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• Published: 18 September 2023

Translational research and key aspects to make it successful

• Animesh Acharjee   ORCID: orcid.org/0000-0003-2735-7010 1 , 2 , 3 , 4  

Translational Medicine Communications volume  8 , Article number:  19 ( 2023 ) Cite this article

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Translational research [ 1 ] refers to the translation of scientific discoveries into practical applications that can benefit patients and the wider society. In translational research, basic scientists and clinicians collaborate to develop research questions and plan for testing, implementing new interventions for the bed applications. So, in a way translational research [ 1 ] aims to improve human health through the integration of basic science and clinical practice. However, it requires special skillsets that are less pronounce in traditional clinical or basic research.

This letter aims to identify the key soft skills parameters that are essential for successful translational projects.

Academic leadership vs. transformative leadership

Leadership can be defined as the ability to motivate, inspire, and direct people or groups towards a common goal. In translational research, leadership can include overseeing a team consisting of healthcare professionals. It also involves setting priorities and objectives for the organisation [ 2 ]. It is vital to implement policies and strategies that improve education quality, support faculty, staff, and uphold institution's mission and objectives. In several ways, translational leadership is different from academic leadership: Translational research leadership must be more patient-centric and people-cantered. It should also focus on the integration of individual expertise into a larger framework. Facilitate interactions between clinically motivated issues and non-clinical elements such as statistical or mathematical considerations. These skills can be developed while maintaining cultural humility. This will result in stronger translational research teams and increased satisfaction at the locations where they are applied.

Management of the translational projects

Project management is a method of ensuring that complex tasks are completed on time and in a systematic manner. This involves applying relevant data, tools, and skills in a logical, structured, and efficient way. Project management is essential for translational research and it requires collaboration and mainly coordination between many departments. It can be very beneficial when specialists with different skills or professions collaborate on specific tasks. Although project management is well-established in many other industries, it has been less popular in academic science and clinical research. In the past two decades, there has been a rise in funding for collaborative research projects that bring together subject matter experts from different fields to solve scientific problems. The federal Programme Management Improvement and Accountability Act (Federal Programme Management Improvement and Accountability Act) further demonstrated this trend in this domain.

• Communication

Communication is the key in the translational research as it improves everyone's awareness and keeps them informed about the situation’s arounds the projects. One of the examples would be in the area of the translational diagnostics research where discovery is made by computational or quantitative group and trial performed by another group. Those groups need to be constant communication on the updates and follow ups needed accordingly. One of the best ways to communicate and getting periodic update using an organised meeting with an agenda. Such meetings helps to keep everything in order and ensures that all topics are covered. In addition to this, brief report or minutes document helps to make decisions and prioritize actions and hold participants accountable for their responsibilities. Thus, it provides an unique opportunity to bridge the gap in translational medicine [ 3 ].

Team composition and dynamics

For teams to achieve their translational driven goals, team dynamics [ 4 ] is essential. It creates an environment that encourages, produces work, and helps its members grow professionally. The interactions, relationships, communication patterns, and performance of team members can have an impact on their overall effectiveness and performance. Team dynamics that work well emphasize cooperation, mutual respect and open communication. They also encourage inclusion with people who are supportive. Different perspectives, backgrounds and experiences can benefit teams for example: inclusion of the machine learning and clinical expert in the same team. However, it can also lead to conflicts which must be managed well using soft skills like conflict or stress management. Team members need to have faith in each other's abilities, intentions, as well as their dedication. Team success is dependent on the ability to resolve conflicts and hence it is important to take timely steps, understand each other's perspective, and find mutually beneficial solutions.

Collaboration and network

Collaboration is the key in this domain. Researchers in the lab-based projects or clinic are expected to combine the expertise and work in a collaborative environment. This also helps communities to figure out what kind of health innovations they need [ 5 ]. Eventually, those collaborations help us to make a network of people with multiple expertise and impact on the society. Most of the time, translational research results can be used and influence society. This is generally done at one of three stages: initial research to influence, research that applies to society, or research on society. However, the important question is how can we get the next generation leaders interested in the translational research that can help people? Networks and the interactions may be the first step towards it.

Roles and responsibilities

Translational research is an interdisciplinary domain that combines scientific discoveries with practical applications in healthcare. Hence, it involves various roles and responsibilities, including basic researchers conducting fundamental research, clinical researchers conducting clinical trials. Translational scientists serving as a bridge between basic researchers and clinicians. Each role plays a vital role in bringing scientific discoveries to the forefront of patient care and public health. Each of the roles need to be defined properly with some flexibility to adapt and move forward. In case of more dynamic roles, a training and integration programme need to be designed.

Conclusions

It is worth taking the time to realise the complex nature of the translational research and their multiple components. We often focus on the outer circle (Fig.  1 ) which is technology or domain specific whereas inner circle which is mainly focused on the non-technical skills are also important. Over the years, translational research has traditionally followed the path of the technology outer circle in Fig.  1 , this has presented challenges because brilliant academic research has rarely translated effectively primarily because it is not directed at clinically critical questions as opposed to good science, as a result, the bench-to-bedside model, as outlined in this work, is evolving to recognize that it must be bed-to-bench-to-bed. To be successful in this area, a training programme in translational research must provide its trainees with exposure to and practise in a wide range of abilities that are typically not covered in a single curriculum. We, as a scientific community, need to welcome and develop multidisciplinary teams from across institutions into our labs, where they may be recognized and rewarded for taking on the difficult task of finding answers rather than raising additional questions. There is a long road ahead for the next generation of researchers who may not follow the standard path to success in academia, and it is imperative that we, as administrators, teachers, and mentors, continue to invest in them.

Multiple processes and steps around translational research is shown. The outer periphery is more on the translational processes and inner circles are more on the soft skills that require to execute the processes in the outer circles

Availability of data and materials

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Acknowledgements

I would like to thank the reviewers for their constructive suggestions.

The author acknowledge support from the NIHR Birmingham SRMRC, HYPERMARKER (Grant agreement ID 101095480), and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.

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Animesh Acharjee

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Animesh Acharjee conceived the idea and designed the analysis; wrote, edited and reviewed the manuscript.

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Acharjee, A. Translational research and key aspects to make it successful. transl med commun 8 , 19 (2023). https://doi.org/10.1186/s41231-023-00153-9

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Translational research: from basic research to regional biomedical entrepreneurship

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This paper examines the effect of translational research on knowledge production and biomedical entrepreneurship across U.S. regions. Researchers have earlier investigated the outputs of translational research by focusing on academic publications. Little attention has been paid to linking translational research to biomedical entrepreneurship. We construct an analytical model based on the knowledge spillover theory of entrepreneurship and the entrepreneurial ecosystem approach to examine the relationship between translational research, biomedical patents, clinical trials, and biomedical entrepreneurship. We test the model across 381 U.S. metropolitan statistical areas using 10 years of panel data related to the NIH Clinical and Translational Science Awards (CTSA) program. CTSA appears to increase the number of biomedical patents and biomedical entrepreneurship as proxied by the NIH Small Business Innovation Research (SBIR) grants. However, the magnitudes of the effects are relatively small. Path analysis shows that the effect of translational research on regional biomedical entrepreneurship is not strongly conveyed through biomedical patents or clinical trials.

Plain English Summary

Can programs designed to speed the transformation of research results into products/processes increase regional entrepreneurship in the biomedical sector? Translational research programs generally address the gap between basic science and clinical trials/commercialization. We examine one such program, the National Institutes of Health (NIH)’s Clinical and Translational Science Awards (CTSA) program, that has supported more than 60 U.S. universities and other institutions since 2006. We find that the program has positively affected regional biomedical entrepreneurship. Translational research also appears to increase the number of regional biomedical patents. The increased biomedical patents could not, however, be said to have “caused” the higher levels of regional biomedical entrepreneurship. Policymakers may intensify efforts to improve the utilization of knowledge produced by translational research activity by boosting efforts to enhance the entrepreneurial awareness and inclination of translational researchers.

