{"paper_id":"224bb84b-0c45-4ef7-b2d1-31c86dc63ef7","body_text":"Structural Barriers to Knowledge Co-production in the Context of Public Health Crisis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Structural Barriers to Knowledge Co-production in the Context of Public Health Crisis Ji Eun Park, Myounghee Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9465628/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Knowledge translation (KT) is widely recognized as essential for evidence-informed policymaking, yet the research-policy gap persists. Co-production, namely collaborative knowledge production among researchers, policymakers, and practitioners, has emerged as a promising approach to bridge this gap. However, existing literature has focused largely on articulating co-production's principles and anticipated benefits, while empirical investigations into why it fails in practice remain limited. We conducted a qualitative study using semi-structured in-depth interviews with 15 participants, including university-based researchers (n = 10) and government-funded institute researchers (n = 5) with direct experience in Korea's COVID-19 research response. Data were analyzed using framework analysis following Spencer's five-stage approach, informed by the Knowledge-to-Action model across four KT stages: knowledge production, exchange, utilization, and conversion to knowledge production. Barriers were identified across all four KT stages, reflecting five interrelated structural mechanisms: epistemological incommensurability and incomplete boundary work; institutional fragmentation and misaligned incentives; the dominance of biomedical epistemology and the systematic exclusion of uncomfortable knowledge; bureaucratic expert dependency and path-dependent decision-making; and the absence of equity and power redistribution. These mechanisms formed a self-reinforcing cycle that systematically foreclosed the preconditions for co-production, not as isolated failures but as structurally produced outcomes. Barriers to co-production are structural rather than communicative in nature. Addressing them requires concrete institutional responses: restructuring research funding to mandate interdisciplinary participation, reforming advisory committee composition and deliberative processes to enable substantive non-biomedical input, and establishing permanent knowledge brokering infrastructure rather than project-based arrangements. Co-production will remain aspirational without systemic interventions that realign institutional incentives, redistribute epistemic authority, and create sustained platforms for researcher-policymaker-public collaboration. Knowledge Translation Knowledge Production Co-production COVID-19 Evidence-Informed policymaking Background Knowledge translation (KT) has been recognized as an essential mechanism for linking research evidence to healthcare policy and practice, and is considered to provide a theoretical basis for achieving evidence-informed policymaking[ 1 – 3 ]. However, despite various KT strategies, the gap between research and policy persists[ 4 , 5 ]. Policymakers often do not use research evidence, and interactions between researchers and policymakers frequently remain at a formal level[ 6 , 7 ]. Traditional KT discourse has focused primarily on how to effectively transfer and facilitate the uptake of already-produced knowledge among policymakers[ 8 , 9 ]. This approach, however, has paid relatively little attention to what knowledge is produced in the first place and who participates in the knowledge production process[ 10 ]. From a knowledge production perspective, if policy contexts and field needs are not reflected at the production stage, subsequent efforts in dissemination and uptake inevitably encounter significant limitations[ 11 , 12 ]. The success of KT is thus shaped considerably by how knowledge is produced, even before the uptake stage. This topic is important in public health crises, where timely and policy-relevant knowledge is essential. These observations have led to growing interest in co-production[ 13 , 14 ]. Co-production moves beyond the linear model in which researchers produce knowledge independently and then transfer it to policymakers[ 15 ]. Instead, it is an approach in which researchers, policymakers, and field practitioners collaborate throughout the knowledge production process[ 16 , 17 ]. When diverse actors participate from the knowledge production stage, the resulting knowledge is more likely to align with policy contexts and be applicable in practice[ 18 ]. However, existing discussions on co-production have concentrated largely on articulating its principles and anticipated benefits[ 19 , 20 ]. Empirical investigations into why co-production does not function as intended and under what conditions collaborative knowledge production is structurally constrained remain limited[ 21 ]. COVID-19 response of South Korea (hereafter, Korea) provides a relevant case for exploring the structural conditions underlying co-production. Korea possesses well-established public research system and infectious disease response systems, making it difficult to attribute limitations in collaborative knowledge production to insufficient institutional capacity[ 22 , 23 ]. If barriers to KT persisted despite substantial institutional capacity, this suggests that structural factors beyond capacity were at play. In this context, this study examines the barriers that emerged at each stage of knowledge translation, production, exchange, utilization, and conversion to knowledge production, during Korea’s COVID-19 response, and analyzes the structural conditions underlying these barriers. Through this analysis, the study aims to deepen understanding of the preconditions necessary for co-production to function effectively, and to offer directions for research policy in future public health emergency responses. Methods Aim This study examines barriers to KT that emerged during the COVID-19 response, with particular attention to how these barriers impeded co-production among researchers, policymakers, and field practitioners. The analysis focuses on the interplay of power imbalances, institutional structures, and political contexts that structurally blocked collaborative knowledge production. Selection of the Study Area Korea offers a distinctive case for examining why KT barriers persist even when institutional capacity is well established. Unlike settings where resource constraints or underdeveloped research infrastructure might explain co-production difficulties, Korea has made substantial investments in public health research following the 2015 MERS outbreak[ 22 , 24 ]. This paradox, where significant institutional capacity coexists with persistent structural barriers to inclusive knowledge governance, allows for analytical isolation of factors beyond mere capacity deficits. Additionally, Korea’s research policy, where funding has concentrated on healthcare technology development,[ 25 ] provides an opportunity to examine how funding priorities shape knowledge production patterns[ 26 , 27 ]. Findings from this context may inform other countries with similarly structured research systems seeking to understand why collaborative knowledge production remains elusive despite adequate resources and formal mechanisms for stakeholder engagement. Study Design We conducted a qualitative study using semi-structured in-depth interviews to explore factors affecting KT during the COVID-19 response. The interview guide drew on the Knowledge-to-Action framework and was refined through two pilot interviews before data collection. Interview questions were organized around the four stages of KT: knowledge production, knowledge exchange, knowledge utilization, and conversion to knowledge production. Within each stage, participants were asked to reflect on facilitators, barriers, and their own experiences navigating the research-policy interface (Supplementary Material 1). Participants We used purposive sampling to recruit researchers and policy practitioners with experience in planning, conducting, or utilizing COVID-19 research. To capture diverse perspectives, we included participants from government-funded research institutes and universities across disciplines, including public health, epidemiology, sociology, economics, and communication studies. Recruitment began with a researcher who had led multiple government-funded COVID-19 projects, followed by snowball sampling. Of 28 experts contacted, 15 agreed to participate; others declined due to time constraints or concerns about identifiability. Participants were categorized into two groups based on their role in KT: university-based researchers working across public health, health policy, epidemiology, sociology, anthropology, and communication studies (n = 10); and researchers from government-funded research institutes with experience in policy-relevant research and serving as boundary spanners between academic and policy communities (n = 5) (Table 1 ). Table 1 Participant characteristics ID Affiliation Discipline Years of Experience U1 University Psychiatry 15–20 U2 University Sociology 20+ U3 University Anthropology 10–15 U4 University Health Policy 15–20 U5 University Health Service Research 10–15 U6 University Mathematics 20+ U7 University Health Service Research 10–15 U8 University Health Economics 5–10 U9 University Epidemiology 20+ U10 University Communication 10–15 G1 Government-funded Research Institute Economics 5–10 G2 Government-funded Research Institute Health Systems Research 15–20 G3 Government-funded Research Institute Social Welfare 20+ G4 Government-funded Research Institute Community Health 15–20 G5 Government-funded Research Institute Social Epidemiology 10–15 U = University-based researcher; G = Government-funded research institute researcher. Affiliations reflect participants' institutional positions at the time of interview. To protect participant anonymity, specific committee during the COVID-19 period are not disclosed. Years of experience refers to research experience in the relevant discipline. Data Collection Potential participants were identified through online searches of COVID-19 research networks and policy documents, then recruited via snowball sampling. Each was contacted individually by email with a study description and Korean-language interview guide. Interviews were conducted between October 2022 and February 2023. The authors conducted face-to-face interviews at participants’ offices, and online interviews were offered when in-person meetings were not feasible. Each interview lasted 60 to 90 minutes and was audio-recorded with consent. Both interviewers took independent notes during each session, which were shared with the full research team immediately afterward to ensure transparency and support collaborative interpretation. Data Analysis Interview data were analyzed using framework analysis following Spencer's five-stage approach[ 28 ]. This method allows for systematic analysis while remaining open to emergent themes. The Knowledge-to-Action framework informed the initial analytical structure,[ 1 ] but the final four stages (knowledge production, exchange, utilization, and conversion to knowledge production) emerged through iterative deductive-inductive analysis. In the familiarization stage, the first author transcribed all recordings verbatim and read through transcripts repeatedly. During identification, we developed the thematic framework by noting recurring ideas and checking alignment with a priori categories. In indexing, we applied the framework to all transcripts. The charting stage involved extracting coded data into matrices organized by theme and participant. Finally, in mapping and interpretation, we examined relationships between themes and developed explanatory accounts. During disassembling, transcripts were segmented into meaning units ranging from single sentences to short paragraphs, then open-coded. Coding proceeded both inductively and in dialogue with the interview guide structure. The research team shared analytic memos and held regular discussions to refine codes, and discrepancies were resolved through deliberation between the two lead authors. In reassembling, codes were grouped into candidate themes. During interpreting, themes were iteratively reviewed for coherence with the research questions and the broader policy context; disagreements were resolved through discussion until consensus was reached. In the concluding phase, the first author drafted thematic summaries, which the corresponding author reviewed to finalize the analysis. Themes were organized according to the four KT stages, and subtheme frequency was tallied based on meaning units. All data collection and analysis were conducted in Korean. Trustworthiness and Reflexivity Several strategies were employed to enhance rigor. Despite the modest sample size, we sought breadth by including participants with varied institutional roles. Interpretive consistency was supported through iterative cross-checking, peer debriefing, and collaborative discussion throughout the analytic process. As researchers specializing in health and research policy rather than infectious disease, our disciplinary positioning shaped the interview guide and analytic lens. We acknowledge that this background, along with the fact that many participants were affiliated with government-linked institutions, may have oriented perspectives toward pragmatic rather than critical viewpoints. Interviews were conducted and analyzed in Korean, then translated into English during manuscript preparation. Although care was taken to preserve meaning, certain cultural or sociopolitical nuances may have been attenuated. We recognize that some degree of interpretive influence may have been introduced through the translation and analytic process. Ethical Issues This study was approved by an Institutional Review Board (IRB no. blinded for review). Results This study identified barriers across four stages of KT during Korea’s COVID-19 response. These barriers not only hindered KT but also structurally blocked co-production, the collaborative knowledge production among researchers, policymakers, and field partners. These barriers recurred across stages and appeared to reflect underlying structural patterns, which are analyzed in the Discussion. 1. Knowledge Production Stage: Epistemic Exclusion and Structural Precarity At the knowledge production stage, two interrelated barriers emerged. Funding structures concentrated resources in biomedical fields while structurally marginalizing social science disciplines (1.1), and short-term project cycles combined with career pressures eroded researchers’ capacity to sustain inquiry over time (1.2). 1.1 Unequal Research Opportunities Research opportunities in COVID-19 studies were unevenly distributed. Funding concentrated on development of healthcare technology like vaccines, therapeutics, and diagnostic devices, structurally excluding ‘minor disciplines’ such as public health, social sciences, and humanities. \"People in the social sciences or humanities [...] it felt like we were being pushed to the periphery. Looking at the research funding announcements, there was almost nothing we could do.