandate and underscores its vital role in promoting evidence-informed decision-making worldwide ( 17 , 23 ). This paper presents the methods and outcomes of a multi-stage development process of the WHO GRA on KT and EIP. The agenda aims to improve research efficiency, guide funding, increase evidence use, foster collaboration, and raise awareness of KT research. It builds on previous work, notably continuing the foundational efforts initiated by Oliver and Boaz in 2018 to transform the use of research evidence in policy-making ( 13 ). Methods From October 2023 to March 2025, a structured five-step approach was implemented, aligned with WHO’s guidelines for research prioritization ( 22 ) (Fig. 1 ). Early in the development of the GRA, WHO convened an international Advisory Committee (AC) comprising thirteen experts in KT and research prioritization. This committee provided strategic guidance throughout the process (see Supplementary File 1). Note The number of research areas listed under each consultation/survey describes the input, not the outcome. Methodological details of each phase are described below. Step 1: Preparatory Phase Evidence mapping activities During the initial stages, a scoping review ( 24 ) was conducted to map existing research in KT strategies, priorities and funding. This was complemented by a “Global Living Evidence Map on the art and science of promoting evidence-informed decision-making,” commissioned to the Pan-African Collective for Evidence (PACE) ( 25 ). To build on these findings, WHO launched a global online survey between February and March 2024, targeting researchers and professionals with expertise in KT. Following WHO ethical clearance (ERC.0004060), this survey invited participants to identify up to three major research gaps in KT or existing areas requiring further or improved investigation. Disseminated widely through global and regional networks, the survey received responses from 153 respondents, each submitting at least one research priority related to KT and EIP. The responses were reviewed and integrated with the evidence synthesis results, generating an initial list of 120 priority research areas (Supplementary File 2), which served as the foundation for the subsequent priority-setting steps. Based on these outcomes, the project team, in collaboration with the AC, developed a conceptual framework to guide the prioritization process. Structured around four overarching research domains – later consolidated into three – this framework was designed to comprehensively capture the scope of KT/EIP, organize the identified research priorities, and facilitate both the consultative process and effective dissemination of the findings (Box 1). Box 1: Conceptual Framework KT/EIP Interventions : This domain encompassed research exploring the design, implementation, sustainability, and evaluation of interventions to promote evidence-informed decision-making. Barriers, Facilitators, and Opportunities for KT/EIP : Studies examined factors that influenced the effective translation of research evidence into policy. KT/EIP Methods, Standards, and Measurements : This category focused on investigations aimed at enhancing the quality of KT via improved methods, standards, tools, and measurements. KT/EIP Theories and Frameworks : The research focuses on the development and analysis of theories that provide structured approaches to guide the KT into policy and practice. It incorporates concepts, theories, models, frameworks, taxonomies, and typologies of KT, all contributing to a deeper understanding of KT processes. Call for experts In parallel with the evidence mapping activities, WHO issued an open call in December 2023 to its six regions (Africa, Americas, Eastern Mediterranean, Europe, South-East Asia and Western Pacific) inviting KT researchers and practitioners, regardless of their organizational affiliation, to participate in a global consultative process. The objective was to engage a diverse range of interest-holders through a series of online consultations and Delphi surveys to jointly identify research gaps, opportunities, and priority areas for inclusion in the final KT research agenda. Eligibility criteria required participants to demonstrate expertise and experience in KT research and implementation within the health sector or other policy domains with significant health implications (e.g., healthcare delivery, education, environment, and agriculture). All selected experts were required to review WHO’s Code of Conduct for Experts and submit a Declaration of Interest form. No conflicts of interest were identified that could compromise the integrity of the research agenda development process. Step 2: First Expert Consultation The first expert consultation, conducted virtually on 26 March 2024, served as a structured platform for researchers and practitioners to critically examine current research gaps and opportunities in KT/EIP. During the session, participants were assigned to breakout groups aligned with the four framework domains (Box 1) and their corresponding 120 research areas (Supplementary File 2). The feedback received from participants included suggestions for refinement, consolidation, removal, or inclusion of new research areas (Supplementary File 3). Following the consultation, the project team, in collaboration with the AC and external reviewers, refined the list of research areas, removed redundancies, merged overlapping items, and reformulated others for clarity, resulting in a refined and shortened list of research areas. This revised list served as the basis for the first round of the Delphi survey. The Delphi method was selected for its ability to include geographically diverse participants, minimize the influence of dominant voices, and provide a systematic, transparent, and replicable method for aggregating individual scores ( 22 ). Two Delphi survey rounds were conducted in 2024 to build consensus on the optimal ranking of the research areas, which were subsequently discussed and further refined during follow-up consultations. Step 3: First Delphi Survey The first online Delphi survey was conducted in June-July 2024. The primary objective of this round was to reduce the list of research areas resulting from the first consultation to a more focused set of high-priority topics (Supplementary File 4a). These areas were again categorized within the four framework domains (Box 1). A Microsoft Excel-based questionnaire was chosen for respondents’ familiarity with spreadsheets as well as its flexibility, allowing participants to complete the survey offline at their convenience. Participants were given three weeks to complete the survey and were asked to score each research area based on three criteria, agreed upon by consensus of the AC via several related virtual meetings (details in Supplementary File 4b): Impact : Assess the research's potential to enhance KT/EIP, resulting in positive health and social sector outcomes. Inclusivity : Evaluate the likelihood of the research promoting equal opportunities and resources for underrepresented groups and elevating diverse perspectives. Feasibility : Determine the probability of the research's successful execution, considering scientific and financial limitations, as well as logistical arrangements and other potential barriers. Scoring System and Data Analysis The analysis followed a three-step process: Scoring : Responses were numerically coded (High = 3, Moderate = 2, Low = 1), with "do not know" and blank responses excluded. Intermediate Scores : For each research area, an intermediate score for each criterion was calculated as the ratio between the sum of the assigned scores over the number of scorers. Overall Scores : Each research area was assigned an overall score, derived as the mean of the three intermediate scores. These were then rescaled to a 0–1 scale, with 1 representing the maximum possible score. Generally, only research areas with a score of ≥ 0.70 were retained for the second Delphi survey to ensure a balanced representation across domains and to maintain a manageable number of research areas for the second Delphi round. Additionally, a comparative analysis was conducted between responses from participants in LIC/LMIC countries and those from higher-income countries; redundant or out-of-scope areas were excluded, and additional proposals from participants were incorporated. Step 4: Second Delphi Survey The second online Delphi survey was conducted with a questionnaire open from 11 November to 10 December 2024, administered via the WHO DataForm online survey platform. This round aimed to prioritize the shortlisted areas, allowing for more in-depth consideration of their relative importance. WHO invited the same cohort as in the first Delphi survey except for two individuals who withdrew for personal reasons. The 149 participants were instructed to allocate a total of 26 tokens across 34 predefined research areas to generate a prioritized shortlist. They could assign between 0 and 3 tokens per area and were required to allocate at least five tokens of the three framework domains, thereby ensuring balanced representation. All 34 research areas were displayed on a single page, grouped by domain but ordered randomly within each group, with toggle bars facilitating token distribution. Due to the constraint of only 26 tokens – about 75% of the total – participants had to make deliberate and strategic prioritization decisions. This token allocation method was chosen for its simplicity and effectiveness in clearly differentiating the relative importance of each research area. Scoring System and Data Analysis The analysis began by calculating the total number of tokens allocated to each research area and domain. The areas were ranked from highest (98 tokens) to lowest (29 tokens), with scores expressed as a percentage of the maximum possible (252 tokens). The percentages ranged from 38.9% ("Identify, develop, and evaluate strategies to institutionalize evidence") to 11.5% ("Improve criteria and processes for setting research priorities in /EIP"). Based on these results, the 34 areas were grouped into four categories: Group A (the top 10 prioritized research areas), Group B (11th -15th ), Group C (16th -20th ), and Group 0 (lowest-ranked research areas; 21st -34th ). To address the overrepresentation of academia/research sector respondents (75%), a re-weighting analysis was conducted, stratifying responses by sector and reclassifying areas into A, B, C, or 0 based on their frequency in each subgroup (Supplementary File 5). Step 5: Second Expert Consultation The final consultation took place virtually on 26 February 2025. The objective of the consultation was to validate the final version of the GRA and to gather expert input on dissemination and implementation strategies. The session was designed to be highly interactive, incorporating live discussions, collaborative whiteboards, and surveys to promote meaningful engagement. To validate the final version, participants voted between a concise list of 11 priority areas and an expanded list of 19. The consultation also addressed practical considerations for dissemination and implementation. Additionally, between 2023 and 2025, WHO project team engaged informally with research funders to raise awareness, align strategies, and gather feedback on the GRA for KT/EIP, fostering coordination without involving them directly in the formal prioritization process. Results Participants A total of 202 applications were received, of which 151 experts met the eligibility criteria and were invited to participate in the consultative process. Of these, 131 individuals participated in at least one consultation or Delphi survey. Among these, 92 identified as female and 39 as male. Geographically, respondents originated from the African Region ( 21 ), the Americas ( 46 ), the Eastern Mediterranean Region ( 10 ), the European Region ( 25 ), the South-East Asia Region ( 9 ), and the Western Pacific Region ( 20 ). Additionally, based on the income level of their country of origin, 77 respondents were from high-income countries, 22 from upper-middle-income countries, 23 from lower-middle-income countries, and 9 from low-income countries (Supplementary File 6). First Expert Consultation The initial expert consultation included 112 participants and resulted in the reduction of an initial list of 120 research areas to a refined list of 50. This new list served as the basis for the first Delphi survey (Supplementary File 4a). Demographic characteristics of the consultation participants are presented in Supplementary File 6. First Delphi Survey A total of 83/151 individuals submitted questionnaires, representing a 55% response rate. Of these respondents, 57 (69%) were women. The participant pool spanned 29 countries, encompassing all WHO regions and World Bank income levels, with 21 respondents (25%) based in low- or middle-income countries. Most respondents were affiliated with academia/research (70%). Demographic characteristics are presented in Supplementary File 6. Research Area Prioritization Initially, 35 out of 50 research areas scored ≥ 0.70 and were retained. However, comparative analysis revealed that participants from LIC/LMIC countries generally rated and prioritized aspects related to engagement processes, contextual factors, and intersectionality higher, while assigning lower importance to issues of structural inequalities and decolonization than high-income country respondents. Therefore, several areas previously excluded due to lower scores were reintroduced based on their relevance in LIC and LMIC contexts. Additionally, 41 new proposals were incorporated: one as a new research area, 24 as sub-areas under existing research areas, and 16 were excluded due to redundancy or misalignment with the study scope. Fifteen participants provided qualitative feedback, suggesting refinements to wording and structure, and identifying potential overlaps. These comments were reviewed by the project team and used to revise and harmonize the research areas. As a result of this comprehensive process, 34 research areas were selected to be carried over to the second Delphi survey (Supplementary File 5). Finally, to address thematic overlaps and ensure balanced representation across domains, two framework domains were merged into a single domain: “Methods, Standards, and Measurements” and “Theories and Frameworks”. The final three domains were distributed as follows: “Interventions” with 13 areas, “Barriers, Facilitators, and Opportunities” with 8 areas, and “Methods, Standards, Measurements, Theories, and Frameworks” comprising 13 areas (Box 2). Second Delphi Survey A total of 84/149 individuals submitted questionnaires, representing a 56% response rate. Of these respondents, 57 (68%) were women. Respondents were based in 29 countries, encompassing all WHO regions and World Bank income levels, with 24 respondents (29%, versus 25% in the first survey) based in low- or middle-income countries. Most respondents were affiliated with academia/research (75%, versus 70% in the first survey). Demographic characteristics are presented in Supplementary File 6. Additionally, the chi-square test was performed to compare gender, geographic region, country income level, and work sector between participants of the two Delphi surveys. The results indicated no statistically significant differences (Table 1 ). Table 1 Demographic characteristics of participants in the two Delphi Surveys Delphi 1 (n = 83) Delphi 2 (n = 84) p-value Gender 0.828 Female 57 (68.7%) 57 (67.9%) Male 26 (31.3%) 27 (32.1%) Geography* 0.848 AFR 13 (15.7%) 15 (17.9%) AMR 31 (37.3%) 29 (34.5%) EMR 3 (3.6%) 4 (4.8%) EUR 13 (15.7%) 13 (15.5%) SEAR 7 (8.4%) 7 (8.3%) WPR 16 (19.3%) 16 (19.0%) Country income level** 0.606 HIC 46 (55.4%) 47 (56.0%) UMIC 16 (19.3%) 13 (15.5%) LMIC 15 (18.1%) 17 (20.2%) LIC 6 (7.2%) 7 (8.3%) Participants’ sector 0.98 Academia/research 58 (69.9%) 63 (75.0%) Non-Governmental Organization 7 (8.4%) 5 (6.0%) Government 6 (7.2%) 8 (9.5%) Private sector 5 (6.0%) 3 (3.6%) United Nations 3 (3.6%) 2 (2.4%) Other international organization 2 (2.4%) 2 (2.4%) Funder*** 1 (1.2%) 1 (1.2%) *AFR = WHO African Region; AMR = WHO Region of the Americas; EMR = WHO Eastern Mediterranean Region; EUR = WHO European Region; SEAR = WHO South-East Asia Region; WPR = WHO Western Pacific Region. **HIC = High-income country; UMIC = Upper-middle income country; LMIC = Lower-middle income country; LIC = Low-income country. ***A practitioner who declared no conflict of interest Research Area Prioritization After the refinement process (see Methods section above), the research areas were distributed across the four groups as follows: 11 research areas in Group A, 4 in Group B, and 5 in Group C, with 14 areas excluded (Group 0). In addition, two thematically overlapping research areas were merged into one: “Explore engagement strategies for evidence uptake by decision-makers” and “Understand engagement processes between evidence generators, intermediaries, and users, including co-creation and co-production” were combined into a single area titled “Explore engagement processes between evidence generators, intermediaries, and users, including co-creation and co-production, for evidence uptake by decision-makers.” This consolidation resulted in a final pool of 19 prioritized research areas (cf. Supplementary File 5 for details). Table 2 presents the 19 research priorities, organized across the three thematic domains. The 'Rank' column on the left displays the overall prioritization from highest to lowest. The final three columns provide a breakdown of rankings within each individual domain. Table 2 Ranked order of 19 research areas constituting the GRA Rank Research area Interventions Barriers, facilitators, and opportunities Methods, standards, measurements, theories, and frameworks 1 Identify, develop, and assess strategies and approaches to institutionalize evidence production, translation, and use 1 2 Evaluate the impacts of KT/EIP products and interventions 2 3 Explore engagement processes between evidence generators, intermediaries, and users, including co-creation and co-production, for evidence uptake by decision-makers 3 4 Examine contextual factors and their role in research uptake, implementation, and scaling-up of KT/EIP approaches at different levels 1 5 Understand, develop, and assess strategies for translating evidence during public health emergencies 4 6 Analyze factors determining the engagement of decision-makers in evidence uptake 2 7 Integrate a Diversity, Equity, and Inclusion (DEI) lens into KT/EIP activities 3 8 Explore innovative and evolving technologies that support KT/EIP, such as Artificial Intelligence (AI) 1 9 Understand and develop approaches for policy learning and transfer of successful KT/EIP experiences between countries and sectors 4 10 Engage individuals with lived experiences in knowledge generation and translation 5 11 Investigate factors that affect the relationship between evidence generators, intermediaries, users, and citizens, including co-creation and co-production 6 12 Investigate methods, theories, and frameworks for decolonizing knowledge, research methods, and measures 2 13 Assess capacity-strengthening interventions, including their adaptation and scale-up 5 14 Identify, develop, and assess strategies for scaling-up KT/EIP interventions 6 15 Identify, develop, and assess evidence-to-policy engagement mechanisms and best practices 7 16 Improve the communication of research findings 8 17 Explore methods for intersecting KT/EIP with basic science, clinical practice, public health, political science, and science diplomacy 3 18 Explore methods for integrating and harmonizing local and global knowledge in the KT/EIP process, and for improving evidence contextualization 4 19 Develop typologies, metrics, and standardizations for KT/EIP approaches 5 Second Expert Consultation The final expert consultation included 69 participants. Among the attendees, 57% identified primarily as evidence generators, 39% as evidence intermediaries or brokers, and 4% as evidence users. Demographic characteristics are presented in Supplementary File 6. Additionally, a chi-square test was performed. There were no statistically significant differences in gender between the two consultations, but statistically significant differences were observed in both geography and country income level (Table 3 ). Table 3 Demographic characteristics of participants in the two Expert Consultation Consultation 1 (n = 112) Consultation 2 (n = 69) p-value Gender 0.113 Female 81 (72.3%) 44 (63.8%) Male 31 (27.7%) 24 (34.8%) Geography* 0.048 AFR 17 (15.2%) 12 (17.4%) AMR 41 (36.6%) 18 (26.1%) EMR 10 (8.9%) 5 (7.2%) EUR 24 (21.4%) 16 (23.2%) SEAR 7 (6.3%) 6 (8.7%) WPR 13 (11.6%) 12 (17.4%) Country income level** 0.008 HIC 67 (59.8%) 34 (49.3%) UMIC 18 (16.1%) 15 (21.7%) LMIC 18 (16.1%) 16 (23.2%) LIC 9 (8.0%) 4 (5.8%) *AFR = WHO African Region; AMR = WHO Region of the Americas; EMR = WHO Eastern Mediterranean Region; EUR = WHO European Region; SEAR = WHO South-East Asia Region; WPR = WHO Western Pacific Region. **HIC = High-income country; UMIC = Upper-middle income country; LMIC = Lower-middle income country; LIC = Low-income country. During the consultation the results of Delphi Survey 2 were presented to validate the agenda, followed by a poll to determine preferences between a shorter list of 11 research areas from Category A and an expanded list of 19 priority areas (Table 2 ). A slight preference emerged for the expanded list (53%) due to its perceived expediency, balance, comprehensiveness, and inclusion of foundational methods and standards. The shorter list was acknowledged as potentially more suitable for simplicity and ease of use for specific audiences. Despite this divergence in opinion, the consultation broadly affirmed the three-domain framework of the GRA (Box 2). Roadmap for Dissemination and Implementation Participants emphasized that the GRA should serve as a reference for regional, national and context-specific research agendas. Dissemination should be tailored to diverse audiences and utilize multiple formats, including podcasts, public lectures, training materials, and translations into UN languages. Ongoing engagement with key interest-holders – including funders, research networks, and community actors – was considered essential. Regional and national involvement, along with organizational champions, was highlighted as critical for success. Incorporating the agenda into academic curricula, especially at graduate (masters and doctoral) levels, was seen to promote sustainability. Funding remains a challenge; participants emphasized aligning with existing mechanisms, exploring co-funding models, and supporting early-career researchers. Regular monitoring and evaluation of the agenda’s implementation were deemed necessary to ensure progress. Most participants (84%) planned to use the agenda to promote KT/EIP, while 72% intended to apply it to their research strategies. The final evaluation showed high satisfaction (average rating 4.4/5) with the outcome of the agenda-setting process, while 87.5% expressed interest in continued involvement. The GRA on KT/EIP was officially launched on 15 May 2025 during a global webinar, attended by over 800 participants. Discussion Representing WHO’s first global and multisectoral agenda for research priorities in KT and EIP, this study identified 19 priority research areas aimed at addressing the most critical gaps in the field. The structured, multi-phased approach of this GRA provides a solid foundation for future research initiatives. The initial phase involved synthesizing existing evidence on KT strategies, priorities and funding mechanisms ( 24 ), alongside a living evidence map focused on