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1 Introduction 

Recent COVID-19 vaccine developments have demonstrated the importance of the rapid transfer of scientific knowledge to the clinical and commercial fields for public health. The first U.S. COVID-19 vaccine utilizes a novel technology, messenger RNA (mRNA), developed by a biotech firm, and reduced the development time significantly (Zimmer et al., 2021 ). Other COVID-19 vaccines also have been developed by rapidly transferring new technologies from labs to hospitals (The Johns Hopkins Coronavirus Resource Center, n.d. ; Zimmer et al., 2021 ).

Rapid transfer between basic research and clinical and commercial applications has been discussed for a long time. The decreasing productivity—the average FDA approvals per R&D investment—in the pharmaceutical sector has, however, put this topic once again at the center of attention (Heller & de Melo-Martín, 2009 ; Juliano, 2013 ; Kim, 2019 ; Schuhmacher et al., 2016 ; Wegener & Rujescu, 2013 ). The slow transfer has been pointed out as one of the reasons for anemic performance (Institute of Medicine, 2013 ). Slow connection largely comes from multiple barriers including risky and expensive clinical trials, data sharing issues, and lack of experts (Coller & Califf, 2009 ; Heller & de Melo-Martín, 2009 ; Institute of Medicine, 2013 ).

In response to this policy concern, the National Institutes of Health’s (NIH) Clinical and Translational Science Awards (CTSA) program has been providing over $500 million annually to more than 60 U.S. universities and non-profit research institutions since 2006 to help address those obstacles (Kim, 2019 ; Llewellyn et al., 2018 ). Through the improvement of translational research conditions, the CTSA program desires to increase the speed and volume of the transfer of scientific knowledge into more practical applications (NIH, 2006 ).

Scholars have analyzed the contribution of the CTSA program to increasing academic publications (Kim, 2019 ; Kim et al., 2020 ; Liu et al., 2016 ; Llewellyn et al., 2018 ; Schneider et al., 2017 ). However, the literature has yet to address whether and how this specific translational research program contributes to the biomedical business. Also, the earlier appraisals were generally restricted to the funding of recipient institutions and did not comprehensively consider other factors surrounding translational research and their interactions. As translational research aims to facilitate the transfer of basic research into more practical forms of knowledge, it is important to examine its contribution to the biomedical enterprise, beyond the publication performance of the grant recipients.

We examine the effect of translational research on biomedical knowledge production and biomedical entrepreneurship. Based on the knowledge spillover theory of entrepreneurship and entrepreneurial ecosystem approach, we construct an analytical model and test it across all 381 U.S. metropolitan statistical areas (MSAs) using 10 years of panel data. The NIH CTSA program is utilized as an approximation for translational research while the NIH Small Business Innovation Research (SBIR) program serves as an approximation for biomedical entrepreneurship.

The results indicate a positive association between CTSA funding and regional SBIR grants, but the magnitude is relatively small. CTSA funding increases biomedical patents, but it does not increase the number of clinical trials conducted regionally. Biomedical patents have a positive relationship with SBIR grants, whereas clinical trials do not. Path analysis reveals that the effect of the CTSA funding on SBIR grants is not strongly conveyed through either biomedical patents or clinical trials. We thus conclude that translational research through the CTSA program has had a limited impact on exploitable knowledge production and regional biomedical entrepreneurship.

The rest of the paper is organized as follows. Section  2 presents background on translational research, literature review, and research questions. In Section 3 , we explain how we construct and operationalize the analytical model. We present results in-detail in Section 4 and discuss main findings and policy implications in the following section. The last section concludes.

2 Literature review

2.1 the context of translational research.

Translational research has emerged as an important driver to facilitate conversion and increase biomedical productivity (Fishburn, 2013 ; Van der Laan & Boenink, 2015 ; Woolf, 2008 ; Zerhouni, 2003 ). Translational research is generally understood as a concerted effort to produce new products, services, or treatments from basic research in a rapid manner (Fishburn, 2013 ). Van der Laan and Boenink ( 2015 ) succinctly summarize the emergence of translational research as a reflection of the desire to get more benefit from society’s investment in basic research.

The conceptualization of “translational research” varies among researchers and continues to evolve (Van der Laan & Boenink, 2015 ). Originally, translational research was viewed as a two-phase process: the translation from basic science to human studies, and the translation of new knowledge into clinical practice and health decision-making (Sung et al., 2003 ). NIH ( 2006 ) has a similar definition, found in its first request for application for the Institutional CTSA program.

In the biomedical sector, researchers have recently more narrowly conceptualized the translation framework, which spans from basic science to translation to community (Blumberg et al., 2012 ). This more elongated framework has been reflected in the NIH’s CTSA funding opportunity announcement (NIH, 2012 ). The core elements in the newer translational research framework are from basic science to translation to patients—processes to convert discoveries in the laboratory into clinical trials (Fishburn, 2013 ). Similarly, the FDA also identified this part as a “critical path” to drug development (Woodcock & Woosley, 2008 , p. 4). Many biomedical researchers focused on a narrow conception that usually covered the area “between basic science…and new approaches for pre-clinical work” (Van der Laan & Boenink, 2015 , p. 37).

The domain between basic research and the near-market can be recognized as a market failure because of the sub-optimal distribution of resources such as venture capital funding. Public agencies like the NIH strongly support basic research, while the private sector heavily invests in marketable products or services. However, the middle part—the so-called valley of death—is often regarded as too risky for the private sector to invest in because it requires huge investments (Butler, 2008 ), along with the uncertainty of getting a good return on such investments. Figure  1 illustrates the conceptualization of the valley of death along the translational continuum in the biomedical sector.

Illustration of valley of death in biomedical sector. Adapted from Seyhan ( 2019 , p.7) and Reis et al., ( 2008 , p.10)

The gap between basic science and clinical science is often referred “translational gap” in the biomedical sector (Seyhan, 2019 , p. 6). Crossing the gap requires not just enough funding, but also strong support to advance discoveries in the lab toward the bedside. Thus, public intervention is justified to mitigate the uncertainty and a large number of resource inputs when developing scientific knowledge, products, and services. Proper policy measures have the potential to shorten the time required for the development of biomedical products and services, thereby contributing to the greater public good.

2.2 Extant literature and research questions

Several scholars have studied the effects of translational research on academic publications, especially by analyzing the CTSA program (Kim, 2019 ; Kim et al., 2020 ; Liu et al., 2016 ; Llewellyn et al., 2018 ; Schneider et al., 2017 ). A strong emphasis has been paid to the number of articles published, among other outputs, resulting from the CTSA program. Investigators have shown that the CTSA program has increased the recipients’ numbers of publications (Kim, 2019 ; Liu et al., 2016 ; Llewellyn et al., 2018 ).

While publication is one critical channel to advance and spread knowledge in the biomedical sector (Llewellyn et al., 2018 ), the literature has yet to address whether or how the CTSA program contributes to economic activity. Economic activity matters because translational research was initiated to facilitate the conversion of basic research into clinical and commercial areas (NIH, 2006 ; Van der Laan & Boenink, 2015 ). Furthermore, the CTSA program considers collaboration with industry and other stakeholders as one of its objectives (NIH, 2006 , 2017 ).

The expansion of the scope of current research to address the impact of translational research on the commercialization of the produced knowledge would be helpful in deepening our understanding. For instance, one can consider whether translational research increases the production of commercially exploitable knowledge, as approximated by biomedical patents and clinical trials, and the extent to which this knowledge enhances biomedical entrepreneurship.

The extant literature on translational research also lacks a general theoretical framework to analyze the effects of translational research comprehensively. Instead, investigators have narrowly restricted their analysis only to the CTSA program recipients (e.g., Kim, 2019 ; Liu et al., 2016 ; Llewellyn et al., 2018 ; Schneider et al., 2017 ). Furthermore, these examinations have not taken into account the fact that translational research is a part of the complex biomedical ecosystem. Indeed, diverse stakeholders (e.g., universities, biomedical firms, and pharmaceuticals) take part in the process that moves a product or a service from its scientific discovery to clinical and commercial fields (Fishburn, 2013 ; Pisano, 2006 ). Given that translational research covers a wide spectrum within the broader biomedical ecosystem, it is imperative to take into account how translational research interacts with other components in the system (Simons et al., 2020 ).

In this vein, a more systemic focus on relevant interactions could lead to a more comprehensive analysis of the effects of translational research. Additionally, we assert that a relevant conceptual framework is needed linking translational research to economic and business activities more comprehensively. We propose such an analytical model in the next section.