\" (Participant U1, Psychiatry) Most urgent research projects focused on biomedical topics such as clinical characteristics of SARS-CoV2, vaccine efficacy, and therapeutic development. Conversely, research on social and structural issues, such as the impact of quarantine measures on daily life, health inequalities among vulnerable groups, and the breakdown of care systems, was deemed ‘secondary’. \"My research originally focused on the impact of epidemic control policies on caregivers, but I had to include terms like mental ‘health ’ or ‘health disparities ’ in the proposal. That is what it takes to be recognized as healthcare research.\" (Participant U2, Sociology) The disparity in publication speed across disciplines was also a significant issue. Quantitative research in epidemiology was swiftly reflected in policy through rapid publication, whereas qualitative research in sociology or anthropology missed the policy window and was excluded. \"By the time we finish fieldwork and analysis [...] policies are already decided. Our research is only used for post-hoc evaluation.\" (Participant U3, Anthropology) Research opportunities were concentrated among a small number of ‘core’ researchers, and projects were repeatedly assigned to those with prior experience in infectious disease or established networks with the government, making entry even more difficult for early-career researchers or those in peripheral academic fields. 1.2 Threatened Research Continuity In the early stages of the pandemic, researchers’ sense of social responsibility and collective creativity led to innovative responses, such as devising drive-through testing methods. However, from the mid-stage onward, researchers felt pressure to return to their ‘main jobs’ and burdens related to career management, threatening the continuity of their research. These conditions amount to what might be termed structural precarity in knowledge production: a state in which researchers’ capacity to sustain inquiry is chronically undermined not by individual choices but by short-term funding logic, misaligned institutional incentives, and career structures that systematically disincentivize sustained engagement with policy-relevant questions. \"At first, everyone thought it was a national crisis and dedicated themselves. But after a year passed [...] I started wondering how this would be recognized as my research achievement and whether it would help my career pathway.\" (Participant U4, Health Policy) Most COVID-19 research projects were designed as short-term assignments lasting 3-6 months, with no guarantee of securing follow-up funding. This made it impossible to maintain research teams or accumulate long-term data. In fields like public health, where specialized personnel are limited, existing researchers found themselves overwhelmed with work. \"Professors juggle multiple research projects. Dedicating yourself entirely to COVID-19 research just was not realistic.\" (Participant U5, Health Service Research) \"I work at a government -funded research institution with existing responsibilities. COVID-19 was not my primary focus, and I had limited capacity, but I was overwhelmed with COVID-19 research requests.\" (Participant G1, Economics) Tensions persisted between social responsibility and performance management (securing research funding, publication output, teaching and administrative duties). 2. Knowledge Exchange Stage: Severed Mediation and Epistemic Hierarchy The knowledge exchange stage revealed two compounding barriers. Epistemic divides between disciplines and evidence hierarchy disputes blocked meaningful dialogue between researchers and policymakers (2.1), while the structural fragility of mediation channels prevented these divides from being bridged institutionally (2.2). 2.1 Deficient Mutual Understanding There was a lack of mutual understanding between researchers and policymakers, with pronounced differences in perceptions regarding the hierarchy of evidence. Those with conservative perspectives recognized only randomized controlled trials (RCT) and systematic reviews (SR) as rigorous evidence, while those with flexible perspectives argued that diverse forms of evidence should be accepted during public health crises, leading to conflict. \"Policymakers ask, ''Has this been validated by an RCT?'' How can you conduct an RCT during a pandemic [...] We are doing our best with observational studies, modeling, and field evidence.\" (Participant U6, Mathematics) This strict stance on the hierarchy of evidence proved problematic, when evaluating the effectiveness of non-pharmaceutical interventions, such as mask-wearing and social distancing. While conducting RCTs for these interventions is difficult for ethical and practical reasons, observational studies or modeling studies were dismissed as ‘low-level evidence’. Epistemic divides between disciplines were also a significant factor hindering knowledge exchange. Initial ‘soft’ discussions became contentious as knowledge accumulated, with each discipline remaining stuck in a fragmented understanding, much like the parable of the blind men and the elephant. \"Each discipline has its own jargon that feels intuitive internally. The challenge was figuring out how much background to provide when communicating with researchers from other fields.\" (Participant U7, Health Service Research) \"Genuine interdisciplinary dialogue requires acknowledging that your perspective, however valid, does not capture the whole picture.\" (Participant G2, Health System Research) 2.2 Limited Institutionalization of Knowledge Brokering Channels for mediating between researchers and policymakers remained fragile and temporary. Policymakers perceived health as a ‘specialized domain,’ exhibiting a passive attitude characterized by excessive reliance on medical experts and reluctance to voice their own opinions. \"Policymakers often defer on healthcare issues, saying ''that is outside my expertise''. They treat it as a specialized domain and hesitate to question or challenge what medical experts tell them.\" (Participant U7, Health Service Research) Meanwhile, researchers at government-funded research institutes, who possessed dual expertise in both research and policy, endeavored to serve as boundary spanners mediating between academic and policy communities. However, their temporary advisory positions and project-based engagements led to severed connections upon the end of their terms. The absence of regularized exchange channels resulted in only sporadic meetings, and the pattern of exchanges naturally fading away upon the termination of research funding repeated itself. 3. Knowledge Utilization Stage: Exclusion and Instrumentalization At the knowledge utilization stage, barriers operated through both exclusion and instrumentalization. Non-biomedical and field knowledge were excluded within advisory structures (3.1), while decision-making processes remained path-dependent and formalistic rather than evidence-informed (3.2). 3.1 Excluded Knowledge Asymmetric Evidence Selection: Biased Advisory Structure Although the advisory committee was diverse in composition, discussions were dominated by physicians. \"When medical professionals speak at meetings, everyone listens. But when a public health officers speaks, the question ''How does that explain medically?'' comes back. We are constantly placed in a position where we have to justify ourselves.\" (Participant U8, Health Economics) The medical-centric structure was clearly evident at the February 3, 2020 advisory meeting (all physicians) and persisted in subsequent committees: the With COVID Committee (50% physicians out of 18 members) and the Daily Life Recovery Support Committee (75% physicians out of 8 members). Asymmetric Evidence Selection: Silenced Voices from the Field Voices from the field were not reflected until the mid-pandemic period. Opinions from public health centers were excluded from policy discussions, and vulnerable groups, caregivers, women, individuals with severe mental illness, and persons with disabilities, migrants, and the homeless, experienced ongoing invisibilization. \"Outbreaks in long-term care facilities were predictable, but the response was delayed. We kept warning on the ground, but those voices were not heard in policy meetings.\" (Participant G3, Social Welfare) Public health center staff were best positioned to understand the realities on the front lines of disease control, yet their experience and knowledge were scarcely considered in policy meetings. While some center directors attended advisory meetings, their opinions were dismissed as ‘anecdotal evidence’ and had no real impact on policy decisions. Responsibility-Avoiding Evidence Selection Past infectious disease response experiences and overseas policy cases were selectively utilized. Participants noted a tendency to choose proven methods in case of policy failure, which they attributed to a desire to secure a basis for deflecting accountability. . \"Officials always want overseas precedents before committing resources. Trying something untested carries political risk. If it fails and there is no precedent to point to, they are left exposed.\" (Participant G4, Community Health) 3.2 Stagnant Evidence-Informed Decision-Making Culture Evidence-Informed vs. Evidence-Based Policymakers considered themselves ‘evidence-informed’ but in practice remained merely ‘evidence-based’. Evidence-informed policy involves exploring evidence relevant to actual problems in the field and considering social, institutional, and political contexts, yet in reality it manifested as selective evidence utilization at the decision-making moment and a focus on outcomes. For instance, discussions on economic damage, the double burden of care, elderly isolation, and the exhaustion of public health centers were insufficient. Policymakers initially relied heavily on scientific evidence, expecting a quick resolution. When the pandemic extended, they attempted to diversify advisory membership, but the meetings lacked deliberative structure and failed to facilitate genuine consensus among stakeholders with differing interests. Formalistic Advisory Meetings The advisory meetings were described by participants as formalistic. The meetings served to ‘filter’ proposals the government had already decided upon, failing to operate as venues for substantive discussion. Some participants described the advisory meetings as acting as mere rubber stamps. \"Sometimes meeting materials arrived the morning of the meeting itself. How could we possibly review that and offer opinions? It was essentially going to rubber-stamp the government proposal.\" (Participant U9, Epidemiology) 4. Conversion to Knowledge Production Stage: Insufficient Momentum The final stage concerned whether COVID-19 experience could be converted into a foundation for future knowledge production. Although participants recognized the need for systematic documentation (4.1), both internal structural constraints and the rapid decline in external funding momentum prevented this from materializing (4.2). 4.1 Recognition of Need to Systematize COVID-19 Experience Researchers recognized the need to organize COVID-19 experience, and key research topics for future responses to similar public health crises were proposed, including the social gains and losses of non-pharmaceutical interventions, the scale of health damage to vulnerable groups, and mental health service demand by disaster scale. \"We have conducted a massive social experiment during this pandemic. If we do not properly document and analyze it, we will repeat the same mistakes next time.\" (Participant G5, Social Epidemiology) 4.2 Insufficient Momentum for Knowledge Systematization However, the momentum for knowledge systematization was insufficient. Internal factors included a limited pool of researchers, a rigid research funding structure, and an inflexible organizational culture. With the number of infectious disease researchers itself being limited, existing researchers were already overburdened, and the entry of new researchers was constrained by insufficient funding and an unstable research environment. \"Government funding priorities follow what public health and preventive medicine researchers emphasize. If your topic does not fit that mold, you get pushed aside. And when everything else is covered, they suddenly say communication matters.\" (Participant U10, Communication) Externally, as social interest declined sharply, research funding was cut. Starting in the second half of 2021, COVID-19-related research funding began to decrease sharply, and in 2022, almost no new projects were announced. \"Securing the same priority and budget we had during the pandemic is now extremely difficult. Actually, it is nearly impossible.