EIP ( 16 ). This was further complemented by primary data collected through a global survey. Together, these inputs informed the development of a conceptual framework comprising four domains and an initial list of 120 priority areas. This framework subsequently guided the prioritization process, the organization of priorities, and the dissemination of findings. The process to develop the research agenda was evidence-informed, comprehensive, and was strengthened by being expanded and refined by thematic experts – an approach that underpins its credibility and reliability ( 22 , 26 , 27 ). This methodology aligns with existing initiatives that emphasize similar key elements: starting from a foundation of pre-existing, relevant evidence ( 28 – 30 ), applying a geographical lens that may be global, regional, or local ( 21 , 31 – 34 ), and ensuring the meaningful inclusion of diverse interest-holders ( 26 , 35 – 37 ). Furthermore, the use of varied methodological tools – such as the modified Delphi techniques employed in this study ( 28 , 34 , 38 ) – alongside guiding frameworks for both processes and outcomes ( 26 , 29 , 39 – 41 ), further reinforce the robustness and rigor of the approach. A closer look at the top ten priority research areas identified in the GRA shows that four of the top five prioritized research areas correspond to the KT/EIP interventions domain (Table 2 ). These include the identification and evaluation of strategies to institutionalize KT, as well as assessing the impact of KT activities and products. The importance of exploring engagement and co-creation processes among evidence generators, intermediaries, and users is also emphasized, as these facilitate the integration of evidence into decision-making. Additionally, the development and evaluation of targeted strategies for translating evidence during public health emergencies is highlighted. In this context, future research should also consider the sustainability and scalability of KT interventions ( 42 ), and conduct rigorous evaluation studies to assess the effectiveness of different KT strategies in diverse contexts and populations ( 43 ). Such studies should also address the costs, benefits, and unintended consequences of KT interventions, such as co-creation ( 39 , 44 , 45 ), using mixed-methods approaches to capture both quantitative and qualitative outcomes. Strategies for KT/EIP in emergency contexts have become a major priority in recent years due to the COVID-19 pandemic and other crises such as climate change. Advances in this area include the use of artificial intelligence (AI) and the development of rapid and living evidence syntheses ( 46 – 48 ). Five of the top ten high-priority research areas correspond to Domain 2 (Barriers, Facilitators, and Opportunities). These include examining contextual factors and their influence on the uptake, implementation, and scaling up of KT/EIP approaches at various levels; analyzing the determinants that influence decision-makers' engagement in evidence utilization; integrating a DEI perspective into KT and EIP activities; developing approaches for policy learning and the transfer of successful KT/EIP experiences across countries and sectors; and actively engaging individuals with lived experiences in knowledge generation and translation. Incorporating methods to explore the lived experiences of actors involved in KT processes can offer valuable insights into the barriers and facilitators of evidence uptake ( 35 , 49 – 51 ). Moreover, it is essential that prioritization methods consider individual patient needs, values, and experiences alongside population-level concerns and issues related to the health system ( 52 ). With respect to Domain 3 (Methods, standards, etc.), only one research area is represented among the top ten priorities of the GRA. This area emphasizes the exploration of innovative and evolving technologies that support KT/EIP, such as AI. The potential use of AI to inform more equitable and evidence-based policies underscores the need for robust infrastructures and regulatory frameworks to support its responsible application ( 53 ). For example, Ramezani et al. propose a framework for evaluating health equity using machine learning (ML) and social determinants: ML algorithms are used to identify key factors influencing life expectancy and health inequalities, and implementing such frameworks could support the prioritization and mitigation of health inequities in policy-making ( 54 ). The recent establishment of the Evidence Synthesis Infrastructure Collaboration (ESIC) ( 55 ), as outlined in the Cape Town Consensus (2025) ( 56 ), aims to build a user-centered, distributed infrastructure that responsibly leverages AI to support decision-makers, promote equity, and prioritize the needs of the Global South. The integration of AI marks a critical inflection point in KT/EIP, with living evidence syntheses (LES) ( 57 ), emerging as a promising strategy for delivering timely, contextualized evidence – particularly in rapidly evolving fields, as demonstrated by the National Clinical Evidence Team (NCET) during COVID-19 ( 58 ). For this research agenda, the diverse composition of participants was essential to ensuring the relevance and applicability of the identified research priorities across a wide range of contexts, particularly in LMICs where KT challenges and priorities often differ significantly ( 52 ). As the priority-setting exercise demonstrated, participants from LMICs placed greater emphasis on participatory processes, contextual factors, and intersectionality in KT, while structural inequalities and decolonization received comparatively lower prioritization. This disparity could partly be explained by the relatively smaller sample size of colleagues based in LMICs, but further research is needed to explore the underlying reasons more systematically. Moreover, the predominance of experts affiliated with academia and research institutions highlights the valuable contribution of scholarly perspectives but also underscores the need for broader engagement from other sectors – including government agencies, non-governmental organizations, and the private sector – to support a more holistic and multidisciplinary research agenda. In recent years, co-participation and co-production have gained traction in policy-oriented KT processes, reinforcing the importance of inclusive approaches ( 59 – 61 ). Engaging a diverse group of interest-holders across sectors, WHO regions, and income levels was essential to ensure that the research priorities reflect global needs and are applicable across sectors and contexts ( 28 , 36 , 37 , 62 ). Beyond demonstrating methodological rigor, the GRA provides a strategic framework to guide investments and capacity-building efforts, fostering synergies, and reducing research duplication between sectors, regions and countries. Several initiatives currently aim to promote EIDM, emphasizing the collaborative and interdisciplinary exchange of knowledge in policy development ( 13 ). The COVID-19 pandemic accelerated these efforts within the health sector ( 63 ). However, significant challenges persist in the public policy sphere, particularly the scarcity of tools to assess policy effectiveness, especially in LMICs, as noted by the International Initiative for Impact Evaluation (3ie) ( 64 ) and the UK Foreign, Commonwealth and Development Office through the Research Capacity Consortium ( 65 ). Globally, the WHO-led Global Coalition for Evidence ( 56 ), launched at the Global Evidence Summit in 2024, aims to strengthen the global evidence ecosystem and foster collaboration to institutionalize evidence-based decision-making. Aligned with these initiatives is the ESIC, already mentioned. The integration of AI represents a critical inflection point, with LES ( 57 ) emerging as an effective strategy for providing timely, contextualized evidence—particularly in rapidly evolving fields, as demonstrated by the NCET and ALEC during COVID-19( 66 ). Funding organizations such as the Impact Funders Forum, facilitated by Pew Charitable Trusts ( 67 ), emphasize participatory research to enhance the relevance and utility of research outcomes. Finally, to contribute to the implementation of the WHO GRA on KT and EIP, the Special Programme for Research and Training in Tropical Diseases (TDR) adopted and implemented a Call for Applications 2025–2026 with a focus on "One Health" and targeted at LMIC. Limitations Overall, the development of the GRA was reinforced by a broad and inclusive participatory process, encompassing both expert consultations and Delphi rounds. The Delphi methodology played a critical role in establishing and ranking research priorities. Although the composition of experts was balanced across the two Delphi rounds, some imbalances were observed within each round. For example, despite broad global representation, certain regions and income levels were more prominently represented than others, which may have influenced the prioritization outcomes. However, although various approaches to research prioritization exist, there is no single best method ( 21 ). The Delphi method, while widely used, introduces a degree of subjectivity ( 38 , 68 ), and one potential limitation is the risk of selection bias. In this priority-setting exercise, there were no statistically significant differences between participants in both rounds, which were central to the prioritization and scoring process. In addition, the overrepresentation of participants from the academic and research sector was adjusted during the analysis to mitigate potential bias. The type of Delphi method employed can also influence the outcomes ( 69 , 70 ). Accordingly, the second round was refined based on lessons learned from the limitations identified in the first. By requiring experts to allocate a fixed number of tokens in the second round, we encouraged more deliberate prioritization, reducing the tendency for participants to default to moderate or non-committal choices. Nonetheless, significant differences were found between participants in the two expert consultations in terms of geographical origin and country income level. These disparities may have influenced individual responses, shaped by individual biases or pre-existing priorities. In the final consultation, participants from LMICs represented only 29% of the total. To address this underrepresentation, selected priorities raised by LMIC participants were considered and incorporated into the final agenda. This inclusive and participatory approach is supported by existing literature on research prioritization ( 71 ), which also highlights the value of integrating lived experiences from those directly involved in KT processes ( 35 , 49 , 50 ). Conclusion This study contributes to the KT/EIP field by proposing a prioritized research agenda designed to guide future research efforts and inform policy decisions. The agenda represents a crucial resource for researchers, policy-makers, and funders, and needs to be complemented by sustained engagement with interest-holders to maximize its impact. Future research should focus on validating and refining this agenda, promoting a holistic approach to KT/EIP, and ensuring its relevance, utility, and effective implementation across various settings. Declarations Consent Statement This study involved a voluntary online survey to gather input for a research prioritization exercise on issues in the public domain. Informed consent was obtained from all survey participants; by participating, respondents explicitly agreed to the statement: “I consent to my anonymized responses being used for research and/or publication purposes by the World Health Organization.” The WHO Ethics Review Committee determined that the study was exempt from formal ethical review (Protocol ID: ERC.0004060). All external contributors to the consultations and Delphi exercise submitted declarations of interest, which were assessed for potential conflicts; none were deemed significant. Ethics approval and consent to participate The priority-setting activities were conducted in consultation with the WHO's Ethics Review Committee (ERC), which confirmed that ethical approval was not required for this type of process involving consultations and expert meetings. The record of this consultation is ERC.004060. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Disclaimer The authors alone are responsible for the views expressed in this paper and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. Funding Not applicable Author Contribution TK, BK and AB, AM, TS, RFT, and DL conceptualized and coordinated the research prioritization process, developed the protocol, and oversaw process implementation. EC, BK, and TK drafted the initial manuscript, while all authors (EC, BK, AB, AM, TS, RFT, DL and TK) made significant contributions to the subsequent reviews and read and approved the final manuscript. TK was responsible for submitting the manuscript for publication. Acknowledgement The authors would like to thank all participants of the consultations and surveys for their continued engagement, effort, and invaluable input. We are also grateful to Kathryn Oliver and the Pan-African Collective for Evidence for conducting the scoping review and developing the evidence gap map. Finally, we extend our sincere thanks to Mareike Günther, Stefano Burzo, Sarah Shanks, Marcela Velez, Davi Romao, as well as all Advisory Committee Members (listed in Supplementary File 1), for their crucial support in the development and implementation of the Delphi surveys and online consultations. Data Availability The data supporting the conclusions of this article are in the Supplementary Files. Raw datasets are available in anonymized form from the corresponding author upon request. References World Health Organization. (2004). World report on knowledge for better health: strengthening health systems. World Health Organization. Curran JA, Grimshaw JM, Hayden JA, Campbell B. Knowledge translation research: the science of moving research into policy and practice. Journal of Continuing Education in the Health Professions. 2011;31(3):174–80. Chapman E, Pantoja T, Kuchenmüller T, Sharma T, Terry RF. 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Footnotes Code of Conduct for WHO experts can be accessed through the WHO website (last accessed 11 June 2025): https://www.who.int/publications/m/item/declaration-of-interests---annex-b Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":229695,"visible":true,"origin":"","legend":"\u003cp\u003eStages of the prioritization process\u003c/p\u003e\n\u003cp\u003eNote: The number of research areas listed under each consultation/survey describes the input, not the outcome.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8056056/v1/b520121e0e02078e9fef7ac8.png"},{"id":98444747,"identity":"cd8ef589-46de-4f93-95af-d413b78a37c4","added_by":"auto","created_at":"2025-12-17 17:17:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1412292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8056056/v1/e53a0158-868b-4151-a002-69cc060cbd2e.pdf"},{"id":98071135,"identity":"38abce35-d8ed-4fe2-b0e3-7dd56f48e0a8","added_by":"auto","created_at":"2025-12-12 12:56:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":80468,"visible":true,"origin":"","legend":"","description":"","filename":"HRPSSubmissionGRAKTSupplementaryFiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-8056056/v1/df2a1cb1a66cc6b8ce38f36a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advancing Evidence-Informed Policy: Outcomes of the Global Research Agenda on Knowledge Translation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKnowledge translation (KT) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) research \u0026ndash; the scientific study of methods to promote the uptake of research findings by patients, healthcare providers, managers, healthcare users and policy-makers (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) \u0026ndash; has gained prominence as a means to bridge the evidence-policy-practice gap in health and related sectors (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Over the past decade, numerous KT initiatives have sought to connect evidence producers and users, who often hold diverse priorities, values, and perspectives, to advance evidence-informed policy-making (EIP) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Among these initiatives, the WHO Evidence-informed Policy Network (EVIPNet) has played a pivotal role as both a user and generator of research on what works in KT and EIP, fostering learning through real-world application, cross-country exchange, and assessment of KT mechanisms.\u003c/p\u003e\u003cp\u003eDespite these efforts, significant challenges remain. Only a small portion of relevant research leads to measurable impact, and the process of translating new evidence into improved public policy and health interventions remains slow and complex. This gap prompts scientific inquiry into its root causes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A further barrier is the limited culture of EIP among decision-makers globally (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTranslating evidence into public health policy is particularly challenging. Oliver and Boaz emphasize the need for dialogue to facilitate evidence uptake (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, barriers such as poor coordination and inadequate funding continue to lead to research duplication and leave critical knowledge gaps underfunded.\u003c/p\u003e\u003cp\u003eCurrent research efforts aim to optimize KT strategies and produce actionable knowledge for policy-makers (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Yet, empirical evidence regarding the effectiveness of specific KT interventions and their underlying mechanisms remains limited (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Inconsistent funding for KT impedes the development of robust evidence, further compounded by the unequal global distribution of KT research, which is predominantly concentrated in high-income countries (\"Global North\"), resulting in regional underrepresentation (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe complex and interdisciplinary nature of the evidence ecosystem makes it difficult to identify gaps and set comprehensive research priorities (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Addressing these challenges requires strategic collaboration among evidence producers, policy-makers, intermediaries, and funders to jointly define knowledge gaps and establish relevant research agendas (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Strong partnerships, supported by appropriate frameworks, are essential to enhance KT activities and improve health policies and outcomes (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn response to these challenges, WHO launched a joint initiative in 2023 to identify research priorities and develop a comprehensive GRA for KT and EIP \u0026ndash; one that spans geographies, sectors, and disciplines. Leveraging its global health leadership (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), extensive KT expertise, and convening power, WHO is uniquely positioned to lead this effort. The organization\u0026rsquo;s commitment to research agenda-setting reflects its longstanding role in supporting Member States to prioritize health research and information (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Its leadership in KT \u0026ndash; through normative guidance and capacity-building at national, regional and global levels \u0026ndash; remains central to its mandate and underscores its vital role in promoting evidence-informed decision-making worldwide (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis paper presents the methods and outcomes of a multi-stage development process of the WHO GRA on KT and EIP. The agenda aims to improve research efficiency, guide funding, increase evidence use, foster collaboration, and raise awareness of KT research. It builds on previous work, notably continuing the foundational efforts initiated by Oliver and Boaz in 2018 to transform the use of research evidence in policy-making (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eFrom October 2023 to March 2025, a structured five-step approach was implemented, aligned with WHO\u0026rsquo;s guidelines for research prioritization (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Early in the development of the GRA, WHO convened an international Advisory Committee (AC) comprising thirteen experts in KT and research prioritization. This committee provided strategic guidance throughout the process (see Supplementary File 1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eThe number of research areas listed under each consultation/survey describes the input, not the outcome.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eMethodological details of each phase are described below.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStep 1: Preparatory Phase\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003eEvidence mapping activities\u003c/h2\u003e\u003cp\u003eDuring the initial stages, a scoping review (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) was conducted to map existing research in KT strategies, priorities and funding. This was complemented by a \u0026ldquo;Global Living Evidence Map on the art and science of promoting evidence-informed decision-making,\u0026rdquo; commissioned to the Pan-African Collective for Evidence (PACE) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo build on these findings, WHO launched a global online survey between February and March 2024, targeting researchers and professionals with expertise in KT. Following WHO ethical clearance (ERC.0004060), this survey invited participants to identify up to three major research gaps in KT or existing areas requiring further or improved investigation. Disseminated widely through global and regional networks, the survey received responses from 153 respondents, each submitting at least one research priority related to KT and EIP. The responses were reviewed and integrated with the evidence synthesis results, generating an initial list of 120 priority research areas (Supplementary File 2), which served as the foundation for the subsequent priority-setting steps.\u003c/p\u003e\u003cp\u003eBased on these outcomes, the project team, in collaboration with the AC, developed a conceptual framework to guide the prioritization process. Structured around four overarching research domains \u0026ndash; later consolidated into three \u0026ndash; this framework was designed to comprehensively capture the scope of KT/EIP, organize the identified research priorities, and facilitate both the consultative process and effective dissemination of the findings (Box 1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eBox 1: Conceptual Framework\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eKT/EIP Interventions\u003c/b\u003e: This domain encompassed research exploring the design, implementation, sustainability, and evaluation of interventions to promote evidence-informed decision-making.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBarriers, Facilitators, and Opportunities for KT/EIP\u003c/b\u003e: Studies examined factors that influenced the effective translation of research evidence into policy.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eKT/EIP Methods, Standards, and Measurements\u003c/b\u003e: This category focused on investigations aimed at enhancing the quality of KT via improved methods, standards, tools, and measurements.