Knowledge is typically assumed to spill over from its original source. However, the flows of the ideas and knowledge are hampered by the so-called knowledge filter including institutional, geographical, and economic constraints (Almeida & Kogut, 1999 ; Carlsson et al., 2009 ). Audretsch and Lehmann ( 2005 , p. 1195) define knowledge filter as “the gap between new knowledge and what Arrow ( 1962 ) referred to as economic knowledge or commercialized knowledge.” The knowledge filter concept is in line with the notion of “barriers to transmission” proposed by Hayter ( 2013 ).

The literature in the field of the knowledge spillover theory of entrepreneurship (KSTE) provides a theoretical foundation for explaining knowledge production and utilization (Ghio et al., 2015 ). Economic agents like entrepreneurs utilize the new knowledge to open opportunities by creating new firms to exploit the unused knowledge that firms or research organizations have generated (Acs et al., 2009 ; Audretsch, 1995 ; Braunerhjelm et al., 2010 ; Hayter, 2013 ). This concept envisages entrepreneurship as an effective vehicle enabling the utilization of new knowledge. At the same time, the KSTE implies that knowledge does not flow seamlessly from the inventor to the innovator; neither is all knowledge commercially useful in its original form (Braunerhjelm et al., 2010 ; Hayter, 2013 ).

Based on the KSTE, it is expected that more knowledge production and spillover would lead to higher levels of entrepreneurship (Acs et al., 2009 ). By definition, translational research is expected to generate more usable forms of knowledge, such as publications, patents, and clinical trials. This, in turn, could affect entrepreneurship, since entrepreneurs can utilize converted knowledge to start a new business, for instance, in the biomedical sector. Thus, translational research activities would seem to facilitate knowledge exchange and help overcome obstacles associated with the traditional linear model of technology transfer (Hayter et al., 2020 ). It is in this sense that the KSTE can help us understand how translational research can affect biomedical entrepreneurship through the conversion and utilization of knowledge.

Another useful thread of research that provides insights for the current study is the entrepreneurial ecosystem approach. Explaining what makes a particular region or city achieve more than its counterparts has been an important focus for researchers and practitioners around the globe (Brown & Mason, 2017 ; Feldman, 2014 ). The entrepreneurial ecosystem approach has emerged as a conceptual framework to explain the dynamics within a system (Brown & Mason, 2017 ). While there is no standard definition of an entrepreneurial ecosystem (Stam & Van de Ven, 2021 ), scholars proposed several working terms. For instance, an entrepreneurial ecosystem is a dynamic system with diverse stakeholders, which can include entrepreneurs, universities, government, and consumers (Audretsch & Belitski, 2017 ). More broadly, Stam ( 2015 ) defined the entrepreneurial ecosystem as “a set of interdependent actors and factors coordinated in such a way that they enable productive entrepreneurship” (p. 1765). While there has been some criticism (See Stam & Van den Ven, 2021 ), the entrepreneurial ecosystem approach has provided a compelling framework to analyze regional context (e.g., Mack & Mayer, 2016 ; Spigel, 2017 ).

The sectoral perspective of the biomedical sector should also be emphasized. Every sector has different knowledge and technology bases, as well as different types of actors, networks, and institutions (Malerba, 2004 ). Thus, entrepreneurial ecosystems could be formed or worked based on industry-specific characters (Mason & Brown, 2014 ). As Pisano ( 2006 ) described, the biotech sector has its own particular anatomy, quite distinct from other sectors like information technology. Considering that the biomedical industry is a science-based business, in this paper we define biomedical entrepreneurship as knowledge-intensive entrepreneurial activities that utilize knowledge to exploit opportunities within the biotechnology sector (Malerba & McKelvey, 2020 ; Pisano, 2006 ).

In light of the KSTE and the entrepreneurial ecosystem approach, we consider the following two research questions. First, to what extent do increased level of knowledge translate into biomedical entrepreneurship? Second, do biomedical patents and clinical trials serve as effective forms of knowledge connecting translational research to biomedical entrepreneurship? Further downstream than academic publications, we would like to explore whether these two well-known forms of knowledge are the connecting rods between basic research and biomedical entrepreneurship as described by scholars (e.g., Pisano, 2006 ).

3 Methodology

3.1 empirical context.

To support and facilitate the translation process, the NIH initiated a translational research program called the Clinical and Translational Science Awards (CTSA) program in 2006. Through it, the NIH provides about $500 million annually to approximately 60 universities and nonprofit institutes (Llewellyn et al., 2018 ; NIH, 2019 ). The CTSA program is designed “to transform the local, regional, and national environment for clinical and translational science, thereby increasing the efficiency and speed of clinical and translational research” (NIH, 2006 ). To achieve its goals, the program supports “training, research and infrastructure to help researchers engage in clinical research—and cross the valley of death” (Butler, 2008 , p. 841). In particular, the funded projects and initiatives sponsor activities that influence the translation environment. For instance, SMART IRB provides a platform to help researchers and institutions researching multiple sites that require integrated collaboration. One thing to note is that unlike other NIH awards supporting projects based on diseases, specialties, and investigators, the CTSA program supports improvements specifically in the translational environment (NIH, 2006 ).

3.2 Model specification

We construct an analytical model for biomedical knowledge production and biomedical entrepreneurship. Considering that translational research helps facilitate the conversion of basic science into more usable forms of knowledge, we expect that a vibrant translational research activity increases more useful and commercially exploitable knowledge production. Then, entrepreneurs have a wider set of relevant knowledge stocks to draw from. In this respect, the knowledge spillover theory of entrepreneurship enables us to analyze whether translational research increases biomedical entrepreneurship.

We explore the linkage between translational research and biomedical entrepreneurship, biomedical patents, and clinical trials. Patents are regarded as an important milestone before moving toward commercialization (Reitz & Czupich, 2014 ). Commercialization in the biomedical sector generally occurs through the licensing of intellectual property rights (Kettler, 2000 ; Pisano, 2006 ; Scherer, 2010 ). Each stage of clinical trials generates critical information regarding safety, efficacy, and others, and they are pre-requisite for commercialization in the biomedical sector (NIH, n.d.-a ; Varmaghani et al., 2020 ). In sum, our model posits that by increasing such exploitable knowledge translational research endows entrepreneurs in a region with a wider set of relevant knowledge stock to utilize.

In the construction of the model, we take into account the factors that influence biomedical entrepreneurship. Translational research is not a stand-alone element but an interconnected factor in the biomedical development system. We identify regional factors which may affect regional biomedical entrepreneurship, including public and private biomedical R&D investment, human capital, the presence of large biomedical firms, per capita income, population size, and the size of the regional economy. Figure  2 depicts a schematic description of the model.

A schematic description of the analytical model

In Fig.  2 , the thick black arrow from translational research to biomedical entrepreneurship shows the “direct” relationship between two sides. For further exploration of the detailed relationship, we separate biomedical patents and clinical trials from other outputs, and investigate their roles in bridging translational research and biomedical entrepreneurship. Two red dashed arrows from translational research to biomedical entrepreneurship through biomedical patents and clinical trials depict the “indirect” relationships between two sides. Regional factors are included as control variables in the model.

In the following sections, we first focus on estimating the direct relationship between translational research and biomedical entrepreneurship. Subsequently, we estimate the indirect relationships in order to investigate how the indirect effects of translational research affects biomedical entrepreneurship through biomedical patents and clinical trials.

3.3 Operationalization of the model

We empirically test the analytical model across all 381 U.S. metropolitan statistical areas (MSAs Footnote 1 ) with a panel dataset ranging from 2006 to 2015. The CTSA program is utilized herein as an approximation for translational research while the NIH SBIR program serves as an approximation for biomedical entrepreneurship.

First, we begin with estimating the direct effect of CTSA funding on SBIR grants. Endogeneity is one challenge in estimating the relationship between CTSA funding and SBIR grants. Institutions receiving CTSA funding are not randomly distributed, but they have been selected based on scientific competence among the applicants (NIH, 2006 ). In addition, the SBIR program selects small firms with “feasibility, technical merit, and commercial potential” (NIH, n.d.-b ). Thus, the competitiveness in winning the CTSA funding could be related to the capability of getting the SBIR grants at the regional level.