\" (Participant U5, Health Service Research) Discussion This study confirms that KT faced structural barriers throughout all four stages of the COVID-19 response: knowledge production was constrained by funding concentration, exchange was hindered by epistemic divides, utilization was shaped by path-dependent advisory structures, and the conversion to future knowledge production lost momentum as public and institutional attention to the pandemic waned. These barriers were not mere communication issues nor temporary coordination failures, but appeared to reflect deeper patterns involving power imbalances, institutional structures, and political contexts. While structural factors appear dominant in this analysis, individual and contingent factors may also have contributed. Cross-cutting analysis of the barriers identified in the Results reveals five interrelated structural mechanisms across micro (interpersonal interactions), meso (institutional structures), and macro (research policy) levels: (1) epistemological incommensurability and incomplete boundary work; (2) institutional fragmentation and misaligned incentives; (3) the dominance of biomedical epistemology and the production of uncomfortable knowledge; (4) bureaucratic expert dependency and path-dependent decision making; and (5) the absence of equity and power redistribution (Table 2 ). Table 2 Synthesis of Knowledge Translation Barriers and Structural Mechanisms KT Stage Barriers Mechanism Level Knowledge Production • Funding concentrated on biomedical fields • Social sciences structurally excluded • Short-term projects (3–6 months) • Career pressure vs. social responsibility • Biomedical epistemology dominance • Institutional fragmentation Macro Meso Knowledge Exchange • Evidence hierarchy disputes (RCT supremacy) • Epistemic divides between disciplines • Temporary mediation channels • Failed knowledge brokering • Epistemological incommensurability • Institutional fragmentation Micro Meso Knowledge Utilization • Physician-dominated advisory committees • Field voices silenced • Risk-averse evidence selection • Formalistic rubber-stamp meetings • Bureaucratic path dependency • Biomedical epistemology dominance • Absence of equity Macro Meso Conversion to Knowledge Production • Declining social interest • Research funding cuts • Limited researcher pool • Rigid organizational culture • Institutional fragmentation • Biomedical epistemology dominance Meso Macro Levels: Macro = research policy; Meso = institutional structures; Micro = interpersonal interactions. Mechanisms cut across KT stages and are analyzed in detail in the Discussion. Epistemological Incommensurability and Incomplete Boundary Work Difficulties in interdisciplinary communication stemmed largely from epistemological incommensurability rather than communication skills alone. Each discipline possessed distinct knowledge systems, methodological traditions, and validity criteria, and there was no boundary work mechanism to reconcile these differences[ 29 , 30 ]. Differences in perceptions of evidence hierarchies were problematic, as researchers internalized a hierarchy placing randomized controlled trials and systematic reviews at the apex, with qualitative research or field evidence positioned lower down[ 31 ]. This hierarchy posed particular challenges for responding to COVID-19, which was not merely a biomedical problem but a social pandemic that disproportionately affected vulnerable populations, disrupted economic activities, and exposed structural inequalities in care systems[ 32 , 33 ]. Addressing such multidimensional crises requires drawing on diverse forms of evidence beyond clinical trials, including insights from social sciences, community health, and frontline practice[ 34 ]. Public health is inherently interdisciplinary,[ 35 ] but COVID-19 responses tended to overlook this characteristic. Such marginalization of non-biomedical disciplines mirrors patterns seen in the UK, where mathematical modeling took center stage while research on community and welfare inequalities was devalued,[ 36 ] and where excessive reliance on RCTs and modeling excluded qualitative evidence from the field[ 37 ]. Barriers to multidisciplinary research operate complexly across scientific, structural, and interactional dimensions,[ 38 ] with communication issues permeating all these layers[ 39 ]. This epistemological divide has implications beyond academic disagreement; it determines which problems become visible to policymakers and which solutions are deemed legitimate[ 40 ]. When biomedical framing dominates, social determinants of health and structural inequalities remain systematically invisible in policy discussions. Bridging these epistemological divides requires dedicated intermediaries who can translate across disciplinary boundaries. The mediating role of researchers at government-funded research institutes was crucial yet structurally vulnerable. These boundary spanners occupy a unique institutional position, possessing both research expertise and proximity to policy processes, enabling them to translate between academic and policy communities[ 41 ].While they could function as politically astute, pragmatic intermediaries,[ 42 , 43 ] their small numbers and temporary positions prevented the formation of sustainable connections. This signifies the failure to institutionalize the mediation system, leading to inefficiency where relationships must be rebuilt from scratch during each crisis. These structural barriers also affected individual researchers. Researchers’ conflicts of multiple responsibilities were a product of structural pressures. The tension between internal social responsibility (scientific rigor), external social responsibility (solving societal problems), and performance management pressures transformed some researchers from pure scientists into covert issue advocates[ 44 ]. This was less a matter of individual choice than the result of structural pressures created by the correlation between securing research funding and research outcomes[ 45 , 46 ]. Institutional Fragmentation and Misaligned Incentives Institutional structures also systematically hindered co-production. Research funding allocation focused on healthcare technology development, advisory committee composition remained formally diverse, and the academic reward system penalized interdisciplinary research[ 47 , 48 ]. These elements operated in separate institutional silos with conflicting logics: funding agencies prioritized technological innovation, advisory bodies sought rapid expert consensus, and universities rewarded disciplinary specialization. This institutional fragmentation, where different components of the research policy system pursued incompatible goals,[ 49 ] fundamentally blocked the integrated approach required for co-production. The problems with the academic reward system were severe, as researchers perceived interdisciplinary collaborative projects as more difficult to publish than traditional single-discipline papers and as being undervalued in performance evaluations. This functioned as a structural disincentive, weakening researchers’ motivation to participate in co-production[ 48 , 50 – 52 ]. Beyond these individual-level disincentives, institutional procedures created additional barriers to inclusive participation. The ethics review process, for instance, faced a dilemma between speed and participation. The rapid approvals demanded during the pandemic led to the omission of participatory approval processes involving civic partners or vulnerable stakeholders, further undermining the foundations of co-production[ 53 , 54 ]. The temporary and short-term structure of advisory committees made it impossible to build the trust and relationships required for co-production. While co-production demands time and mutual learning,[ 55 ] Korea’s advisory structure consisted of one-off meetings, and relationships naturally dissolved once research funding ended. This reflects a structural problem where co-production is replaced by one-time consultations. The Dominance of Biomedical Epistemology and Uncomfortable Knowledge Research funding concentrated on healthcare technologies systematically excluded social science and public health perspectives, generating what Rayner terms ‘uncomfortable knowledge’, which institutions find difficult to absorb because it challenges prevailing assumptions or threatens established priorities[ 56 ]. Participants explicitly identified research topics that were recognized as important yet systematically left unstudied: care system impacts, health inequalities among vulnerable populations, and non-pharmaceutical intervention effects on daily life. This pattern reflects a broader tendency for institutions to suppress or ignore knowledge that does not align with dominant frameworks, not necessarily through deliberate suppression but through structural priorities that render certain questions invisible[ 56 , 57 , 58 ]. Research on vulnerable populations, care systems, and health equity remained understudied, creating blind spots in pandemic response that shaped which policy options were visible and which were not. The interdisciplinary hierarchy where medicine takes precedence over preventive medicine, and preventive medicine over social sciences, is deeply entrenched in healthcare system,[ 59 , 60 , 61 ] forming a structured hierarchy of knowledge[ 40 ]. Medical dominance, especially physicians, manifested not only in the composition of advisory committees but also functioned as epistemic power, determining which knowledge was deemed ‘scientific’ and ‘reliable’. Medical professionals secured strong social trust due to their accumulated practical expertise in hospitals and laboratories and their position directly handling lives, and trust in physicians acted as a key predictor of epidemic prevention compliance[ 62 , 63 ]. The physician-centered structure excluded field perspectives,[ 64 ] and medical explanations monopolized scientific legitimacy while excluding social, cultural, and ethical contexts[ 65 ]. The voices of medical experts are perceived as scientific and neutral, yet they actually represent specific perspectives[ 66 ]. This has led to narrow problem recognition, reducing epidemic prevention to merely a technical issue of suppressing infectious diseases and causing invisibilization of its comprehensive impacts on daily life and vulnerable groups. Bureaucratic Expert Dependency and Path-Dependent Decision Making Policymakers’ reliance on experts reflected the static culture of bureaucracy. Policymakers defined health as a ‘specialized domain,’ deferred their own judgment, and delegated authority to specific types of experts, physicians in particular. This is a classic pattern of technocracy, reducing policy judgment to technical expertise[ 67 , 68 ]. The problem was that this delegation was selective: medical expertise was embraced, while public health, social science, and field experience expertise were marginalized. This selective delegation can be explained by path dependency, which manifested in two forms, the repetition of past infectious disease response experiences and the context-free transplantation of overseas cases. Such reliance on past experiences and proven methods, combined with political risk aversion, exemplifies increasing returns path dependency[ 69 ]. Comparative cases illustrate the dual nature of this phenomenon. In Norway, repeating past responses secured social trust but reduced opportunities for policy shifts,[ 70 ] while in Korea, adhering to past frameworks hindered creative and structural transformations[ 71 ]. The gap between espoused evidence-informed policy and actual evidence-based practice stemmed from this path-dependent, risk-averse decision-making culture[ 72 , 73 ]. Selecting proven methods provided political cover in case of policy failure, while exploring context-sensitive evidence for emerging issues carried greater uncertainty and thus greater political risk[ 74 , 75 ]. Similarly, the formalistic nature of advisory meetings can be understood as a legitimation strategy[ 76 ]. Convening expert panels provided the appearance of scientific rigor while maintaining bureaucratic control over actual decisions[ 68 ]. This signifies the instrumentalization of science, where scientific knowledge is mobilized not to inform decisions but to justify predetermined policy directions[ 77 , 78 ]. The Absence of Equity and Power Redistribution The equity mandate clarifies the core element overlooked in the Korean case.[ 79 ] Equality grants everyone an equal voice and role, whereas equity involves actively and continuously redistributing resources and power, placing greater weight on the voices of marginalized groups. Korea's COVID-19 advisory structure failed to achieve even equality. While the advisory committee formally included experts from diverse backgrounds, discussions were dominated by physicians' voices, marginalizing perspectives from public health or the social sciences. In the Korean case, all three dimensions of power redistribution were absent. In the material dimension, researchers outside biomedical fields lacked equitable access to funding, institutional resources, and academic prestige. In the cognitive dimension, diverse forms of knowledge, including field experience and social science evidence, were denied equal recognition. In the decision-making dimension, non-biomedical voices held neither veto power nor meaningful representation. These absences were most visible at the front lines. Health center staff, care providers, and vulnerable groups were excluded from policy meetings, their experiential knowledge was dismissed as unscientific, and they were granted no decision-making authority whatsoever. This resonates with the argument by Cameron and Fiolet[ 80 ] that co-production requires not merely broader participation but genuine redistribution of epistemic power. As reported in the Results, the cluster infection cases in long-term care facilities illustrate the cost of such exclusion. Field workers predicted outbreaks, yet their warnings went unheard in policy meetings, responses were delayed, and infections recurred. This pattern parallels the failure to improve long-term care facilities by excluding direct care workers (who provide 90% of care).[ 81 ] Excluding those with the most information is not only an ethical issue but also one of effectiveness. Implications for Co-production Theory and Practice The preceding analysis reveals how epistemological incommensurability, institutional fragmentation, biomedical epistemic dominance, bureaucratic path dependency, and the absence of equity collectively undermined co-production in Korea’s COVID-19 response. These findings offer several contributions to co-production theory and practice. Theoretically, this study makes two contributions to co-production scholarship. First, while existing literature has demonstrated that co-production can increase research impact under favorable conditions,[ 55 ] it has paid insufficient attention to the structural conditions under which co-production is systematically foreclosed before it begins[ 41 , 82 ]. The Korean case reveals that these conditions, equitable power relations, institutional alignment, and epistemic pluralism, are not incidental but structural. This suggests that co-production frameworks must incorporate explicit assessment of these preconditions before implementation. Second, by situating co-production failure within a multi-level framework spanning micro, meso, and macro dimensions, this study moves beyond single-level explanations prevalent in the literature. Korea’s case is theoretically productive precisely because its well-developed institutional capacity rules out resource deficiency as an explanation, isolating structural and epistemic factors as the primary mechanisms. These mechanisms are not Korea-specific. Wherever biomedical epistemology structures funding priorities, advisory roles remain temporary, and equity is treated as optional rather than structural, similar dynamics are likely to emerge regardless of overall institutional capacity. Practically, this study underscores that co-production may require more than improved communication or goodwill alone[ 83 ]. The structural mechanisms identified here, epistemological incommensurability, institutional fragmentation, biomedical epistemic dominance, path dependency, and the absence of equity, are predominantly structural in nature, suggesting that structural responses may be necessary, though not sufficient on their own[ 8 , 15 ]. Policymakers and research institutions seeking to foster co-production must address funding allocation biases, reform advisory committee compositions, and create sustainable platforms for ongoing researcher-policymaker-public engagement rather than ad hoc consultations[ 11 , 84 ]. Specifically, this study points to three directions for policy development, each grounded in the structural mechanisms identified. First, the finding that funding concentration reproduces epistemic exclusion suggests that research governance frameworks