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eKT/EIP Theories and Frameworks\u003c/b\u003e: The research focuses on the development and analysis of theories that provide structured approaches to guide the KT into policy and practice. It incorporates concepts, theories, models, frameworks, taxonomies, and typologies of KT, all contributing to a deeper understanding of KT processes.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eCall for experts\u003c/h3\u003e\n\u003cp\u003eIn parallel with the evidence mapping activities, WHO issued an open call in December 2023 to its six regions (Africa, Americas, Eastern Mediterranean, Europe, South-East Asia and Western Pacific) inviting KT researchers and practitioners, regardless of their organizational affiliation, to participate in a global consultative process. The objective was to engage a diverse range of interest-holders through a series of online consultations and Delphi surveys to jointly identify research gaps, opportunities, and priority areas for inclusion in the final KT research agenda.\u003c/p\u003e\u003cp\u003eEligibility criteria required participants to demonstrate expertise and experience in KT research and implementation within the health sector or other policy domains with significant health implications (e.g., healthcare delivery, education, environment, and agriculture).\u003c/p\u003e\u003cp\u003eAll selected experts were required to review WHO\u0026rsquo;s Code of Conduct for Experts and submit a Declaration of Interest form. No conflicts of interest were identified that could compromise the integrity of the research agenda development process.\u003c/p\u003e\n\u003ch3\u003eStep 2: First Expert Consultation\u003c/h3\u003e\n\u003cp\u003eThe first expert consultation, conducted virtually on 26 March 2024, served as a structured platform for researchers and practitioners to critically examine current research gaps and opportunities in KT/EIP. During the session, participants were assigned to breakout groups aligned with the four framework domains (Box 1) and their corresponding 120 research areas (Supplementary File 2). The feedback received from participants included suggestions for refinement, consolidation, removal, or inclusion of new research areas (Supplementary File 3). Following the consultation, the project team, in collaboration with the AC and external reviewers, refined the list of research areas, removed redundancies, merged overlapping items, and reformulated others for clarity, resulting in a refined and shortened list of research areas. This revised list served as the basis for the first round of the Delphi survey.\u003c/p\u003e\u003cp\u003eThe Delphi method was selected for its ability to include geographically diverse participants, minimize the influence of dominant voices, and provide a systematic, transparent, and replicable method for aggregating individual scores (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Two Delphi survey rounds were conducted in 2024 to build consensus on the optimal ranking of the research areas, which were subsequently discussed and further refined during follow-up consultations.\u003c/p\u003e\n\u003ch3\u003eStep 3: First Delphi Survey\u003c/h3\u003e\n\u003cp\u003eThe first online Delphi survey was conducted in June-July 2024. The primary objective of this round was to reduce the list of research areas resulting from the first consultation to a more focused set of high-priority topics (Supplementary File 4a). These areas were again categorized within the four framework domains (Box 1).\u003c/p\u003e\u003cp\u003eA Microsoft Excel-based questionnaire was chosen for respondents\u0026rsquo; familiarity with spreadsheets as well as its flexibility, allowing participants to complete the survey offline at their convenience. Participants were given three weeks to complete the survey and were asked to score each research area based on three criteria, agreed upon by consensus of the AC via several related virtual meetings (details in Supplementary File 4b):\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eImpact\u003c/b\u003e: Assess the research's potential to enhance KT/EIP, resulting in positive health and social sector outcomes.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInclusivity\u003c/b\u003e: Evaluate the likelihood of the research promoting equal opportunities and resources for underrepresented groups and elevating diverse perspectives.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFeasibility\u003c/b\u003e: Determine the probability of the research's successful execution, considering scientific and financial limitations, as well as logistical arrangements and other potential barriers.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eScoring System and Data Analysis\u003c/h2\u003e\u003cp\u003eThe analysis followed a three-step process:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eScoring\u003c/b\u003e: Responses were numerically coded (High\u0026thinsp;=\u0026thinsp;3, Moderate\u0026thinsp;=\u0026thinsp;2, Low\u0026thinsp;=\u0026thinsp;1), with \"do not know\" and blank responses excluded.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntermediate Scores\u003c/b\u003e: For each research area, an intermediate score for each criterion was calculated as the ratio between the sum of the assigned scores over the number of scorers.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eOverall Scores\u003c/b\u003e: Each research area was assigned an overall score, derived as the mean of the three intermediate scores. These were then rescaled to a 0\u0026ndash;1 scale, with 1 representing the maximum possible score.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eGenerally, only research areas with a score of \u0026ge;\u0026thinsp;0.70 were retained for the second Delphi survey to ensure a balanced representation across domains and to maintain a manageable number of research areas for the second Delphi round. Additionally, a comparative analysis was conducted between responses from participants in LIC/LMIC countries and those from higher-income countries; redundant or out-of-scope areas were excluded, and additional proposals from participants were incorporated.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStep 4: Second Delphi Survey\u003c/h3\u003e\n\u003cp\u003eThe second online Delphi survey was conducted with a questionnaire open from 11 November to 10 December 2024, administered via the WHO DataForm online survey platform. This round aimed to prioritize the shortlisted areas, allowing for more in-depth consideration of their relative importance. WHO invited the same cohort as in the first Delphi survey except for two individuals who withdrew for personal reasons. The 149 participants were instructed to allocate a total of 26 tokens across 34 predefined research areas to generate a prioritized shortlist. They could assign between 0 and 3 tokens per area and were required to allocate at least five tokens of the three framework domains, thereby ensuring balanced representation. All 34 research areas were displayed on a single page, grouped by domain but ordered randomly within each group, with toggle bars facilitating token distribution. Due to the constraint of only 26 tokens \u0026ndash; about 75% of the total \u0026ndash; participants had to make deliberate and strategic prioritization decisions. This token allocation method was chosen for its simplicity and effectiveness in clearly differentiating the relative importance of each research area.\u003c/p\u003e\n\u003ch3\u003eScoring System and Data Analysis\u003c/h3\u003e\n\u003cp\u003eThe analysis began by calculating the total number of tokens allocated to each research area and domain. The areas were ranked from highest (98 tokens) to lowest (29 tokens), with scores expressed as a percentage of the maximum possible (252 tokens). The percentages ranged from 38.9% (\"Identify, develop, and evaluate strategies to institutionalize evidence\") to 11.5% (\"Improve criteria and processes for setting research priorities in /EIP\"). Based on these results, the 34 areas were grouped into four categories: Group A (the top 10 prioritized research areas), Group B (11th -15th ), Group C (16th -20th ), and Group 0 (lowest-ranked research areas; 21st -34th ).\u003c/p\u003e\u003cp\u003eTo address the overrepresentation of academia/research sector respondents (75%), a re-weighting analysis was conducted, stratifying responses by sector and reclassifying areas into A, B, C, or 0 based on their frequency in each subgroup (Supplementary File 5).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStep 5: Second Expert Consultation\u003c/h2\u003e\u003cp\u003eThe final consultation took place virtually on 26 February 2025. The objective of the consultation was to validate the final version of the GRA and to gather expert input on dissemination and implementation strategies. The session was designed to be highly interactive, incorporating live discussions, collaborative whiteboards, and surveys to promote meaningful engagement.\u003c/p\u003e\u003cp\u003eTo validate the final version, participants voted between a concise list of 11 priority areas and an expanded list of 19. The consultation also addressed practical considerations for dissemination and implementation.\u003c/p\u003e\u003cp\u003eAdditionally, between 2023 and 2025, WHO project team engaged informally with research funders to raise awareness, align strategies, and gather feedback on the GRA for KT/EIP, fostering coordination without involving them directly in the formal prioritization process.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eA total of 202 applications were received, of which 151 experts met the eligibility criteria and were invited to participate in the consultative process. Of these, 131 individuals participated in at least one consultation or Delphi survey. Among these, 92 identified as female and 39 as male. Geographically, respondents originated from the African Region (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), the Americas (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), the Eastern Mediterranean Region (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), the European Region (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), the South-East Asia Region (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and the Western Pacific Region (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Additionally, based on the income level of their country of origin, 77 respondents were from high-income countries, 22 from upper-middle-income countries, 23 from lower-middle-income countries, and 9 from low-income countries (Supplementary File 6).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eFirst Expert Consultation\u003c/h2\u003e\u003cp\u003eThe initial expert consultation included 112 participants and resulted in the reduction of an initial list of 120 research areas to a refined list of 50. This new list served as the basis for the first Delphi survey (Supplementary File 4a). Demographic characteristics of the consultation participants are presented in Supplementary File 6.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eFirst Delphi Survey\u003c/h2\u003e\u003cp\u003eA total of 83/151 individuals submitted questionnaires, representing a 55% response rate. Of these respondents, 57 (69%) were women. The participant pool spanned 29 countries, encompassing all WHO regions and World Bank income levels, with 21 respondents (25%) based in low- or middle-income countries. Most respondents were affiliated with academia/research (70%). Demographic characteristics are presented in Supplementary File 6.