To address the possible endogeneity, we employ the difference-in-difference (DID) method. The treatment group is comprised of MSAs with CTSA funding, while the comparison group is MSAs with no CTSA funding. A conventional DID equation can be written as Eq.  1 . The dependent variable, SBIR mt , counts the number of SBIR grants received by small firms in an MSA m in year t . We use the number of grants as a proxy for entrepreneurship (Lee et al., 2004 ; Qian et al., 2013 ).

One thing to note is that MSAs in the treatment group receive funding in different time periods, of different durations and different funding sizes. MSAs in the comparison group have zero CTSA funding throughout the whole period. Figure  3 illustrates the difference in funding between the treatment and comparison regions. The solid line represents a profile of one MSA in the treatment group. In total, there are 46 different CTSA funding profiles, as all 46 MSAs in the treatment group have different funding sizes during different periods. The MSAs in the comparison group are represented by the dotted line in Fig.  3 , which shows zero value for the whole period.

Imaginary profiles of CTSA funding in the treatment and comparison groups

In line with previous research (Angrist & Pischke, 2008 , 2014 ; Bertrand et al., 2004 ), we replace the interaction term in Eq.  1 with CTSA funding, as shown in Eq.  2 . Here, the CTSA funding variable, CTSA mt , measures the degree of treatment in MSA m in year t . Accordingly, Eq.  2 includes the MSA dummy (ϒ m ) and the time dummy (λ t ). X mt as control variables. β 2 is the coefficient of our interest. Standard errors are calculated by a robust method and clustered at the MSA. As the dependent variables are count variables that are highly right-skewed, we use the Poisson option.

Second, we estimate the effects of translational research on biomedical knowledge production. Equations  3 and 4 estimate the effects of CTSA funding on biomedical patents and on clinical trials respectively. We use the same DID design as in Eq.  2 .

Third, we estimate the indirect effect of translational research on biomedical entrepreneurship through biomedical patents and clinical trials. As shown in Fig.  2 , biomedical patents and clinical trials are endogenous variables. They are affected by the CTSA funding and other regional conditions. They also affect another endogenous variable, SBIR grants. In this estimation, we consider three paths between the CTSA funding and the SBIR grants: (1) indirect path 1—through biomedical patents; (2) indirect path 2—through clinical trials; (3) direct path—all other outputs except biomedical patents and clinical trials. Equation  5 describes three paths between the CTSA funding and the SBIR grants.

We use path analysis/structural equation modeling to solve the set of simultaneous equations indicated by Eqs.  3 , 4 , and 5 . While the negative binomial model might also be used due to the count variables with over-dispersion, the Poisson option is used here. According to Cameron and Trivedi ( 2010 ), the cluster-robust standard error can be used to address both over-dispersion and serial correlation. Standard errors are calculated by a robust method and clustered at the MSA. We also show the result with the negative binomial estimates in the following section.

Regarding the decision rule of statistical analysis, we use the threshold of 0.1 and report the precise p value. Amrhein et al., ( 2019 , p.306) suggested that researchers need to discuss the meaning of the estimates more explicitly, as well as provide a precise number for the p value if reported, rather an overly relying on “dichotomous” decision rules, like using p values. Following their recommendations, we report the precise p values of the main results and then discuss the implications in-depth.

3.4 Data and variables

Table 1 lists the variables, measures, and data sources. The NIH SBIR grant data were obtained from the NIH RePORTER (NIH, n.d.-d ). We include only new SBIR projects in Phase I and Fast Track, Footnote 2 which means that renewed, supplemental, or extension projects have been excluded. Projects in Phase II also are excluded because they are only available to successful Phase I projects, which are influenced by diverse factors (e.g., firms’ management). The CTSA funding data were likewise obtained from the NIH RePORTER (NIH, n.d.-d ). We use the funding opportunity announcements (FOAs) Footnote 3 of the CTSA program to identify relevant projects (Liu et al., 2016 ).

Biomedical patent data were obtained from the U.S. Patent and Trademark Office ( n.d. ). Following Cortright and Mayer ( 2002 ), we include three biomedical-related technology classes: Class 424-Drug, Bio-Affecting, and Body Treating Compositions (includes Class 514); Class 435-Chemistry: Molecular Biology and Microbiology; and Class 800-Multicellular Living Organisms and Unmodified Parts Thereof and Related Processes. The patent data include the granted utility patents to an MSA from 2006 to 2015, which is the most recent year categorized at the MSA level by the U.S. Patent and Trademark Office.

We obtained clinical trial data from the U.S. National Library of Medicine’s ClinicalTrials.gov website. According to the U.S. law enacted in 1997 and 2007, and the decision by the International Committee of Medical Journal Editors in 2005, all clinical studies should be registered to the ClinicalTrials.gov registry (Califf et al., 2012 ). We downloaded 180,926 clinical studies based on the first study submission date between 2004 and 2015. Some clinical studies were conducted at multiple sites, also including in foreign countries. We removed those that had study locations outside the U.S. After this cleaning process, we were left with 523,341 U.S. clinical trial locations.

Variables representing regional conditions are added as control variables. First, regional public R&D spending in the life science and medical research field is added to represent the strength of scientific knowledge. As there is no aggregated public R&D spending data in life science and medical fields at the MSA level, we collected university R&D expenditures from the Higher Education Research and Development (HERD) Survey (National Science Foundation, 2011 , 2015 , 2018 ). The R&D spending data has been aggregated at the MSA level. As the HERD data may include the CTSA funding, we subtracted CTSA funding from them to construct the final dataset.

Second, we measure the R&D spending of biomedical firms to control the effect of private R&D in that sector. Firm data is obtained from Compustat, a collection of financial information of publicly traded companies. Biomedical firms are selected based on North American Industry Classification System (NAICS) codes, Footnote 4 defined by DeVol et al. ( 2004 ).

Third, we approximate the regional human capital by the percentage of adults (above 25) holding at least a bachelor’s degree or above (Florida, 2002 ; Qian et al., 2013 ). The data is collected from the U.S. Census ( n.d.-a ).

Fourth, the number of large biomedical firms is added to proxy the role of the established firms in the biomedical ecosystem as suggested by the anchor tenant theory (Agrawal & Cockburn, 2003 ; Feldman, 2003 ). Firm data is obtained from Compustat. We counted biomedical firms belonging to the top 25 percent (i.e., 75th percentile) in terms of annual revenue to include relatively large firms.

Fifth, we add per capita income to represent the individual’s ability to start and support a new business. Wallsten ( 2001 ) uses this variable in estimating the probability of winning the SBIR grant at the MSA level. The data is obtained from the U.S. Bureau of Economic Analysis (BEA) ( n.d.-a ).

Sixth, following Qian et al. ( 2013 ), we use regional population density since agglomeration can facilitate knowledge sharing through close and frequent interactions. The population and area data were obtained from the U.S. Census ( n.d.-b , n.d.-c ). Populations between 2006 and 2010 are calculated by interpolating the population in 2000 and 2010 due to the lack of data at the MSA level.

Seventh, the size of the regional economy is added to the list of controls. Access to finance is an important element in expanding venture business and further growth (Isenberg, 2011 ). It is more critical in the biomedical sector because of large resource input needs and a high level of uncertainty (DiMasi et al., 2016 ; Pisano, 2006 ; Sacks et al., 2014 ). We utilize regional GDP to approximate the size of the regional economy and the strength of venture capital financing. The GDP data was obtained from the U.S. Bureau of Economic Analysis ( n.d.-b ).

We used the zip code-MSA code conversion file provided by the U.S. Department of Housing and Urban Development ( n.d. ) to aggregate the data at the MSA level. With the data introduced, we constructed a panel data set of 10 years from 2006 to 2015.

4.1 Descriptive statistics

Summary statistics and the correlation matrix of key variables are presented in Tables 2 and 3 , respectively.

4.2 Parallel trends

With a DID design, the treatment and comparison groups need to have common trends before the treatment. We examine whether the two groups have common trends in our dependent variables—SBIR grants, biomedical patents, and clinical trials—respectively.