may consider treating interdisciplinary mandates not as add-ons but as structural prerequisites, embedded in grant conditions and evaluation criteria from the outset. Second, the failure of boundary spanning under temporary institutional arrangements points to the potential value of permanent, career-protected knowledge brokering roles rather than project-based advisory positions. Third, the gap between formally diverse advisory committees and substantively physician-dominated deliberation indicates that compositional diversity alone may be insufficient; deliberative process reform, including structured mechanisms for non-biomedical input, could be considered to shift epistemic power within advisory bodies. This study has several limitations. First, as a qualitative study in a single country, the findings reflect Korea’s particular context, its healthcare system, bureaucratic culture, and the 2015 MERS experience. Whether these patterns apply elsewhere depends on contextual similarities. However, structural mechanisms such as epistemic hierarchy and institutional fragmentation are not bound by context in the same way that policy outcomes are. Their operation is recognizable across systems with differing healthcare traditions, even where surface-level conditions differ. Although full theoretical saturation was not reached, participants from different institutional backgrounds were raising substantively similar points by the later interviews, suggesting adequate thematic coverage of the core phenomena under investigation. The goal was not statistical representativeness but analytical sufficiency for the research questions posed, consistent with purposive sampling norms in qualitative policy research. Second, this study focused exclusively on researchers, leaving policymakers, frontline health workers, and civil society actors unrepresented. This design choice was deliberate. Researchers are the primary agents of knowledge production, and understanding how structural barriers operate at the production stage required foregrounding their perspectives, particularly those outside clinical medicine. The experiences of policymakers and frontline actors nonetheless remain unexamined. Future research should engage these groups directly to examine how structural exclusion is experienced from the receiving end, and whether the mechanisms identified here are recognized across stakeholder positions. Third, interviews occurred in late 2022 and early 2023 about events from two years prior. Retrospective accounts nonetheless allow participants to reflect on structural patterns that may not have been visible in real time. Fourth, recruitment through networks of funded researchers likely overrepresented those who secured grants while underrepresenting those excluded from pandemic research. The structural barriers described were consistent across participants with varying levels of funding success, suggesting the core findings are not artifacts of this sampling bias. Fifth, our training in health policy rather than clinical medicine oriented our analysis toward structural factors, possibly underemphasizing individual and relational dimensions of co-production failure. The structural perspective developed in this study nonetheless offers a lens that relational accounts tend to overlook. Co-production fails not from poor communication but from structural barriers requiring structural responses, and making that case requires precisely the kind of institutional and epistemic focus adopted in this analysis. Conclusion This study suggests that barriers to co-production lie not in unwillingness to engage but in the absence of conditions that enable genuine collaboration. The five mechanisms identified here, from epistemic hierarchies to limited equity, point to issues unlikely to be resolved through facilitation alone. They suggest a need to reconsider how research is funded, how advisory bodies operate, and how different forms of knowledge are recognized. Without shifts in these structural conditions, co-production risks remaining aspirational. The question may be less about including more voices than about whether those voices influence what gets studied and what gets decided. Abbreviations KT: Knowledge Translation RCT: Randomized Controlled Trials SR: Systematic Reviews Declarations Ethical approval and consent to participate This study received ethical approval from the Institutional Review Board of the National Medical Center (IRB No. NMC-2024-01-0061). Written informed consent was obtained from all participants prior to interview. Consent for publication Not applicable Availability of data and materials The datasets used in this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the National Medical Center Research Fund [grant number I-2024-001]. Authors’ contributions JP contributed to the conceptualization and design of the study, developed the interview guide, conducted and transcribed all interviews, led the data analysis, drafted the manuscript, and acquired the funding that supported this work. MK contributed to the conceptualization and overall direction of the research, provided supervision throughout the study, and critically revised the manuscript for intellectual content. Both authors reviewed and approved the final version. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 03 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Submission checks completed at journal 22 Apr, 2026 First submitted to journal 19 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9465628\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":633628116,\"identity\":\"826c80bc-4fd2-497b-b98d-f5de6ce82f7b\",\"order_by\":0,\"name\":\"Ji Eun Park\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Kyung Hee University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ji\",\"middleName\":\"Eun\",\"lastName\":\"Park\",\"suffix\":\"\"},{\"id\":633628117,\"identity\":\"aa95e20d-ef7b-43cb-a696-2e79a0bdba27\",\"order_by\":1,\"name\":\"Myounghee Kim\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnElEQVRIiWNgGAWjYDACCTaGAwwVMN4BorWcIVULA2MbKVp0Z7clHro57040fwPzsQ8MZ+4R1mJ259iBw7nbnuXOOMCWPIPhRjERWm6kNwC1HM7dwMBjzMDwIYFYLXNI05IGdFgDTMsN4rQkHM45djh3xmG2ZIaEM8RpMf6cU3M4t7+9+TDDh2NEaEEAZiAmScMoGAWjYBSMAtwAALloP/HIdrLQAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"National Medical Center\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Myounghee\",\"middleName\":\"\",\"lastName\":\"Kim\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-20 02:08:26\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9465628/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9465628/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":108458678,\"identity\":\"6959eb94-8c94-44ba-927f-2e1157cff59a\",\"added_by\":\"auto\",\"created_at\":\"2026-05-04 23:42:18\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":393142,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9465628/v1/595ef0d4-6870-400b-9419-df2fbd3718ce.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Structural Barriers to Knowledge Co-production in the Context of Public Health Crisis\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eKnowledge translation (KT) has been recognized as an essential mechanism for linking research evidence to healthcare policy and practice, and is considered to provide a theoretical basis for achieving evidence-informed policymaking[\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. However, despite various KT strategies, the gap between research and policy persists[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Policymakers often do not use research evidence, and interactions between researchers and policymakers frequently remain at a formal level[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTraditional KT discourse has focused primarily on how to effectively transfer and facilitate the uptake of already-produced knowledge among policymakers[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. This approach, however, has paid relatively little attention to what knowledge is produced in the first place and who participates in the knowledge production process[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. From a knowledge production perspective, if policy contexts and field needs are not reflected at the production stage, subsequent efforts in dissemination and uptake inevitably encounter significant limitations[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. The success of KT is thus shaped considerably by how knowledge is produced, even before the uptake stage. This topic is important in public health crises, where timely and policy-relevant knowledge is essential.\\u003c/p\\u003e \\u003cp\\u003eThese observations have led to growing interest in co-production[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Co-production moves beyond the linear model in which researchers produce knowledge independently and then transfer it to policymakers[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Instead, it is an approach in which researchers, policymakers, and field practitioners collaborate throughout the knowledge production process[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. When diverse actors participate from the knowledge production stage, the resulting knowledge is more likely to align with policy contexts and be applicable in practice[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. However, existing discussions on co-production have concentrated largely on articulating its principles and anticipated benefits[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Empirical investigations into why co-production does not function as intended and under what conditions collaborative knowledge production is structurally constrained remain limited[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eCOVID-19 response of South Korea (hereafter, Korea) provides a relevant case for exploring the structural conditions underlying co-production. Korea possesses well-established public research system and infectious disease response systems, making it difficult to attribute limitations in collaborative knowledge production to insufficient institutional capacity[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. If barriers to KT persisted despite substantial institutional capacity, this suggests that structural factors beyond capacity were at play.\\u003c/p\\u003e \\u003cp\\u003eIn this context, this study examines the barriers that emerged at each stage of knowledge translation, production, exchange, utilization, and conversion to knowledge production, during Korea\\u0026rsquo;s COVID-19 response, and analyzes the structural conditions underlying these barriers. Through this analysis, the study aims to deepen understanding of the preconditions necessary for co-production to function effectively, and to offer directions for research policy in future public health emergency responses.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAim\\u003c/h2\\u003e \\u003cp\\u003eThis study examines barriers to KT that emerged during the COVID-19 response, with particular attention to how these barriers impeded co-production among researchers, policymakers, and field practitioners. The analysis focuses on the interplay of power imbalances, institutional structures, and political contexts that structurally blocked collaborative knowledge production.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eSelection of the Study Area\\u003c/h3\\u003e\\n\\u003cp\\u003eKorea offers a distinctive case for examining why KT barriers persist even when institutional capacity is well established. Unlike settings where resource constraints or underdeveloped research infrastructure might explain co-production difficulties, Korea has made substantial investments in public health research following the 2015 MERS outbreak[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. This paradox, where significant institutional capacity coexists with persistent structural barriers to inclusive knowledge governance, allows for analytical isolation of factors beyond mere capacity deficits. Additionally, Korea\\u0026rsquo;s research policy, where funding has concentrated on healthcare technology development,[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e] provides an opportunity to examine how funding priorities shape knowledge production patterns[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFindings from this context may inform other countries with similarly structured research systems seeking to understand why collaborative knowledge production remains elusive despite adequate resources and formal mechanisms for stakeholder engagement.\\u003c/p\\u003e\\n\\u003ch3\\u003eStudy Design\\u003c/h3\\u003e\\n\\u003cp\\u003eWe conducted a qualitative study using semi-structured in-depth interviews to explore factors affecting KT during the COVID-19 response. The interview guide drew on the Knowledge-to-Action framework and was refined through two pilot interviews before data collection. Interview questions were organized around the four stages of KT: knowledge production, knowledge exchange, knowledge utilization, and conversion to knowledge production. Within each stage, participants were asked to reflect on facilitators, barriers, and their own experiences navigating the research-policy interface (Supplementary Material 1).\\u003c/p\\u003e\\n\\u003ch3\\u003eParticipants\\u003c/h3\\u003e\\n\\u003cp\\u003eWe used purposive sampling to recruit researchers and policy practitioners with experience in planning, conducting, or utilizing COVID-19 research. To capture diverse perspectives, we included participants from government-funded research institutes and universities across disciplines, including public health, epidemiology, sociology, economics, and communication studies.