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eResearch Area Prioritization\u003c/h2\u003e\u003cp\u003eInitially, 35 out of 50 research areas scored\u0026thinsp;\u0026ge;\u0026thinsp;0.70 and were retained. However, comparative analysis revealed that participants from LIC/LMIC countries generally rated and prioritized aspects related to engagement processes, contextual factors, and intersectionality higher, while assigning lower importance to issues of structural inequalities and decolonization than high-income country respondents. Therefore, several areas previously excluded due to lower scores were reintroduced based on their relevance in LIC and LMIC contexts. Additionally, 41 new proposals were incorporated: one as a new research area, 24 as sub-areas under existing research areas, and 16 were excluded due to redundancy or misalignment with the study scope.\u003c/p\u003e\u003cp\u003eFifteen participants provided qualitative feedback, suggesting refinements to wording and structure, and identifying potential overlaps. These comments were reviewed by the project team and used to revise and harmonize the research areas. As a result of this comprehensive process, 34 research areas were selected to be carried over to the second Delphi survey (Supplementary File 5).\u003c/p\u003e\u003cp\u003eFinally, to address thematic overlaps and ensure balanced representation across domains, two framework domains were merged into a single domain: \u0026ldquo;Methods, Standards, and Measurements\u0026rdquo; and \u0026ldquo;Theories and Frameworks\u0026rdquo;. The final three domains were distributed as follows: \u0026ldquo;Interventions\u0026rdquo; with 13 areas, \u0026ldquo;Barriers, Facilitators, and Opportunities\u0026rdquo; with 8 areas, and \u0026ldquo;Methods, Standards, Measurements, Theories, and Frameworks\u0026rdquo; comprising 13 areas (Box 2).\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1765544154.png\"\u003e\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSecond Delphi Survey\u003c/h2\u003e\u003cp\u003eA total of 84/149 individuals submitted questionnaires, representing a 56% response rate.\u003c/p\u003e\u003cp\u003eOf these respondents, 57 (68%) were women. Respondents were based in 29 countries, encompassing all WHO regions and World Bank income levels, with 24 respondents (29%, versus 25% in the first survey) based in low- or middle-income countries. Most respondents were affiliated with academia/research (75%, versus 70% in the first survey). Demographic characteristics are presented in Supplementary File 6.\u003c/p\u003e\u003cp\u003eAdditionally, the chi-square test was performed to compare gender, geographic region, country income level, and work sector between participants of the two Delphi surveys. The results indicated no statistically significant differences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of participants in the two Delphi Surveys\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDelphi 1\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDelphi 2\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57 (68.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (67.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26 (31.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGeography*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31 (37.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEUR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (15.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (19.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eCountry income level**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46 (55.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (56.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUMIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (15.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (18.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eParticipants\u0026rsquo; sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademia/research\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58 (69.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Governmental Organization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnited Nations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther international organization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunder***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*AFR\u0026thinsp;=\u0026thinsp;WHO African Region; AMR\u0026thinsp;=\u0026thinsp;WHO Region of the Americas; EMR\u0026thinsp;=\u0026thinsp;WHO Eastern Mediterranean Region; EUR\u0026thinsp;=\u0026thinsp;WHO European Region; SEAR\u0026thinsp;=\u0026thinsp;WHO South-East Asia Region; WPR\u0026thinsp;=\u0026thinsp;WHO Western Pacific Region.\u003c/p\u003e\u003cp\u003e**HIC\u0026thinsp;=\u0026thinsp;High-income country; UMIC\u0026thinsp;=\u0026thinsp;Upper-middle income country; LMIC\u0026thinsp;=\u0026thinsp;Lower-middle income country; LIC\u0026thinsp;=\u0026thinsp;Low-income country.\u003c/p\u003e\u003cp\u003e***A practitioner who declared no conflict of interest\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eResearch Area Prioritization\u003c/span\u003e\u003c/p\u003e\u003cp\u003eAfter the refinement process (see Methods section above), the research areas were distributed across the four groups as follows: 11 research areas in Group A, 4 in Group B, and 5 in Group C, with 14 areas excluded (Group 0). In addition, two thematically overlapping research areas were merged into one: \u0026ldquo;Explore engagement strategies for evidence uptake by decision-makers\u0026rdquo; and \u0026ldquo;Understand engagement processes between evidence generators, intermediaries, and users, including co-creation and co-production\u0026rdquo; were combined into a single area titled \u0026ldquo;Explore engagement processes between evidence generators, intermediaries, and users, including co-creation and co-production, for evidence uptake by decision-makers.\u0026rdquo; This consolidation resulted in a final pool of 19 prioritized research areas (cf. Supplementary File 5 for details).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the 19 research priorities, organized across the three thematic domains. The 'Rank' column on the left displays the overall prioritization from highest to lowest. The final three columns provide a breakdown of rankings within each individual domain.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRanked order of 19 research areas constituting the GRA\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResearch area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInterventions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBarriers, facilitators, and opportunities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMethods, standards, measurements, theories, and frameworks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIdentify, develop, and assess strategies and approaches to institutionalize evidence production, translation, and use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvaluate the impacts of KT/EIP products and interventions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExplore engagement processes between evidence generators, intermediaries, and users, including co-creation and co-production, for evidence uptake by decision-makers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExamine contextual factors and their role in research uptake, implementation, and scaling-up of KT/EIP approaches at different levels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderstand, develop, and assess strategies for translating evidence during public health emergencies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnalyze factors determining the engagement of decision-makers in evidence uptake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntegrate a Diversity, Equity, and Inclusion (DEI) lens into KT/EIP activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExplore innovative and evolving technologies that support KT/EIP, such as Artificial Intelligence (AI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderstand and develop approaches for policy learning and transfer of successful KT/EIP experiences between countries and sectors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEngage individuals with lived experiences in knowledge generation and translation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInvestigate factors that affect the relationship between evidence generators, intermediaries, users, and citizens, including co-creation and co-production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInvestigate methods, theories, and frameworks for decolonizing knowledge, research methods, and measures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssess capacity-strengthening interventions, including their adaptation and scale-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIdentify, develop, and assess strategies for scaling-up KT/EIP interventions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIdentify, develop, and assess evidence-to-policy engagement mechanisms and best practices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImprove the communication of research findings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExplore methods for intersecting KT/EIP with basic science, clinical practice, public health, political science, and science diplomacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExplore methods for integrating and harmonizing local and global knowledge in the KT/EIP process, and for improving evidence contextualization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDevelop typologies, metrics, and standardizations for KT/EIP approaches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSecond Expert Consultation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe final expert consultation included 69 participants. Among the attendees, 57% identified primarily as evidence generators, 39% as evidence intermediaries or brokers, and 4% as evidence users. Demographic characteristics are presented in Supplementary File 6.\u003c/p\u003e\u003cp\u003eAdditionally, a chi-square test was performed. There were no statistically significant differences in gender between the two consultations, but statistically significant differences were observed in both geography and country income level (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of participants in the two Expert Consultation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsultation 1\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConsultation 2\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81 (72.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (63.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31 (27.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (34.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eGeography*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (17.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41 (36.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (26.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEUR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (23.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (17.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eCountry income level**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67 (59.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (49.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUMIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (16.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (21.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (16.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (23.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (8.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*AFR\u0026thinsp;=\u0026thinsp;WHO African Region; AMR\u0026thinsp;=\u0026thinsp;WHO Region of the Americas; EMR\u0026thinsp;=\u0026thinsp;WHO Eastern Mediterranean Region; EUR\u0026thinsp;=\u0026thinsp;WHO European Region; SEAR\u0026thinsp;=\u0026thinsp;WHO South-East Asia Region; WPR\u0026thinsp;=\u0026thinsp;WHO Western Pacific Region.