Figure  4 presents the trend for the SBIR grants. As this study is not an ordinary pre- and post-treatment setting, there is no shared variable to indicate the treatment point. The treatment years are centered on the first CTSA funding years of each treated MSA. The comparison group is normalized in 2006, the first CTSA program funding year. The y-axis is the mean SBIR count. The top line represents the treatment group, and the bottom line is the comparison group. The dotted line represents the mean SBIR counts for all the MSAs (entire group).

The trends of the NIH SBIR grant. Treatment group (top line), comparison group (bottom line), entire group (middle line)

For the five years prior to the treatment, all three lines declined: the treatment group by 7.4%; the comparison group by 14.8%; and the entire group by 9.4%. This indicates that the two groups had very similar declining trends before the treatment. The overall declining trends are consistent with other NIH SBIR award data, presented in Fig.  5 , which continued to decline over our research period (NIH, n.d.-e ).

Source: Authors, based on the NIH Data Handbook. National Institutes of Health ( n.d.-e )

The trend of the NIH SBIR awards (Phase I and Fast Track).

After the treatment, the slopes are quite different: the treatment group declines only by 5.7%; the comparison group declines by 29.8%; and the entire group declines by 27.9%. The comparison group seems to follow the general declining trend of the SBIR grants, whereas the treatment group shows a slight upward trajectory with some fluctuations. Thus, the data indicate that the two groups have common trends before the funding and changed courses afterwards.

To examine the data further, Fig.  6 presents each group’s ratio to the entire group’s mean SBIR grants. Each group’s line in Fig.  6 was calculated by dividing the mean of the SBIR grants of each group by the mean of the SBIR grants of the entire group. For instance, the treatment group’s mean SBIR grants are six times larger than the mean SBIR grants of the entire group of MSAs. Before the treatment, the two groups have similar parallel trends, but after the treatment, the treated line increases slightly and steadily, whereas the comparison line declines.

The trends of the NIH SBIR grant ratio. Treatment group (top line), comparison group (bottom line)

Similarly, we also review the trends of biomedical patents and clinical trials. Before the treatment (funding), the two groups’ trends in biomedical patents show similar, parallel trends. After the treatment, the treated line climbs rapidly, whereas the untreated line goes flat (See Figs. 7 and 8 ). Thus, we conclude that two groups suffice parallel trend conditions for the DID design.

The trends of biomedical patents. Treatment group (top line), comparison group (bottom line), entire group (middle line)

The trends of biomedical patent ratio. Treatment group (top line), comparison group (bottom line)

However, we found that clinical trials of these two groups have different trends before the treatment: the treatment group rose by 24% and the comparison group rose by 88% (See Figs. 9 and 10 ). This limits the ability to make a causal claim when estimating the effect of CTSA funding on clinical trials.

The trends of clinical trials. Treatment group (top line), comparison group (bottom line), entire group (middle line)

The trends of clinical trials ratio. Treatment group (top line), comparison group (bottom line)

4.3 Results

4.3.1 direct relationship between ctsa funding on the sbir grants.

We first estimate the effect of CTSA funding on the SBIR grants (Eq.  2 ). Panel A in Table 4 presents the results. Column 3 is the model with the year and MSA fixed effects. The CTSA coefficient is 0.00725 and statistically significant at the 0.05 level ( p value: 0.047). The result indicates that a 1% increase in the CTSA funding is expected to increase the number of SBIR grants in an MSA by 0.00725%. Footnote 5 With the fixed effect negative binomial estimate, we have virtually the same coefficient, but a slightly larger standard error ( p value: 0.086). Considering that we cannot get the clustered-robust standard error using negative binomial model and the over-dispersion can be addressed by the Poisson model (Cameron & Trivedi, 2010 ), the estimate with the Poisson holds. Given that the average of SBIR grant counts in the treatment group is 5.95, doubling the CTSA funding size may change the received SBIR grants by 0.043 (= 5.95*0.00725). In sum, we found that CTSA funding increases the number of SBIR grants, but the effect size seems relatively small.

We also tested the time lag effects for the CTSA and SBIR association by lagging the CTSA funding. The CTSA coefficient increases to 0.00952 which is statistically significant at the 0.01 level ( p value: 0.001) at the length of 5 years. The coefficient is slightly reduced to 0.00822 ( p value: 0.062) at the length of 6 years, but it is still larger than the original coefficient. The CTSA coefficients are small and insignificant with other time lags.

To check the robustness of the results, we utilize the number of CTSA institutions as the main predictor instead of CTSA funding. We draw on the anchor tenant hypothesis, which posits that large firms provide supports for regional innovation activities (Agrawal & Cockburn, 2003 ; Feldman, 2003 ). Given that CTSA institutions are generally large universities with hospitals and substantial research capabilities in the region, one can assume that they work like large established organizations facilitating innovation. The NIH calls CTSA-funded institutions “hubs,” and emphasizes collaboration with regional biomedical networks (NIH, 2012 , 2016 ; Obeid et al., 2014 ). Even though there might be some variations in the scope of their roles in the MSA, it is reasonable to assume that CTSA institutions have similar functionality in facilitating translational research in a particular region. The results are presented in column 4, panel B of Table 4 . The CTSA institution coefficient is highly significant ( p value: 0.007). It indicates that additional CTSA institutions in an MSA increase the number of SBIR grants by 6.3%.

As a second robustness check, we utilize the aggregated monetary value of the SBIR grants as a dependent variable instead of using the SBIR grant counts. Column 5, panel B of Table 4 shows the results of the ordinary least squares (OLS). The dependent variable is SBIR funding in log form. We found that the CTSA funding coefficient is significant at the 0.1 level ( p value: 0.096), and the results show that a 1% increase in CTSA funding increases the SBIR funding by 0.045%. Thus, the robustness checks using the number of CTSA institutions as the main independent variable and the monetary value of SBIR grants as the dependent variable support the main findings in panel A in Table 4 .

4.3.2 The effect of CTSA funding on biomedical knowledge production

In this section, we estimate the effect of the CTSA funding on biomedical patents and clinical trials. To do so, we estimate Eqs. 3 and 4 . Table 5 shows the results. The first column in panel A presents the relationship between CTSA funding and biomedical patents. The coefficient is significant at around 0.05 level ( p value: 0.053). The estimate indicates that a 1% increase in CTSA funding increases the number of biomedical patents by 0.0518%. As the mean of biomedical patents in the CTSA-funded MSAs is 114.8, doubling the CTSA funding can change the number of biomedical patents by 0.6 (= 114.8*0.0052) on average.

A reasonable question is whether the CTSA funding affects patents in different industries or technology fields. In other words, is the relationship presented in column 1 of Table 5 specific to patents in the biomedical field? To examine this question, we collected patent data in two different technology fields: Class 361-Electricity: Electrical Systems and Devices; and Class 726-Information Security. We estimate the effect using the same model specification (i.e., Eq.  3 ). As shown in columns 4 and 6 of Table 6 , all CTSA funding coefficients are statistically insignificant leading to a conclusion that the effect of CTSA funding on patents seems to be biomedical field-specific. Notably, we test only two different classes here; one should be cautious about making overly broad generalizations to other fields.

The second column in panel A of Table 5 shows the relationship between CTSA funding and the number of clinical trials conducted. The results indicate that CTSA funding is not associated with the clinical trials conducted in a region. The CTSA coefficient in the second column of Table 5 is not statistically significant at all. The results rather indicate that the number of clinical trials is affected by human capital and population. We discuss this later in Section 5 .

Considering the estimates presented in panel A of Table 5 combined, one may conclude that the effects of CTSA funding on biomedical knowledge production vary depending on the types of knowledge.

4.3.3 Three paths from CTSA funding to SBIR grants

We now present the regression results of Eq.  5 , which estimate the effects of CTSA funding, biomedical patents, and clinical trials on SBIR grants. The third column, panel B of Table 5 shows the results. The CTSA funding and biomedical patents coefficients are shown to be significant at the 0.1 level ( p values are 0.06 and 0.039, respectively). The CTSA coefficient on the SBIR grants, 0.00663, is slightly smaller than that obtained earlier in the direct relationship between them (Table 4 ). We conjecture that this result is because the effect of CTSA funding is divided into three paths from CTSA funding to SBIR grants.