\\u003c/p\\u003e \\u003cp\\u003eRecruitment began with a researcher who had led multiple government-funded COVID-19 projects, followed by snowball sampling. Of 28 experts contacted, 15 agreed to participate; others declined due to time constraints or concerns about identifiability. Participants were categorized into two groups based on their role in KT: university-based researchers working across public health, health policy, epidemiology, sociology, anthropology, and communication studies (n\\u0026thinsp;=\\u0026thinsp;10); and researchers from government-funded research institutes with experience in policy-relevant research and serving as boundary spanners between academic and policy communities (n\\u0026thinsp;=\\u0026thinsp;5) (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\\u003eParticipant characteristics\\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=\\\"left\\\" 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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eID\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAffiliation\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDiscipline\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYears of Experience\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eU1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUniversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePsychiatry\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15\\u0026ndash;20\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eU2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUniversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSociology\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20+\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eU3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUniversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAnthropology\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10\\u0026ndash;15\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eU4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUniversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHealth Policy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15\\u0026ndash;20\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eU5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUniversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHealth Service Research\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10\\u0026ndash;15\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eU6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUniversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMathematics\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20+\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eU7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUniversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHealth Service Research\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10\\u0026ndash;15\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eU8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUniversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHealth Economics\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eU9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUniversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEpidemiology\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20+\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eU10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUniversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCommunication\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10\\u0026ndash;15\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGovernment-funded Research Institute\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEconomics\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGovernment-funded Research Institute\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHealth Systems Research\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15\\u0026ndash;20\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGovernment-funded Research Institute\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSocial Welfare\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20+\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGovernment-funded Research Institute\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCommunity Health\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15\\u0026ndash;20\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGovernment-funded Research Institute\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSocial Epidemiology\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10\\u0026ndash;15\\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 \\u003cem\\u003eU\\u0026thinsp;=\\u0026thinsp;University-based researcher; G\\u0026thinsp;=\\u0026thinsp;Government-funded research institute researcher. Affiliations reflect participants' institutional positions at the time of interview. To protect participant anonymity, specific committee during the COVID-19 period are not disclosed. Years of experience refers to research experience in the relevant discipline.\\u003c/em\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eData Collection\\u003c/h3\\u003e\\n\\u003cp\\u003ePotential participants were identified through online searches of COVID-19 research networks and policy documents, then recruited via snowball sampling. Each was contacted individually by email with a study description and Korean-language interview guide.\\u003c/p\\u003e \\u003cp\\u003eInterviews were conducted between October 2022 and February 2023. The authors conducted face-to-face interviews at participants\\u0026rsquo; offices, and online interviews were offered when in-person meetings were not feasible. Each interview lasted 60 to 90 minutes and was audio-recorded with consent. Both interviewers took independent notes during each session, which were shared with the full research team immediately afterward to ensure transparency and support collaborative interpretation.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData Analysis\\u003c/h2\\u003e \\u003cp\\u003eInterview data were analyzed using framework analysis following Spencer's five-stage approach[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. This method allows for systematic analysis while remaining open to emergent themes. The Knowledge-to-Action framework informed the initial analytical structure,[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e] but the final four stages (knowledge production, exchange, utilization, and conversion to knowledge production) emerged through iterative deductive-inductive analysis.\\u003c/p\\u003e \\u003cp\\u003eIn the familiarization stage, the first author transcribed all recordings verbatim and read through transcripts repeatedly. During identification, we developed the thematic framework by noting recurring ideas and checking alignment with a priori categories. In indexing, we applied the framework to all transcripts. The charting stage involved extracting coded data into matrices organized by theme and participant. Finally, in mapping and interpretation, we examined relationships between themes and developed explanatory accounts.\\u003c/p\\u003e \\u003cp\\u003eDuring disassembling, transcripts were segmented into meaning units ranging from single sentences to short paragraphs, then open-coded. Coding proceeded both inductively and in dialogue with the interview guide structure. The research team shared analytic memos and held regular discussions to refine codes, and discrepancies were resolved through deliberation between the two lead authors.\\u003c/p\\u003e \\u003cp\\u003eIn reassembling, codes were grouped into candidate themes. During interpreting, themes were iteratively reviewed for coherence with the research questions and the broader policy context; disagreements were resolved through discussion until consensus was reached. In the concluding phase, the first author drafted thematic summaries, which the corresponding author reviewed to finalize the analysis. Themes were organized according to the four KT stages, and subtheme frequency was tallied based on meaning units. All data collection and analysis were conducted in Korean.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eTrustworthiness and Reflexivity\\u003c/h3\\u003e\\n\\u003cp\\u003eSeveral strategies were employed to enhance rigor. Despite the modest sample size, we sought breadth by including participants with varied institutional roles. Interpretive consistency was supported through iterative cross-checking, peer debriefing, and collaborative discussion throughout the analytic process.\\u003c/p\\u003e \\u003cp\\u003eAs researchers specializing in health and research policy rather than infectious disease, our disciplinary positioning shaped the interview guide and analytic lens. We acknowledge that this background, along with the fact that many participants were affiliated with government-linked institutions, may have oriented perspectives toward pragmatic rather than critical viewpoints.\\u003c/p\\u003e \\u003cp\\u003eInterviews were conducted and analyzed in Korean, then translated into English during manuscript preparation. Although care was taken to preserve meaning, certain cultural or sociopolitical nuances may have been attenuated. We recognize that some degree of interpretive influence may have been introduced through the translation and analytic process.\\u003c/p\\u003e\\n\\u003ch3\\u003eEthical Issues\\u003c/h3\\u003e\\n\\u003cp\\u003eThis study was approved by an Institutional Review Board (IRB no. blinded for review).\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThis study identified barriers across four stages of KT during Korea\\u0026rsquo;s COVID-19 response. These barriers not only hindered KT but also structurally blocked co-production, the collaborative knowledge production among researchers, policymakers, and field partners. These barriers recurred across stages and appeared to reflect underlying structural patterns, which are analyzed in the Discussion.\\u003c/p\\u003e\\n\\u003ch2\\u003e\\u003cem\\u003e1. Knowledge Production Stage: Epistemic Exclusion and Structural Precarity\\u003c/em\\u003e\\u003c/h2\\u003e\\n\\u003cp\\u003eAt the knowledge production stage, two interrelated barriers emerged. Funding structures concentrated resources in biomedical fields while structurally marginalizing social science disciplines (1.1), and short-term project cycles combined with career pressures eroded researchers\\u0026rsquo; capacity to sustain inquiry over time (1.2).\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e1.1 Unequal Research Opportunities\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003eResearch opportunities in COVID-19 studies were unevenly distributed. Funding concentrated on development of healthcare technology like vaccines, therapeutics, and diagnostic devices, structurally excluding \\u0026lsquo;minor disciplines\\u0026rsquo; such as public health, social sciences, and humanities.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;People in the social sciences or humanities [...] it felt like we were being pushed to the periphery. Looking at the research funding announcements, there was almost nothing we could do.\\u0026quot; (Participant U1, Psychiatry)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMost urgent research projects focused on biomedical topics such as clinical characteristics\\u0026nbsp;of SARS-CoV2, vaccine efficacy, and therapeutic development. Conversely, research on social and structural issues, such as the impact of quarantine measures on daily life, health inequalities among vulnerable groups, and the breakdown of care systems, was deemed \\u0026lsquo;secondary\\u0026rsquo;.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;My research originally focused on the impact of epidemic control policies on caregivers, but I had to include terms like mental\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003e\\u0026lsquo;health\\u003c/em\\u003e\\u003cem\\u003e\\u0026rsquo; or\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003e\\u0026lsquo;health disparities\\u003c/em\\u003e\\u003cem\\u003e\\u0026rsquo; in the proposal. That is what it takes to be recognized as healthcare research.\\u0026quot; (Participant U2, Sociology)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe disparity in publication speed across disciplines was also a significant issue. Quantitative research in epidemiology was swiftly reflected in policy through rapid publication, whereas qualitative research in sociology or anthropology missed the policy window and was excluded.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;By the time we finish fieldwork and analysis [...] policies are already decided. Our research is only used for post-hoc evaluation.\\u0026quot; (Participant U3, Anthropology)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eResearch opportunities were concentrated among a small number of \\u0026lsquo;core\\u0026rsquo; researchers, and projects were repeatedly assigned to those with prior experience in infectious disease or established networks with the government, making entry even more difficult for early-career researchers or those in peripheral academic fields.\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e1.2 Threatened Research Continuity\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003eIn the early stages of the pandemic, researchers\\u0026rsquo; sense of social responsibility and collective creativity led to innovative responses, such as devising drive-through testing methods. However, from the mid-stage onward, researchers felt pressure to return to their \\u0026lsquo;main jobs\\u0026rsquo; and burdens related to career management, threatening the continuity of their research. These conditions amount to what might be termed structural precarity in knowledge production: a state in which researchers\\u0026rsquo; capacity to sustain inquiry is chronically undermined not by individual choices but by short-term funding logic, misaligned institutional incentives, and career structures that systematically disincentivize sustained engagement with policy-relevant questions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;At first, everyone thought it was a national crisis and dedicated themselves. But after a year passed [...] I started wondering how this would be recognized as my research achievement and whether it would help my career pathway.\\u0026quot; (Participant U4, Health Policy)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMost COVID-19 research projects were designed as short-term assignments lasting 3-6 months, with no guarantee of securing follow-up funding. This made it impossible to maintain research teams or accumulate long-term data. In fields like public health, where specialized personnel are limited, existing researchers found themselves overwhelmed with work.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;Professors juggle multiple research projects. Dedicating yourself entirely to COVID-19 research just was not realistic.\\u0026quot; (Participant U5, Health Service Research)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;I work at a government\\u003c/em\\u003e\\u003cem\\u003e-funded research institution with existing responsibilities. COVID-19 was not my primary focus, and I had limited capacity, but I was overwhelmed with COVID-19 research requests.\\u0026quot; (Participant G1,\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003eEconomics)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTensions persisted between social responsibility and performance management (securing research funding, publication output, teaching and administrative duties).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e2. Knowledge Exchange Stage: Severed Mediation and Epistemic Hierarchy\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe knowledge exchange stage revealed two compounding barriers. Epistemic divides between disciplines and evidence hierarchy disputes blocked meaningful dialogue between researchers and policymakers (2.1), while the structural fragility of mediation channels prevented these divides from being bridged institutionally (2.2).