\u003c/p\u003e\u003cp\u003e**HIC\u0026thinsp;=\u0026thinsp;High-income country; UMIC\u0026thinsp;=\u0026thinsp;Upper-middle income country; LMIC\u0026thinsp;=\u0026thinsp;Lower-middle income country; LIC\u0026thinsp;=\u0026thinsp;Low-income country.\u003c/p\u003e\u003cp\u003eDuring the consultation the results of Delphi Survey 2 were presented to validate the agenda, followed by a poll to determine preferences between a shorter list of 11 research areas from Category A and an expanded list of 19 priority areas (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A slight preference emerged for the expanded list (53%) due to its perceived expediency, balance, comprehensiveness, and inclusion of foundational methods and standards. The shorter list was acknowledged as potentially more suitable for simplicity and ease of use for specific audiences. Despite this divergence in opinion, the consultation broadly affirmed the three-domain framework of the GRA (Box 2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRoadmap for Dissemination and Implementation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants emphasized that the GRA should serve as a reference for regional, national and context-specific research agendas. Dissemination should be tailored to diverse audiences and utilize multiple formats, including podcasts, public lectures, training materials, and translations into UN languages. Ongoing engagement with key interest-holders \u0026ndash; including funders, research networks, and community actors \u0026ndash; was considered essential. Regional and national involvement, along with organizational champions, was highlighted as critical for success. Incorporating the agenda into academic curricula, especially at graduate (masters and doctoral) levels, was seen to promote sustainability. Funding remains a challenge; participants emphasized aligning with existing mechanisms, exploring co-funding models, and supporting early-career researchers. Regular monitoring and evaluation of the agenda\u0026rsquo;s implementation were deemed necessary to ensure progress. Most participants (84%) planned to use the agenda to promote KT/EIP, while 72% intended to apply it to their research strategies. The final evaluation showed high satisfaction (average rating 4.4/5) with the outcome of the agenda-setting process, while 87.5% expressed interest in continued involvement.\u003c/p\u003e\u003cp\u003eThe GRA on KT/EIP was officially launched on 15 May 2025 during a global webinar, attended by over 800 participants.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRepresenting WHO\u0026rsquo;s first global and multisectoral agenda for research priorities in KT and EIP, this study identified 19 priority research areas aimed at addressing the most critical gaps in the field.\u003c/p\u003e\u003cp\u003eThe structured, multi-phased approach of this GRA provides a solid foundation for future research initiatives.\u003c/p\u003e\u003cp\u003eThe initial phase involved synthesizing existing evidence on KT strategies, priorities and funding mechanisms (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), alongside a living evidence map focused on EIP (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This was further complemented by primary data collected through a global survey. Together, these inputs informed the development of a conceptual framework comprising four domains and an initial list of 120 priority areas. This framework subsequently guided the prioritization process, the organization of priorities, and the dissemination of findings.\u003c/p\u003e\u003cp\u003eThe process to develop the research agenda was evidence-informed, comprehensive, and was strengthened by being expanded and refined by thematic experts \u0026ndash; an approach that underpins its credibility and reliability (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This methodology aligns with existing initiatives that emphasize similar key elements: starting from a foundation of pre-existing, relevant evidence (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), applying a geographical lens that may be global, regional, or local (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), and ensuring the meaningful inclusion of diverse interest-holders (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Furthermore, the use of varied methodological tools \u0026ndash; such as the modified Delphi techniques employed in this study (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) \u0026ndash; alongside guiding frameworks for both processes and outcomes (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), further reinforce the robustness and rigor of the approach.\u003c/p\u003e\u003cp\u003eA closer look at the top ten priority research areas identified in the GRA shows that four of the top five prioritized research areas correspond to the KT/EIP interventions domain (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These include the identification and evaluation of strategies to institutionalize KT, as well as assessing the impact of KT activities and products. The importance of exploring engagement and co-creation processes among evidence generators, intermediaries, and users is also emphasized, as these facilitate the integration of evidence into decision-making. Additionally, the development and evaluation of targeted strategies for translating evidence during public health emergencies is highlighted. In this context, future research should also consider the sustainability and scalability of KT interventions (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), and conduct rigorous evaluation studies to assess the effectiveness of different KT strategies in diverse contexts and populations (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Such studies should also address the costs, benefits, and unintended consequences of KT interventions, such as co-creation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), using mixed-methods approaches to capture both quantitative and qualitative outcomes. Strategies for KT/EIP in emergency contexts have become a major priority in recent years due to the COVID-19 pandemic and other crises such as climate change. Advances in this area include the use of artificial intelligence (AI) and the development of rapid and living evidence syntheses (\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFive of the top ten high-priority research areas correspond to Domain 2 (Barriers, Facilitators, and Opportunities). These include examining contextual factors and their influence on the uptake, implementation, and scaling up of KT/EIP approaches at various levels; analyzing the determinants that influence decision-makers' engagement in evidence utilization; integrating a DEI perspective into KT and EIP activities; developing approaches for policy learning and the transfer of successful KT/EIP experiences across countries and sectors; and actively engaging individuals with lived experiences in knowledge generation and translation. Incorporating methods to explore the lived experiences of actors involved in KT processes can offer valuable insights into the barriers and facilitators of evidence uptake (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Moreover, it is essential that prioritization methods consider individual patient needs, values, and experiences alongside population-level concerns and issues related to the health system (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith respect to Domain 3 (Methods, standards, etc.), only one research area is represented among the top ten priorities of the GRA. This area emphasizes the exploration of innovative and evolving technologies that support KT/EIP, such as AI. The potential use of AI to inform more equitable and evidence-based policies underscores the need for robust infrastructures and regulatory frameworks to support its responsible application (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). For example, Ramezani et al. propose a framework for evaluating health equity using machine learning (ML) and social determinants: ML algorithms are used to identify key factors influencing life expectancy and health inequalities, and implementing such frameworks could support the prioritization and mitigation of health inequities in policy-making (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe recent establishment of the Evidence Synthesis Infrastructure Collaboration (ESIC) (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), as outlined in the Cape Town Consensus (2025) (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), aims to build a user-centered, distributed infrastructure that responsibly leverages AI to support decision-makers, promote equity, and prioritize the needs of the Global South. The integration of AI marks a critical inflection point in KT/EIP, with living evidence syntheses (LES) (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e), emerging as a promising strategy for delivering timely, contextualized evidence \u0026ndash; particularly in rapidly evolving fields, as demonstrated by the National Clinical Evidence Team (NCET) during COVID-19 (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor this research agenda, the diverse composition of participants was essential to ensuring the relevance and applicability of the identified research priorities across a wide range of contexts, particularly in LMICs where KT challenges and priorities often differ significantly (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). As the priority-setting exercise demonstrated, participants from LMICs placed greater emphasis on participatory processes, contextual factors, and intersectionality in KT, while structural inequalities and decolonization received comparatively lower prioritization. This disparity could partly be explained by the relatively smaller sample size of colleagues based in LMICs, but further research is needed to explore the underlying reasons more systematically.\u003c/p\u003e\u003cp\u003eMoreover, the predominance of experts affiliated with academia and research institutions highlights the valuable contribution of scholarly perspectives but also underscores the need for broader engagement from other sectors \u0026ndash; including government agencies, non-governmental organizations, and the private sector \u0026ndash; to support a more holistic and multidisciplinary research agenda. In recent years, co-participation and co-production have gained traction in policy-oriented KT processes, reinforcing the importance of inclusive approaches (\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEngaging a diverse group of interest-holders across sectors, WHO regions, and income levels was essential to ensure that the research priorities reflect global needs and are applicable across sectors and contexts (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond demonstrating methodological rigor, the GRA provides a strategic framework to guide investments and capacity-building efforts, fostering synergies, and reducing research duplication between sectors, regions and countries.