The biomedical patent coefficient on the SBIR grants is 0.127 as shown in the third column, panel B of Table 5 , which means that a 1% increase in the number of biomedical patents in a region is associated with increases numbers of SBIR grants. Even though the magnitude is quite small, this result supports the knowledge spillover theory of entrepreneurship (KSTE), which links new knowledge production and entrepreneurial activity (Acs et al., 2009 ). This result provides one more piece of evidence that KSTE holds in a sectoral context like the biomedical sector.

The effect of clinical trials on the SBIR grants is negative and not significant as shown in the third column, panel B of Table 5 . In conjunction with the statistically significant relationship between biomedical patents and SBIR grants, we may conclude that the effect of biomedical knowledge on SBIR grants depends on the type of knowledge as well.

4.3.4 Path analysis

Using path analysis, we estimate the indirect effects of the CTSA funding on the SBIR grants through biomedical patents and clinical trials. Figure  11 summarizes the path results, showing the main relationships between CTSA funding, biomedical patents, clinical trials, and SBIR grants. The path coefficients from the structural equation modeling are those obtained from the regressions presented in Table 5 .

Path analysis result. Poisson option and year/MSA fixed effects used in Stata. N  = 3633 due to missing values in the human capital variable. The robust standard errors are adjusted for 381 MSAs and are in parentheses: *** p  < 0.01, ** p  < 0.05, * p  < 0.1. The solid lines indicate statistically significant relationships, while the dotted lines indicate insignificant ones at the 0.1 level

Following the discussion in Section 3 , we estimate two indirect paths between CTSA funding and SBIR grants: (1) indirect path 1—through biomedical patents and (2) indirect path 2—through clinical trials.

Indirect path 1 from CTSA funding to SBIR grants through biomedical patents is small and insignificant. The coefficient for the indirect path 1 is 0.00066, and the p value is 0.145. The coefficient is around one tenth of the direct path (i.e., 0.00663) as shown in Fig.  11 . This result is contrary to our expectation that the increased biomedical patents by CTSA funding increase SBIR grants. Indirect path 2 from CTSA funding to SBIR grants through clinical trials is also small and insignificant. The coefficient is − 0.0000011, and the p value is 0.994.

These small coefficients and high p values of the indirect paths indicate that CTSA funding has a limited effect on SBIR grants through either biomedical patents or clinical trials. On the other hand, the direct path of CTSA funding on SBIR grants was found to be larger and significant. The coefficient of the direct effect is 0.00663 and significant at the 0.1 level ( p value: 0.06). As discussed earlier, the direct path includes the effects of all outputs other than biomedical patents and clinical trials. Thus, the large differences could indicate that the indirect paths through biomedical patents or clinical trials are not the main channels between CTSA funding and SBIR grants. With the reference to the recent literature (e.g., Kim, 2019 ; Llewellyn et al., 2018 ), one might think that items like scientific publications could be the potential connector between public funding for translational research and biomedical entrepreneurship. Unfortunately, we could not test this due to the lack of relevant publication data at the MSA level.

5 Discussion and policy implications

A core finding of this analysis is that publicly funded translational research does contribute to regional biomedical entrepreneurship. As described, translational research aims to facilitate the transformation of basic science into more usable forms of knowledge. Thus, this result supports the knowledge spillover theory of entrepreneurship (KSTE), that posits that more knowledge leads to higher entrepreneurship (Acs et al., 2009 ). This is also consistent with the widespread belief in the policy community that a region would benefit from vibrant translational research activity in promoting biomedical business. It is notable that academic centers in the biomedical field observe the growth of vibrant activities at the local level and appreciate entrepreneurship (Kimberly & Berglund, 2022 ).

However, the estimated magnitude appears relatively small. We offer three potential reasons. First, we only analyzed the first 10 years of the CTSA program, thus the effect of the program might not have been fully exerted on the regional biomedical ecosystem for the years reviewed in this study. Considering the long-time span involved in biomedical development processes (DiMasi et al., 2003 ; Pisano, 2006 ), it is conceivable that it might require a longer period to observe this relationship more accurately. Second, we could have underestimated the effect by including only SBIR grants received by regional entrepreneurs while excluding other biomedical activities in a region. As noted earlier in the paper, NIH SBIR recipients account for only about 20% of all NIH SBIR applicants (NIH, n.d.-c ). If the selected and the unselected applicants used the CTSA-related outputs with the same frequency, our measurement could have underestimated the true value. Furthermore, NIH SBIR grants are only a small fraction of total financing sources for biomedical firms. Thus, those entrepreneurs who did not apply might have absorbed the effect of the CTSA program on the SBIR grants, in which case, the CTSA coefficient would again be underestimated. Third, the CTSA funding is relatively small compared to other public and private biomedical research funding. The annual CTSA funding for over 60 institutions is equal to about 500 million dollars which compares to the total annual budget of NIH amounting to 39.1 billion dollars in 2019 (Kaiser, 2018 ).

Path analysis indicates that the effect of CTSA funding is not transmitted through biomedical patents. One conceivable reason is the mismatch between the patent production and utilization time, and between the patent production and utilization location. It could be the case that entrepreneurs are not limited to using knowledge produced in their particular regions, but rather, they search for knowledge more globally. For instance, entrepreneurs might have used biomedical patents produced outside their regions from years prior.

Similarly, the effect of CTSA funding on SBIR grants through clinical trials is also weak. We consider three potential explanations. First, some clinical trials are just participating sites that are not influenced by the CTSA activities in a region. According to our data from ClinicaTrials.gov, 13.8% of clinical studies were conducted on more than 10 sites worldwide and 27 clinical studies had more than 1000 sites worldwide. Thus, the large number of participating sites could have attenuated the strength of the relationship between the CTSA and clinical trials. Second, clinical trial site selection is heavily influenced by diverse factors such as recruitment-related factors (Dombernowsky et al., 2019 ; Hurtado-Chong et al., 2017 ; Silva, 2018 ). As shown in Table 5 , the number of clinical trials is strongly associated with population density and income per capita. Thus, these factors might have influenced the clinical trials variable more heavily than other factors, such as CTSA funding. Third, when viewed from the perspective of entrepreneurs, there might be a mismatch between needed knowledge and produced knowledge. Some entrepreneurs require case-specific information for their technological developments and businesses, but the clinical trials conducted in their specific regions might not be directly relevant to their entrepreneurial activities.

Policymakers should intensify efforts to improve the utilization of knowledge produced by translational research activity. The CTSA program could, for instance, expand its educational program for young researchers regarding entrepreneurship. Policymakers can borrow key elements from the I-Corps program of the National Science Foundation supporting the commercialization of basic research. Such an expansion of the CTSA program can provide young researchers with relevant business education and allow them to get more involved in commercialization field. Local governments can also enhance their role in facilitating knowledge utilization by regional biomedical entrepreneurs. They may establish information sharing and connecting organizations adjacent to research-intensive areas to improve the flow of new knowledge between inventors and entrepreneurs. Local governments may also provide more sophisticated support to biomedical entrepreneurs beyond the current SBIR-related support, as surveyed by Lanahan and Feldman ( 2015 ). For instance, state governments may help the establishment of wet-labs for early-stage start-ups like LabCentral in Cambridge, MA, partly supported by the state government (LabCentral, n.d. ).

6 Conclusion

In this paper, we investigated the effects of translational research on biomedical knowledge production and entrepreneurship. We constructed an analytical model, positing that translational research increases biomedical entrepreneurship by increasing knowledge, namely biomedical patents and clinical trials, available to regional entrepreneurs.

The results show that CTSA funding has increased regional SBIR grants, but the impact is relatively small. CTSA funding also increases regional biomedical patents, but it does not seem to increase clinical trials conducted regionally. Biomedical patents have a positive relationship with regional SBIR grants, but clinical trials do not. Path analysis indicates that the effect of the CTSA program on regional SBIR grants is not strongly conveyed through biomedical patents or clinical trials. Based on these results, we conclude that translational research through the CTSA program has a fairly limited incremental impact on exploitable knowledge production and regional biomedical entrepreneurship. However, we will be quick to add the caveats in Section 5 .