\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e2.1 Deficient Mutual Understanding\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003eThere was a lack of mutual understanding between researchers and policymakers, with pronounced differences in perceptions regarding the hierarchy of evidence. Those with conservative perspectives recognized only randomized controlled trials\\u0026nbsp;(RCT) and systematic reviews\\u0026nbsp;(SR) as rigorous evidence, while those with flexible perspectives argued that diverse forms of evidence should be accepted during public health crises, leading to conflict.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;Policymakers ask, \\u0026apos;\\u0026apos;Has this been validated by an RCT?\\u0026apos;\\u0026apos; How can you conduct an RCT during a pandemic [...] We are doing our best with observational studies, modeling, and field evidence.\\u0026quot; (Participant U6,\\u0026nbsp;\\u003c/em\\u003e\\u003cem\\u003eMathematics)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis strict stance on the hierarchy of evidence proved problematic, when evaluating the effectiveness of non-pharmaceutical interventions, such as mask-wearing and social distancing. While conducting RCTs for these interventions is difficult for ethical and practical reasons, observational studies or modeling studies were dismissed as \\u0026lsquo;low-level evidence\\u0026rsquo;.\\u003c/p\\u003e\\n\\u003cp\\u003eEpistemic divides between disciplines were also a significant factor hindering knowledge exchange. Initial \\u0026lsquo;soft\\u0026rsquo; discussions became contentious as knowledge accumulated, with each discipline remaining stuck in a fragmented understanding, much like the parable of the blind men and the elephant.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;Each discipline has its own jargon that feels intuitive internally. The challenge was figuring out how much background to provide when communicating with researchers from other fields.\\u0026quot; (Participant U7, Health Service Research)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;Genuine interdisciplinary dialogue requires acknowledging that your perspective, however valid, does not capture the whole picture.\\u0026quot; (Participant G2, Health System Research)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e2.2 Limited Institutionalization of Knowledge Brokering\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003eChannels for mediating between researchers and policymakers remained fragile and temporary. Policymakers perceived health as a \\u0026lsquo;specialized domain,\\u0026rsquo; exhibiting a passive attitude characterized by excessive reliance on\\u0026nbsp;medical\\u0026nbsp;experts and reluctance to voice their own opinions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;Policymakers often defer on healthcare issues, saying \\u0026apos;\\u0026apos;that is outside my expertise\\u0026apos;\\u0026apos;. They treat it as a specialized domain and hesitate to question or challenge what medical experts tell them.\\u0026quot; (Participant U7, Health Service Research)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMeanwhile, researchers at government-funded research institutes, who possessed dual expertise in both research and policy, endeavored to serve as boundary spanners mediating between academic and policy communities. However, their temporary advisory positions and project-based engagements led to severed connections upon the end of their terms. The absence of regularized exchange channels resulted in only sporadic meetings, and the pattern of exchanges naturally fading away upon the termination of research funding repeated itself.\\u003c/p\\u003e\\n\\u003ch2\\u003e\\u003cem\\u003e3. Knowledge Utilization Stage: Exclusion and Instrumentalization\\u003c/em\\u003e\\u003c/h2\\u003e\\n\\u003cp\\u003eAt the knowledge utilization stage, barriers operated through both exclusion and instrumentalization. Non-biomedical and field knowledge were excluded within advisory structures (3.1), while decision-making processes remained path-dependent and formalistic rather than evidence-informed (3.2).\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e3.1 Excluded Knowledge\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eAsymmetric Evidence Selection: Biased Advisory Structure\\u003c/u\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough the advisory committee was diverse in composition, discussions were dominated by physicians.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;When medical professionals speak at meetings, everyone listens. But when a public health officers speaks, the question \\u0026apos;\\u0026apos;How does that explain medically?\\u0026apos;\\u0026apos; comes back. We are constantly placed in a position where we have to justify ourselves.\\u0026quot; (Participant U8, Health Economics)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe medical-centric structure was clearly evident at the February 3, 2020 advisory meeting (all physicians) and persisted in subsequent committees: the With COVID Committee (50% physicians out of 18 members) and the Daily Life Recovery Support Committee (75% physicians out of 8 members).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eAsymmetric Evidence Selection:\\u0026nbsp;\\u003c/u\\u003e\\u003cu\\u003eSilenced Voices from the Field\\u003c/u\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eVoices from the field were not reflected until the mid-pandemic period. Opinions from public health centers were excluded from policy discussions, and vulnerable groups, caregivers, women, individuals with severe mental illness, and persons with disabilities, migrants, and the homeless, experienced ongoing invisibilization.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;Outbreaks in long-term care facilities were predictable, but the response was delayed. We kept warning on the ground, but those voices were not heard in policy meetings.\\u0026quot; (Participant G3, Social Welfare)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePublic health center staff were best positioned to understand the realities on the front lines of disease control, yet their experience and knowledge were scarcely considered in policy meetings. While some center directors attended advisory meetings, their opinions were dismissed as \\u0026lsquo;anecdotal evidence\\u0026rsquo; and had no real impact on policy decisions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eResponsibility-Avoiding Evidence Selection\\u003c/u\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePast infectious disease response experiences and overseas policy cases were selectively utilized. Participants noted a tendency to choose proven methods in case of policy failure, which they attributed to a desire to secure\\u0026nbsp;a basis\\u0026nbsp;for\\u0026nbsp;deflecting accountability.\\u0026nbsp;.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;Officials always want overseas precedents before committing resources. Trying something untested carries political risk. If it fails and there is no precedent to point to, they are left exposed.\\u0026quot; (Participant G4, Community Health)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e3.2 Stagnant Evidence-Informed Decision-Making Culture\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eEvidence-Informed vs. Evidence-Based\\u003c/u\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePolicymakers considered themselves \\u0026lsquo;evidence-informed\\u0026rsquo; but in practice remained merely \\u0026lsquo;evidence-based\\u0026rsquo;. Evidence-informed policy involves exploring evidence relevant to actual problems in the field and considering social, institutional, and political contexts, yet in reality it manifested as selective evidence utilization at the decision-making moment and a focus on outcomes. For instance, discussions on economic damage, the double burden of care, elderly isolation, and the exhaustion of public health centers were insufficient.\\u003c/p\\u003e\\n\\u003cp\\u003ePolicymakers initially relied heavily on scientific evidence, expecting a quick resolution. When the pandemic extended, they attempted to diversify advisory membership, but the meetings lacked deliberative structure and failed to facilitate genuine consensus among stakeholders with differing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cu\\u003eFormalistic Advisory Meetings\\u003c/u\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe advisory meetings were described by participants as formalistic. The meetings served to \\u0026lsquo;filter\\u0026rsquo; proposals the government had already decided upon, failing to operate as venues for substantive discussion. Some participants described the advisory meetings as acting as mere rubber stamps.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;Sometimes meeting materials arrived the morning of the meeting itself. How could we possibly review that and offer opinions? It was essentially going to rubber-stamp the government proposal.\\u0026quot; (Participant U9, Epidemiology)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003ch2\\u003e\\u003cem\\u003e4. Conversion to Knowledge Production Stage: Insufficient Momentum\\u003c/em\\u003e\\u003c/h2\\u003e\\n\\u003cp\\u003eThe final stage concerned whether COVID-19 experience could be converted into a foundation for future knowledge production. Although participants recognized the need for systematic documentation (4.1), both internal structural constraints and the rapid decline in external funding momentum prevented this from materializing (4.2).\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e4.1 Recognition of Need to Systematize COVID-19 Experience\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003eResearchers recognized the need to organize COVID-19 experience, and key research topics for future responses to similar public health crises were proposed, including the social gains and losses of non-pharmaceutical interventions, the scale of health damage to vulnerable groups, and mental health service demand by disaster scale.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;We have conducted a massive social experiment during this pandemic. If we do not properly document and analyze it, we will repeat the same mistakes next time.\\u0026quot; (Participant G5, Social Epidemiology)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003cem\\u003e4.2 Insufficient Momentum for Knowledge Systematization\\u003c/em\\u003e\\u003c/h3\\u003e\\n\\u003cp\\u003eHowever, the momentum for knowledge systematization was insufficient. Internal factors included a limited pool of researchers, a rigid research funding structure, and an inflexible organizational culture. With the number of infectious disease researchers itself being limited, existing researchers were already overburdened, and the entry of new researchers was constrained by insufficient funding and an unstable research environment.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;Government funding priorities follow what public health and preventive medicine researchers emphasize. If your topic does not fit that mold, you get pushed aside. And when everything else is covered, they suddenly say communication matters.\\u0026quot; (Participant U10, Communication)\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eExternally, as social interest declined sharply, research funding was cut. Starting in the second half of 2021, COVID-19-related research funding began to decrease sharply, and in 2022, almost no new projects were announced.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u0026quot;Securing the same priority and budget we had during the pandemic is now extremely difficult. Actually, it is nearly impossible.\\u0026quot; (Participant U5, Health Service Research)\\u003c/em\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study confirms that KT faced structural barriers throughout all four stages of the COVID-19 response: knowledge production was constrained by funding concentration, exchange was hindered by epistemic divides, utilization was shaped by path-dependent advisory structures, and the conversion to future knowledge production lost momentum as public and institutional attention to the pandemic waned. These barriers were not mere communication issues nor temporary coordination failures, but appeared to reflect deeper patterns involving power imbalances, institutional structures, and political contexts. While structural factors appear dominant in this analysis, individual and contingent factors may also have contributed.\\u003c/p\\u003e \\u003cp\\u003eCross-cutting analysis of the barriers identified in the Results reveals five interrelated structural mechanisms across micro (interpersonal interactions), meso (institutional structures), and macro (research policy) levels: (1) epistemological incommensurability and incomplete boundary work; (2) institutional fragmentation and misaligned incentives; (3) the dominance of biomedical epistemology and the production of uncomfortable knowledge; (4) bureaucratic expert dependency and path-dependent decision making; and (5) the absence of equity and power redistribution (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\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\\u003eSynthesis of Knowledge Translation Barriers and Structural Mechanisms\\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=\\\"left\\\" 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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKT Stage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBarriers\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMechanism\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLevel\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKnowledge\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cb\\u003eProduction\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026bull; Funding concentrated on biomedical fields\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Social sciences structurally excluded\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Short-term projects (3\\u0026ndash;6 months)\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Career pressure vs. social responsibility\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026bull; Biomedical epistemology dominance\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Institutional fragmentation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMacro\\u003c/p\\u003e \\u003cp\\u003eMeso\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKnowledge\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cb\\u003eExchange\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026bull; Evidence hierarchy disputes (RCT supremacy)\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Epistemic divides between disciplines\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Temporary mediation channels\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Failed knowledge brokering\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026bull; Epistemological incommensurability\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Institutional fragmentation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMicro\\u003c/p\\u003e \\u003cp\\u003eMeso\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eKnowledge\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cb\\u003eUtilization\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026bull; Physician-dominated advisory committees\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Field voices silenced\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Risk-averse evidence selection\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Formalistic rubber-stamp meetings\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026bull; Bureaucratic path dependency\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Biomedical epistemology dominance\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Absence of equity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMacro\\u003c/p\\u003e \\u003cp\\u003eMeso\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eConversion to\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cb\\u003eKnowledge\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e\\u003cb\\u003eProduction\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026bull; Declining social interest\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Research funding cuts\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Limited researcher pool\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Rigid organizational culture\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026bull; Institutional fragmentation\\u003c/p\\u003e \\u003cp\\u003e\\u0026bull; Biomedical epistemology dominance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMeso\\u003c/p\\u003e \\u003cp\\u003eMacro\\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 \\u003cem\\u003eLevels: Macro\\u0026thinsp;=\\u0026thinsp;research policy; Meso\\u0026thinsp;=\\u0026thinsp;institutional structures; Micro\\u0026thinsp;=\\u0026thinsp;interpersonal interactions. Mechanisms cut across KT stages and are analyzed in detail in the Discussion.