\u003c/p\u003e\u003cp\u003eSeveral initiatives currently aim to promote EIDM, emphasizing the collaborative and interdisciplinary exchange of knowledge in policy development (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The COVID-19 pandemic accelerated these efforts within the health sector (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). However, significant challenges persist in the public policy sphere, particularly the scarcity of tools to assess policy effectiveness, especially in LMICs, as noted by the International Initiative for Impact Evaluation (3ie) (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e) and the UK Foreign, Commonwealth and Development Office through the Research Capacity Consortium (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobally, the WHO-led Global Coalition for Evidence (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), launched at the Global Evidence Summit in 2024, aims to strengthen the global evidence ecosystem and foster collaboration to institutionalize evidence-based decision-making. Aligned with these initiatives is the ESIC, already mentioned. The integration of AI represents a critical inflection point, with LES (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) emerging as an effective strategy for providing timely, contextualized evidence\u0026mdash;particularly in rapidly evolving fields, as demonstrated by the NCET and ALEC during COVID-19(\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFunding organizations such as the Impact Funders Forum, facilitated by Pew Charitable Trusts (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e), emphasize participatory research to enhance the relevance and utility of research outcomes.\u003c/p\u003e\u003cp\u003eFinally, to contribute to the implementation of the WHO GRA on KT and EIP, the Special Programme for Research and Training in Tropical Diseases (TDR) adopted and implemented a Call for Applications 2025\u0026ndash;2026 with a focus on \"One Health\" and targeted at LMIC.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eOverall, the development of the GRA was reinforced by a broad and inclusive participatory process, encompassing both expert consultations and Delphi rounds. The Delphi methodology played a critical role in establishing and ranking research priorities. Although the composition of experts was balanced across the two Delphi rounds, some imbalances were observed within each round. For example, despite broad global representation, certain regions and income levels were more prominently represented than others, which may have influenced the prioritization outcomes.\u003c/p\u003e\u003cp\u003eHowever, although various approaches to research prioritization exist, there is no single best method (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The Delphi method, while widely used, introduces a degree of subjectivity (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), and one potential limitation is the risk of selection bias. In this priority-setting exercise, there were no statistically significant differences between participants in both rounds, which were central to the prioritization and scoring process. In addition, the overrepresentation of participants from the academic and research sector was adjusted during the analysis to mitigate potential bias. The type of Delphi method employed can also influence the outcomes (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Accordingly, the second round was refined based on lessons learned from the limitations identified in the first. By requiring experts to allocate a fixed number of tokens in the second round, we encouraged more deliberate prioritization, reducing the tendency for participants to default to moderate or non-committal choices.\u003c/p\u003e\u003cp\u003eNonetheless, significant differences were found between participants in the two expert consultations in terms of geographical origin and country income level. These disparities may have influenced individual responses, shaped by individual biases or pre-existing priorities. In the final consultation, participants from LMICs represented only 29% of the total. To address this underrepresentation, selected priorities raised by LMIC participants were considered and incorporated into the final agenda. This inclusive and participatory approach is supported by existing literature on research prioritization (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e), which also highlights the value of integrating lived experiences from those directly involved in KT processes (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study contributes to the KT/EIP field by proposing a prioritized research agenda designed to guide future research efforts and inform policy decisions. The agenda represents a crucial resource for researchers, policy-makers, and funders, and needs to be complemented by sustained engagement with interest-holders to maximize its impact. Future research should focus on validating and refining this agenda, promoting a holistic approach to KT/EIP, and ensuring its relevance, utility, and effective implementation across various settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConsent Statement\u003c/p\u003e\n\u003cp\u003eThis study involved a voluntary online survey to gather input for a research prioritization exercise on issues in the public domain. Informed consent was obtained from all survey participants; by participating, respondents explicitly agreed to the statement: “I consent to my anonymized responses being used for research and/or publication purposes by the World Health Organization.” The WHO Ethics Review Committee determined that the study was exempt from formal ethical review (Protocol ID: ERC.0004060). All external contributors to the consultations and Delphi exercise submitted declarations of interest, which were assessed for potential conflicts; none were deemed significant.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe priority-setting activities were conducted in consultation with the WHO\u0026apos;s Ethics Review Committee (ERC), which confirmed that ethical approval was not required for this type of process involving consultations and expert meetings. The record of this consultation is ERC.004060.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eDisclaimer\u003c/h2\u003e\n\u003cp\u003eThe authors alone are responsible for the views expressed in this paper and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eTK, BK and AB, AM, TS, RFT, and DL conceptualized and coordinated the research prioritization process, developed the protocol, and oversaw process implementation. EC, BK, and TK drafted the initial manuscript, while all authors (EC, BK, AB, AM, TS, RFT, DL and TK) made significant contributions to the subsequent reviews and read and approved the final manuscript. TK was responsible for submitting the manuscript for publication.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank all participants of the consultations and surveys for their continued engagement, effort, and invaluable input. We are also grateful to Kathryn Oliver and the Pan-African Collective for Evidence for conducting the scoping review and developing the evidence gap map. Finally, we extend our sincere thanks to Mareike G\u0026uuml;nther, Stefano Burzo, Sarah Shanks, Marcela Velez, Davi Romao, as well as all Advisory Committee Members (listed in Supplementary File 1), for their crucial support in the development and implementation of the Delphi surveys and online consultations.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data supporting the conclusions of this article are in the Supplementary Files. Raw datasets are available in anonymized form from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. (2004). World report on knowledge for better health: strengthening health systems. World Health Organization.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCurran JA, Grimshaw JM, Hayden JA, Campbell B. Knowledge translation research: the science of moving research into policy and practice. 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Health affairs. 2013;32(2):223\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Code of Conduct for WHO experts can be accessed through the WHO website (last accessed 11 June 2025): \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/m/item/declaration-of-interests---annex-b\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/m/item/declaration-of-interests---annex-b\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Research Priority-setting, Agenda setting, Policy-Making, Knowledge Translation, Evidence informed policy, Delphi technique, Health policy and systems research","lastPublishedDoi":"10.21203/rs.3.rs-8056056/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8056056/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe effective translation of evidence into policy requires strategic engagement among interest-holders to identify current knowledge gaps, align funding, and minimize research duplication. This study outlines the methods and results of a multi-stage process to develop WHO\u0026rsquo;s first Global Research Agenda (GRA) on Knowledge Translation and Evidence-informed Policy-making (KT/EIP), aimed at improving research efficiency, guiding funding, increasing evidence use, fostering collaboration, and raising awareness of KT research.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eFrom October 2023 to March 2025, a structured five-step approach was undertaken, starting with synthesizing existing evidence on KT strategies and priorities and complemented by primary data from a global survey. These inputs were used to develop a conceptual framework to organize research priority areas. This framework guided a global consultative process, which engaged diverse interest-holders through online consultations and Delphi surveys to jointly identify research gaps, opportunities, and priority areas for inclusion in the final research agenda.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe initial step of evidence synthesis identified 120 research areas. Through the global consultative process, these were refined to 19 priority research areas organized into three domains: 1) Research on KT/EIP interventions, 2) Research on barriers, facilitators, and opportunities for KT/EIP, and 3) Research on KT/EIP methods, standards, measurement, theories, and frameworks. Specific research areas include strategies to institutionalize KT, targeted approaches for public health emergencies, contextual factors influencing KT/EIP uptake, and the exploration of innovative technologies like Artificial Intelligence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study proposes a prioritized research agenda to guide future KT/EIP research and inform funding decisions. This resource for researchers, policy-makers, and funders requires sustained engagement with interest-holders to maximize its impact. Future research should validate and refine this agenda, and ensure relevance, utility, and effective implementation across diverse settings.\u003c/p\u003e","manuscriptTitle":"Advancing Evidence-Informed Policy: Outcomes of the Global Research Agenda on Knowledge Translation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 12:56:30","doi":"10.21203/rs.3.rs-8056056/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T10:30:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T12:30:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214746996160117359906914764410595236535","date":"2026-01-08T15:24:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T10:48:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227127131530484289759283565147990890291","date":"2026-01-06T15:03:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74645061252267439364764227547278564903","date":"2025-12-18T07:35:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230007696397359283647233045830922879283","date":"2025-11-21T14:58:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-21T14:40:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-13T13:49:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T13:47:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Health Research Policy and Systems","date":"2025-11-07T10:22:29+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":"e8abcddf-fa28-4359-b6c8-ef8f5a62efc0","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T13:53:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 12:56:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8056056","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8056056","identity":"rs-8056056","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}