This research contributes to the literature on the intersection of translational research and entrepreneurship by explicitly linking translational research to regional biomedical business activity. We broadened the scope of analysis in two respects: (1) from the program-recipient level to the regional level, and (2) from specific outputs to broader socioeconomic impacts (e.g., biomedical entrepreneurship). In relation to the first point, we used the metropolitan statistical area (MSA) as a unit of observation to capture regional economic activities as used by Anselin et al. ( 1997 ), Florida and Mellander ( 2010 ), and Qian and Jung ( 2017 ). We also provided empirical evidence that translational research contributes to biomedical knowledge production in a region, but the knowledge production depends on the type of knowledge. Finally, we added empirical evidence that the knowledge spillover theory of entrepreneurship (Acs et al., 2009 ) holds at the sectoral level like the biomedical field.

As a final note for future research, it should be stressed that the CTSA funding does not represent all translational research activities and that the NIH SBIR grants proxy a small fraction of biomedical business in a region. Obtaining additional data, including a wider spectrum of translational research and biomedical entrepreneurship activities, should improve the accuracy of the results. In addition, upon getting the relevant publication data at the MSA level, researchers can consider additional indirect paths from the CTSA funding to biomedical entrepreneurship.

An MSA is defined as an area with “at least one urbanized area of 50,000 or more population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties (Office of Budget and Management, 2018 ).

Fast Track allows the submission of both Phase I and Phase II together to reduce the funding gap between phases. A Fast Track submission is recognized the same as a “new” project, just like new Phase I projects in the NIH RePORTER system (NIH, n.d.-e ).

The FOA numbers used in this research: RFA-RM-06-002, RFA-RM-07-007, RFA-RM-07-002, RFA-RM-07-006, RFA-RM-08-002, RFA-RM-09-004, RFA-RM-09-019, RFA-RM-10-001, RFA-RM-10-020, RFA-RR-10-007, RFA-RR-11-004, RFA-TR-12-006, RFA-TR-14-009.

NAICS (2017 version) codes used in this research: 325,411, 325,412, 325,413, 325,414, 339,111, 339,112, 339,113, 339,114, 339,115, 339,116, 335,410, 335,417, and 541,714.

The Poisson regression has the exponential form: E ( y | x ) = exp ( x ` β ).

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Translating research for policy: the importance of equivalence, function, and loyalty

• Steve Connelly   ORCID: orcid.org/0000-0003-1758-0366 1 ,
• Dave Vanderhoven 2 ,
• Robert Rutherfoord 3 ,
• Liz Richardson   ORCID: orcid.org/0000-0002-9889-7682 4 &
• Peter Matthews 5  

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The question of how to make academic research more useful to government, and frustration over its lack of obvious use, have long been the subject of policy makers’ and scholars’ attention. These have driven the global development of institutionalised links between the two communities, while also leading to a broad consensus as to why the goal is often not realised. In order to better explain the barriers, this paper takes the concept of “translation” very literally, and proposes an innovative approach, which analyses academic and policy practices using ideas from the humanities-based discipline of Translation Studies. This enables an exploration of what constitutes good translation, and in particular of the tension between keeping faith with the original material and users’ understandable emphasis on functionality. The conclusion is that while some aspect of original research content must be maintained, what this is cannot be prescribed: the appropriate equivalence between original and translation is always context-dependent. This throws the emphasis on the relational aspects of translatorial action for promoting “good translation”. The argument follows Christiane Nord in seeing the core issue as the moral one of a translator’s loyalty to original author and user, and so also of mutual trust between academics and civil servants. This raises important questions about how such trust can be cultivated, and so finally leads to an emphasis on the importance of an endeavour shared by researchers and policy makers, which recognises and respects their different environments and the work involved in creating useful meaning from scholarly research.

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Introduction.

The question of how to make academic social science research more useful to governments has been the subject of policy makers’ and scholars’ attention for at least forty years (Weiss, 1975 , 1979 ). Yet despite increasing demands for policy makers to use research, pressures on academics to have “impact” beyond the academy, and the expansion in resources and institutionalisation of links between the “two communities” (Caplan, 1979 ), frustration over “the visible failures of evidence to influence policy” (Gluckman and Wilsdon, 2016 , p. 2) has always dogged this endeavour. The situation seems paradoxical. The very copious research on research use identifies a set of issues remarkably uniform across time, discipline and place: Weiss’s early insights are still influential; the same diagnoses and prescriptions recur across disciplines (Oliver and Cairney, 2019 ); and Court and Young’s ( 2003 ) fifty case studies, ranging from Argentina to Ukraine, suggest that experiences from the global North are broadly replicated across the world. However, while the situation is not hopeless—there is some evidence that research can influence policy (Bandola-Gill and Lyall, 2017 )—in general this research on research use itself seems lacking in influence.

Oliver and Boaz ( 2019 ) identify problems of fragmentation in the creation and sharing of knowledge and a consequent weakness in the research body (which they characterise as being poorly focused on the important issues), overall leading to ineffective impact strategies. Nevertheless they are optimistic, seeing these as essentially soluble problems, needing better capturing and sharing of knowledge, and more focused research to address enduring, genuine knowledge gaps across the entire research/policy interface from “evidence” production through translation and mobilisation, as well as gaps in terms of process and who is involved (Oliver and Boaz, 2019 ). The breadth of this apparent ignorance suggests the possibility that new ways of thinking about the process as a whole could be useful, in order to throw light on the systemic nature of the barriers implied by their enduring nature. This paper offers such a way of thinking, and we aim to show the utility of conceptualising issues in ways borrowed from the humanities discipline of Translation Studies. This analysis takes Oliver and Boaz’s agenda forward in two ways, linked by an argument for reconceptualising the idea of research translation .

The first way is to widen the analytical focus. There is a very broad consensus that research effectiveness is most efficiently promoted through personal interactions between researchers and policy makers, reflected in the quantity of scholarship on “knowledge brokers”, “boundary spanners”, “research partnerships” and so on. Oliver and Boaz take for granted that “using research well” requires “both users and producers of knowledge having the capacity and willingness to engage in relationship-building and deliberation” (Oliver and Boaz, 2019 , p. 5).They suggest more needs to be known about “who is involved in shaping and producing the evidence base”, how “evidence is discussed, made sense of, negotiated and communicated” and so “what types of interfacing are effective, and how”. While we of course concur with the normative consensus, given its empirical support, this focus draws critical researchers’ attention away from the more normal situation, which interaction is intended to replace: of researchers and users not engaging in dialogue, but respectively publishing research and drawing on these publications in the policy making process. Research on this situation, and thus prescriptions for improvement, are dominated by an unhelpfully simplistic, linear understanding of research translation (Rushmer et al., 2019 ). Therefore, we aim to broaden the scope of Oliver and Boaz’s questions: we suggest there is a need for more sophisticated analysis, which is applicable to all the ways through which research products reach “users”, whether or not interaction is involved.

Secondly, while recognising the value of social scientific contributions to understanding research use, we take our cue from another of Oliver and Boaz’s proposed avenues for exploration. They ask whether evidence “can…survive the translation process?” (Oliver and Boaz, p. 6) and suggest that understanding this could fruitfully draw on theories of communication—theories of how messages have different meanings for their originator and their audience. These are indeed important, showing how cognitive content is only part of the communication process, along with message design and materiality shaping what is actually understood by the audience (Kress, 2010 ; Connelly et al., 2015 ). Here however, we focus on a different approach to theorising the first, perhaps most obvious of these communicative elements: the fate of the cognitive content of a “text” during translation. The rationale for this is to redress a relative lack of critical focus on this content in research use scholarship. The literature appears to be divided between linear, positivistic approaches, which take as given the idea that a core meaning can be “translated” “from bench to bedside” (Woolf, 2008 ) or similar, and a critical response, which problematises this assumption and engages with the social and political aspects of evidence production and use. As a label for what happens when the outputs of research are taken up by non-academic “users”, “translation” is much-used yet clearly ambiguous (Freeman, 2009 ; Ingold and Monaghan, 2016 ; Nutley et al., 2007 ). The dominance of one conceptualisation in the simplistic, linear understanding of research use has led critical scholarship to be either sceptical of the label’s utility altogether (Greenhalgh and Wieringa, 2011 ; Penuel et al., 2015 ) or to interpret it very differently, inspired by actor-network theory (ANT) to emphasise the transformation and “betrayal” of the source inherent in translation and downplay continuities in what is “carried across” (Callon, 1986 ; Law, 1997 ; Rhodes and Lancaster, 2019 ).