\\u003c/em\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEpistemological Incommensurability and Incomplete Boundary Work\\u003c/h2\\u003e \\u003cp\\u003eDifficulties in interdisciplinary communication stemmed largely from epistemological incommensurability rather than communication skills alone. Each discipline possessed distinct knowledge systems, methodological traditions, and validity criteria, and there was no boundary work mechanism to reconcile these differences[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Differences in perceptions of evidence hierarchies were problematic, as researchers internalized a hierarchy placing randomized controlled trials and systematic reviews at the apex, with qualitative research or field evidence positioned lower down[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. This hierarchy posed particular challenges for responding to COVID-19, which was not merely a biomedical problem but a social pandemic that disproportionately affected vulnerable populations, disrupted economic activities, and exposed structural inequalities in care systems[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Addressing such multidimensional crises requires drawing on diverse forms of evidence beyond clinical trials, including insights from social sciences, community health, and frontline practice[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003ePublic health is inherently interdisciplinary,[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e] but COVID-19 responses tended to overlook this characteristic. Such marginalization of non-biomedical disciplines mirrors patterns seen in the UK, where mathematical modeling took center stage while research on community and welfare inequalities was devalued,[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e] and where excessive reliance on RCTs and modeling excluded qualitative evidence from the field[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. Barriers to multidisciplinary research operate complexly across scientific, structural, and interactional dimensions,[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e] with communication issues permeating all these layers[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. This epistemological divide has implications beyond academic disagreement; it determines which problems become visible to policymakers and which solutions are deemed legitimate[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. When biomedical framing dominates, social determinants of health and structural inequalities remain systematically invisible in policy discussions. Bridging these epistemological divides requires dedicated intermediaries who can translate across disciplinary boundaries.\\u003c/p\\u003e \\u003cp\\u003eThe mediating role of researchers at government-funded research institutes was crucial yet structurally vulnerable. These boundary spanners occupy a unique institutional position, possessing both research expertise and proximity to policy processes, enabling them to translate between academic and policy communities[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e].While they could function as politically astute, pragmatic intermediaries,[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e] their small numbers and temporary positions prevented the formation of sustainable connections. This signifies the failure to institutionalize the mediation system, leading to inefficiency where relationships must be rebuilt from scratch during each crisis.\\u003c/p\\u003e \\u003cp\\u003eThese structural barriers also affected individual researchers. Researchers\\u0026rsquo; conflicts of multiple responsibilities were a product of structural pressures. The tension between internal social responsibility (scientific rigor), external social responsibility (solving societal problems), and performance management pressures transformed some researchers from pure scientists into covert issue advocates[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. This was less a matter of individual choice than the result of structural pressures created by the correlation between securing research funding and research outcomes[\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eInstitutional Fragmentation and Misaligned Incentives\\u003c/h2\\u003e \\u003cp\\u003eInstitutional structures also systematically hindered co-production. Research funding allocation focused on healthcare technology development, advisory committee composition remained formally diverse, and the academic reward system penalized interdisciplinary research[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. These elements operated in separate institutional silos with conflicting logics: funding agencies prioritized technological innovation, advisory bodies sought rapid expert consensus, and universities rewarded disciplinary specialization. This institutional fragmentation, where different components of the research policy system pursued incompatible goals,[\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e] fundamentally blocked the integrated approach required for co-production.\\u003c/p\\u003e \\u003cp\\u003eThe problems with the academic reward system were severe, as researchers perceived interdisciplinary collaborative projects as more difficult to publish than traditional single-discipline papers and as being undervalued in performance evaluations. This functioned as a structural disincentive, weakening researchers\\u0026rsquo; motivation to participate in co-production[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR51\\\" citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]. Beyond these individual-level disincentives, institutional procedures created additional barriers to inclusive participation. The ethics review process, for instance, faced a dilemma between speed and participation. The rapid approvals demanded during the pandemic led to the omission of participatory approval processes involving civic partners or vulnerable stakeholders, further undermining the foundations of co-production[\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe temporary and short-term structure of advisory committees made it impossible to build the trust and relationships required for co-production. While co-production demands time and mutual learning,[\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e] Korea\\u0026rsquo;s advisory structure consisted of one-off meetings, and relationships naturally dissolved once research funding ended. This reflects a structural problem where co-production is replaced by one-time consultations.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eThe Dominance of Biomedical Epistemology and Uncomfortable Knowledge\\u003c/h2\\u003e \\u003cp\\u003eResearch funding concentrated on healthcare technologies systematically excluded social science and public health perspectives, generating what Rayner terms \\u0026lsquo;uncomfortable knowledge\\u0026rsquo;, which institutions find difficult to absorb because it challenges prevailing assumptions or threatens established priorities[\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]. Participants explicitly identified research topics that were recognized as important yet systematically left unstudied: care system impacts, health inequalities among vulnerable populations, and non-pharmaceutical intervention effects on daily life. This pattern reflects a broader tendency for institutions to suppress or ignore knowledge that does not align with dominant frameworks, not necessarily through deliberate suppression but through structural priorities that render certain questions invisible[\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e]. Research on vulnerable populations, care systems, and health equity remained understudied, creating blind spots in pandemic response that shaped which policy options were visible and which were not.\\u003c/p\\u003e \\u003cp\\u003eThe interdisciplinary hierarchy where medicine takes precedence over preventive medicine, and preventive medicine over social sciences, is deeply entrenched in healthcare system,[\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e] forming a structured hierarchy of knowledge[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Medical dominance, especially physicians, manifested not only in the composition of advisory committees but also functioned as epistemic power, determining which knowledge was deemed \\u0026lsquo;scientific\\u0026rsquo; and \\u0026lsquo;reliable\\u0026rsquo;. Medical professionals secured strong social trust due to their accumulated practical expertise in hospitals and laboratories and their position directly handling lives, and trust in physicians acted as a key predictor of epidemic prevention compliance[\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe physician-centered structure excluded field perspectives,[\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e] and medical explanations monopolized scientific legitimacy while excluding social, cultural, and ethical contexts[\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e]. The voices of medical experts are perceived as scientific and neutral, yet they actually represent specific perspectives[\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e]. This has led to narrow problem recognition, reducing epidemic prevention to merely a technical issue of suppressing infectious diseases and causing invisibilization of its comprehensive impacts on daily life and vulnerable groups.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBureaucratic Expert Dependency and Path-Dependent Decision Making\\u003c/h2\\u003e \\u003cp\\u003ePolicymakers\\u0026rsquo; reliance on experts reflected the static culture of bureaucracy. Policymakers defined health as a \\u0026lsquo;specialized domain,\\u0026rsquo; deferred their own judgment, and delegated authority to specific types of experts, physicians in particular. This is a classic pattern of technocracy, reducing policy judgment to technical expertise[\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e]. The problem was that this delegation was selective: medical expertise was embraced, while public health, social science, and field experience expertise were marginalized.\\u003c/p\\u003e \\u003cp\\u003eThis selective delegation can be explained by path dependency, which manifested in two forms, the repetition of past infectious disease response experiences and the context-free transplantation of overseas cases. Such reliance on past experiences and proven methods, combined with political risk aversion, exemplifies increasing returns path dependency[\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e]. Comparative cases illustrate the dual nature of this phenomenon. In Norway, repeating past responses secured social trust but reduced opportunities for policy shifts,[\\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e70\\u003c/span\\u003e] while in Korea, adhering to past frameworks hindered creative and structural transformations[\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe gap between espoused evidence-informed policy and actual evidence-based practice stemmed from this path-dependent, risk-averse decision-making culture[\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e73\\u003c/span\\u003e]. Selecting proven methods provided political cover in case of policy failure, while exploring context-sensitive evidence for emerging issues carried greater uncertainty and thus greater political risk[\\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e74\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e]. Similarly, the formalistic nature of advisory meetings can be understood as a legitimation strategy[\\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e76\\u003c/span\\u003e]. Convening expert panels provided the appearance of scientific rigor while maintaining bureaucratic control over actual decisions[\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e]. This signifies the instrumentalization of science, where scientific knowledge is mobilized not to inform decisions but to justify predetermined policy directions[\\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e77\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e78\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eThe Absence of Equity and Power Redistribution\\u003c/h2\\u003e \\u003cp\\u003eThe equity mandate clarifies the core element overlooked in the Korean case.[\\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e79\\u003c/span\\u003e] Equality grants everyone an equal voice and role, whereas equity involves actively and continuously redistributing resources and power, placing greater weight on the voices of marginalized groups. Korea's COVID-19 advisory structure failed to achieve even equality. While the advisory committee formally included experts from diverse backgrounds, discussions were dominated by physicians' voices, marginalizing perspectives from public health or the social sciences.\\u003c/p\\u003e \\u003cp\\u003eIn the Korean case, all three dimensions of power redistribution were absent. In the material dimension, researchers outside biomedical fields lacked equitable access to funding, institutional resources, and academic prestige. In the cognitive dimension, diverse forms of knowledge, including field experience and social science evidence, were denied equal recognition. In the decision-making dimension, non-biomedical voices held neither veto power nor meaningful representation. These absences were most visible at the front lines. Health center staff, care providers, and vulnerable groups were excluded from policy meetings, their experiential knowledge was dismissed as unscientific, and they were granted no decision-making authority whatsoever. This resonates with the argument by Cameron and Fiolet[\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e] that co-production requires not merely broader participation but genuine redistribution of epistemic power.\\u003c/p\\u003e \\u003cp\\u003eAs reported in the Results, the cluster infection cases in long-term care facilities illustrate the cost of such exclusion. Field workers predicted outbreaks, yet their warnings went unheard in policy meetings, responses were delayed, and infections recurred. This pattern parallels the failure to improve long-term care facilities by excluding direct care workers (who provide 90% of care).[\\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e] Excluding those with the most information is not only an ethical issue but also one of effectiveness.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eImplications for Co-production Theory and Practice\\u003c/h2\\u003e \\u003cp\\u003eThe preceding analysis reveals how epistemological incommensurability, institutional fragmentation, biomedical epistemic dominance, bureaucratic path dependency, and the absence of equity collectively undermined co-production in Korea\\u0026rsquo;s COVID-19 response. These findings offer several contributions to co-production theory and practice.\\u003c/p\\u003e \\u003cp\\u003eTheoretically, this study makes two contributions to co-production scholarship. First, while existing literature has demonstrated that co-production can increase research impact under favorable conditions,[\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e] it has paid insufficient attention to the structural conditions under which co-production is systematically foreclosed before it begins[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e]. The Korean case reveals that these conditions, equitable power relations, institutional alignment, and epistemic pluralism, are not incidental but structural. This suggests that co-production frameworks must incorporate explicit assessment of these preconditions before implementation. Second, by situating co-production failure within a multi-level framework spanning micro, meso, and macro dimensions, this study moves beyond single-level explanations prevalent in the literature. Korea\\u0026rsquo;s case is theoretically productive precisely because its well-developed institutional capacity rules out resource deficiency as an explanation, isolating structural and epistemic factors as the primary mechanisms. These mechanisms are not Korea-specific. Wherever biomedical epistemology structures funding priorities, advisory roles remain temporary, and equity is treated as optional rather than structural, similar dynamics are likely to emerge regardless of overall institutional capacity.\\u003c/p\\u003e \\u003cp\\u003ePractically, this study underscores that co-production may require more than improved communication or goodwill alone[\\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e83\\u003c/span\\u003e]. The structural mechanisms identified here, epistemological incommensurability, institutional fragmentation, biomedical epistemic dominance, path dependency, and the absence of equity, are predominantly structural in nature, suggesting that structural responses may be necessary, though not sufficient on their own[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Policymakers and research institutions seeking to foster co-production must address funding allocation biases, reform advisory committee compositions, and create sustainable platforms for ongoing researcher-policymaker-public engagement rather than ad hoc consultations[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e84\\u003c/span\\u003e]. Specifically, this study points to three directions for policy development, each grounded in the structural mechanisms identified. First, the finding that funding concentration reproduces epistemic exclusion suggests that research governance frameworks may consider treating interdisciplinary mandates not as add-ons but as structural prerequisites, embedded in grant conditions and evaluation criteria from the outset. Second, the failure of boundary spanning under temporary institutional arrangements points to the potential value of permanent, career-protected knowledge brokering roles rather than project-based advisory positions. Third, the gap between formally diverse advisory committees and substantively physician-dominated deliberation indicates that compositional diversity alone may be insufficient; deliberative process reform, including structured mechanisms for non-biomedical input, could be considered to shift epistemic power within advisory bodies.\\u003c/p\\u003e \\u003cp\\u003eThis study has several limitations. First, as a qualitative study in a single country, the findings reflect Korea\\u0026rsquo;s particular context, its healthcare system, bureaucratic culture, and the 2015 MERS experience. Whether these patterns apply elsewhere depends on contextual similarities. However, structural mechanisms such as epistemic hierarchy and institutional fragmentation are not bound by context in the same way that policy outcomes are. Their operation is recognizable across systems with differing healthcare traditions, even where surface-level conditions differ. Although full theoretical saturation was not reached, participants from different institutional backgrounds were raising substantively similar points by the later interviews, suggesting adequate thematic coverage of the core phenomena under investigation. The goal was not statistical representativeness but analytical sufficiency for the research questions posed, consistent with purposive sampling norms in qualitative policy research. Second, this study focused exclusively on researchers, leaving policymakers, frontline health workers, and civil society actors unrepresented. This design choice was deliberate. Researchers are the primary agents of knowledge production, and understanding how structural barriers operate at the production stage required foregrounding their perspectives, particularly those outside clinical medicine. The experiences of policymakers and frontline actors nonetheless remain unexamined. Future research should engage these groups directly to examine how structural exclusion is experienced from the receiving end, and whether the mechanisms identified here are recognized across stakeholder positions. Third, interviews occurred in late 2022 and early 2023 about events from two years prior. Retrospective accounts nonetheless allow participants to reflect on structural patterns that may not have been visible in real time. Fourth, recruitment through networks of funded researchers likely overrepresented those who secured grants while underrepresenting those excluded from pandemic research. The structural barriers described were consistent across participants with varying levels of funding success, suggesting the core findings are not artifacts of this sampling bias. Fifth, our training in health policy rather than clinical medicine oriented our analysis toward structural factors, possibly underemphasizing individual and relational dimensions of co-production failure. The structural perspective developed in this study nonetheless offers a lens that relational accounts tend to overlook. Co-production fails not from poor communication but from structural barriers requiring structural responses, and making that case requires precisely the kind of institutional and epistemic focus adopted in this analysis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study suggests that barriers to co-production lie not in unwillingness to engage but in the absence of conditions that enable genuine collaboration. The five mechanisms identified here, from epistemic hierarchies to limited equity, point to issues unlikely to be resolved through facilitation alone. They suggest a need to reconsider how research is funded, how advisory bodies operate, and how different forms of knowledge are recognized. Without shifts in these structural conditions, co-production risks remaining aspirational. The question may be less about including more voices than about whether those voices influence what gets studied and what gets decided.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eKT: Knowledge Translation\\u003c/p\\u003e\\n\\u003cp\\u003eRCT:\\u0026nbsp;Randomized Controlled Trials\\u003c/p\\u003e\\n\\u003cp\\u003eSR: Systematic Reviews\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eEthical approval and consent to participate\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study received ethical approval from the Institutional Review Board of the National Medical Center (IRB No. NMC-2024-01-0061). Written informed consent was obtained from all participants prior to interview.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used in this study are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was supported by the National Medical Center Research Fund [grant number I-2024-001].\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eJP contributed to the conceptualization and design of the study, developed the interview guide, conducted and transcribed all interviews, led the data analysis, drafted the manuscript, and acquired the funding that supported this work. MK contributed to the conceptualization and overall direction of the research, provided supervision throughout the study, and critically revised the manuscript for intellectual content. Both authors reviewed and approved the final version.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eGraham ID, Logan J, Harrison MB, et al. Lost in knowledge translation: time for a map? J Contin Educ Health Prof. 2006;26(1):13\\u0026ndash;24.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLavis JN, Robertson D, Woodside JM, McLeod CB, Abelson J. How can research organizations more effectively transfer research knowledge to decision makers? 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Evid Policy. 2010;6(2):145\\u0026ndash;159.\\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\":\"info@researchsquare.com\",\"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\":\"Knowledge Translation, Knowledge Production, Co-production, COVID-19, Evidence-Informed policymaking\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9465628/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9465628/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eKnowledge translation (KT) is widely recognized as essential for evidence-informed policymaking, yet the research-policy gap persists. Co-production, namely collaborative knowledge production among researchers, policymakers, and practitioners, has emerged as a promising approach to bridge this gap. However, existing literature has focused largely on articulating co-production's principles and anticipated benefits, while empirical investigations into why it fails in practice remain limited. We conducted a qualitative study using semi-structured in-depth interviews with 15 participants, including university-based researchers (n\\u0026thinsp;=\\u0026thinsp;10) and government-funded institute researchers (n\\u0026thinsp;=\\u0026thinsp;5) with direct experience in Korea's COVID-19 research response. Data were analyzed using framework analysis following Spencer's five-stage approach, informed by the Knowledge-to-Action model across four KT stages: knowledge production, exchange, utilization, and conversion to knowledge production. Barriers were identified across all four KT stages, reflecting five interrelated structural mechanisms: epistemological incommensurability and incomplete boundary work; institutional fragmentation and misaligned incentives; the dominance of biomedical epistemology and the systematic exclusion of uncomfortable knowledge; bureaucratic expert dependency and path-dependent decision-making; and the absence of equity and power redistribution. These mechanisms formed a self-reinforcing cycle that systematically foreclosed the preconditions for co-production, not as isolated failures but as structurally produced outcomes. Barriers to co-production are structural rather than communicative in nature. Addressing them requires concrete institutional responses: restructuring research funding to mandate interdisciplinary participation, reforming advisory committee composition and deliberative processes to enable substantive non-biomedical input, and establishing permanent knowledge brokering infrastructure rather than project-based arrangements. Co-production will remain aspirational without systemic interventions that realign institutional incentives, redistribute epistemic authority, and create sustained platforms for researcher-policymaker-public collaboration.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Structural Barriers to Knowledge Co-production in the Context of Public Health Crisis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-04 23:42:01\",\"doi\":\"10.21203/rs.3.rs-9465628/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-03T13:44:16+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"247885449978045327327843553069373515238\",\"date\":\"2026-04-23T14:34:11+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-04-23T08:42:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-04-22T13:45:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-04-22T13:45:01+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Health Research Policy and Systems\",\"date\":\"2026-04-20T02:04:25+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"84803a33-d8d9-407b-b1ae-4a85cb62ffb7\",\"owner\":[],\"postedDate\":\"May 4th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-03T13:44:16+00:00\",\"index\":14,\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-04T23:42:01+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-04 23:42:01\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9465628\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9465628\",\"identity\":\"rs-9465628\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}