Here, we argue for a middle way: that while linear understandings are clearly inadequate, moving meaningful content from one group to another—from researchers to the users in the non-academic worlds of practice and policy making—is constitutive of the very idea of research use . Our contention is that understanding the work done on, and with, that content by all those involved in this “translation” will help to explain both problems with research use and possible solutions. In this paper we show how concepts drawn from the humanities discipline of Translation Studies can aid such analysis, since for over two thousand years scholars in that discipline have been grappling with what it means to turn texts from one language into another, to move semantic content between cultures, and what is valued in the output of a translation (Munday, 2012 ). We expand on this below, but emphasise here that we are looking beyond the conceptualisations and uses of “translation” which are probably familiar to most scholars of research use. Our explicit aim is to learn from the concept’s original “home” in the humanities, and draw on arguably its least metaphorical, most literal meaning to illuminate analogous processes which take place in research use, and which are not accessible through either linear or transformative conceptualisations.

In this paper, we first set the scene by clarifying our conceptualisation of the research-policy relationship and how this relates to the existing literature and uses of the concept of translation. The bulk of the paper introduces three ideas from Translation Studies—“equivalence”, “function” and “loyalty.” These are linked by their roles in the development of ways of thinking about the desirable relationship between a “source text” and its translation, as theorists and practicing translators explored the dilemma posed by the tension between sustaining fidelity to an original source and producing a translation, which is functional for an audience (Nord, 2018 ; Schäffner, 2018 ). We show how each in turn leads to useful insights into research translation, through exploring empirical material from a pair of research projects concerned with the use of academic social science research by a UK central government ministry. As with any case study, the details are unique to their context. However, given the apparent ubiquity of the issues faced by those attempting to make research more influential, and the nature of the middle-range conceptual development presented, we suggest that the analysis has very general relevance and practical implications.

Taking research into policy making

Despite the fragmentation of the research base noted by Oliver and Boaz, systematic reviews identify a consistent set of enablers and barriers, many first identified by Caplan ( 1979 ) and subsequently widely corroborated empirically. These are principally the importance of political and institutional context, the nature and relevance of evidence, and the nature of links between academic and policy communities (see reviews by Court and Young, 2003 ; Gaudreau and Saner, 2014 ; Mitton et al., 2007 ; Nutley et al., 2007 ; Oliver et al., 2014 ; Oliver and Cairney, 2019 ). Proposed solutions are similarly consistent, with Oliver and her colleagues’ systematic review typically identifying “timely access to good quality and relevant research evidence, collaborations with policymakers and relationship- and skills-building with policymakers” (Oliver et al., 2014 , p. 1) and the “need for high-quality, simple, clear and relevant research summaries, to be delivered by known and trusted researchers” ( 2014 , p. 9).

However, the theoretical underpinning for these is seen to be insufficient to provide a secure base for improvement (Ingold and Monaghan, 2016 ; Boswell and Smith, 2017 ; Oliver and Cairney, 2019 ). The issue of how key elements of the processes are conceptualised is fundamental, in particular the issues of who is involved and the relationships between them, and how research outputs are reworked in the process of being taken into the policy process (Boswell and Smith, 2017 ; Rushmer et al., 2019 ). Two dominant, conflicting positions are clearly visible both in the practical world of research use and in academic analyses, which share frustration over the lack of research “impact” but little else.

The policy world’s self-understanding is still dominated by a linear, rational model (Boswell and Smith, 2017 ; HM Treasury, 2020 ), within which academic research has a clear role in providing evidence about the nature of problems and about “what works.” This conceptualisation underpins much of the research on how to improve researchers’ ability to “push” their knowledge into the world and on policy makers’ ability to “pull” it in effectively (Rushmer et al., 2019 ).

This model has long been criticised on the grounds that it does not accurately describe policy making or the role of knowledge and research in the process (Lindblom and Cohen, 1979 ; Weiss, 1979 ). Despite their differences, alternative analyses concur that policy making is neither rational nor linear, being complex and political, involving many stakeholders with multiple goals operating in contexts of institutional complexity (Boswell and Smith, 2017 ). In parallel, more sophisticated accounts have been developed of how research is actually used, many drawing on Weiss’s suggestions that alongside instrumental uses, research also serves an enlightenment function, through introducing ideas, which change how issues are conceptualised. It is also used politically, to bolster already-taken decisions, and tactically, when the symbolic visibility of the research process itself is what matters (Weiss, 1979 ).

At the heart of the issue of research translation is the idea of distinct groups, the producers and users of research outputs, between whom there are troublesome boundaries, which need to be traversed in some way in order for research to be used. Most of the academic and policy literature is dominated by the idea of “two communities”, which agree on the need for evidence-based policy making, but have very different cultures (Caplan, 1979 ; Wingens, 1990 ). The boundary between them is thus seen as one which presents barriers to intercultural communication, which can be overcome by aligning languages, increasing information about what knowledge is available, coproduction and other means of learning about the others’ domain, and employing individuals who can span boundaries and broker communication (Oliver et al., 2014 ). Despite its domination of the practice of research use, and research on this (Rushmer et al., 2019 ), this conceptualisation is arguably over-individualistic, and neglects more structural factors (Nutley et al., 2007 ; Wingens, 1990 ). An alternative view suggests that there are two systems, with different functions and therefore principal logics.

Wingens ( 1990 ) claims that governments will necessarily use research pragmatically and selectively, given their need to “establish collectively binding decisions” (p. 35) (that is, to govern). In contrast, academic products are generated in a system that (in principle) privileges truth, and will therefore have to be transformed in some way in order to be comprehensible and functional for government. As government researchers writing to an academic audience, Phoenix and her colleagues describe how their world does “not value their research by journal impact and funding. Instead, the value of research is assessed according to its impact in decision making” (Phoenix et al., 2019 , p. 3). This view suggests that the boundary between the systems will not be traversed simply by individuals developing greater intercultural competence. However, positions differ on its permeability. Boswell and Smith ( 2017 ) point to theories that suggest that the systems are too “autonomous” for “flows, diffusion or causality” between them ( 2017 , p. 6); in contrast Smith and Joyce ( 2012 ) point to network theories, which show that much policy making spans organisational boundaries rather easily, among groups, which share interests and values. Wingens argues for a middle ground, recognising the systemic, structural differences but suggesting that communication will be possible, since “neither scientists nor policymakers are completely predetermined by the social systems in which they have to act” and they are likely to have shared experience, insights, and language (Wingens, 1990 , p. 39). We share his position, on both the general theoretical nature of the relationship between actors and institutional contexts, and on empirical grounds: our own research and that of Phoenix and her colleagues points to exactly the kind of shared experiences that Wingens postulates.

Regardless of how the process is conceptualised, there is a consensus that intercultural communication is facilitated by dialogue of some kind across these boundaries. An extensive and varied literature explores ways in which this may be done; while we cannot explore this in detail here, we sketch out some of its contours in order to show how our work complements it through investigating what is involved in the work of translating across boundaries. This literature can be characterised by the organisational form it explores or proposes. The two principal differences are: (a) between whether a “knowledge broker” (often envisaged as a third party, a “boundary spanner”) is seen as valuable in bridging the gap between research producers and users, or if exchanges between members of the two communities are sufficient; and (b) whether the brokering task is individual or collective, to be conducted at an organisational level. The individual knowledge broker is a salient figure in the literature, typically conceived of as a person distinct from either community, with specific intercultural skills. They occupy an intermediary position, which enables them to bridge gaps and connect communities (Kislov et al., 2016 , 2017 ) exactly because the differences between Caplan’s two communities mean that “neither researchers nor decision makers are best placed to drive the translation, transfer and implementation of…research evidence” (Ward et al., 2009 , p. 2). Proponents of boundary spanners in this context suggest that they may improve both the process of creating relevant research and the capacity of users to use it (Bednarek et al., 2018 ) through a combination of working directly with the content of research as “knowledge managers”, working as “linkage agents” facilitating interchange between researchers and users, and as “capacity builders” sharing their expertise with these groups (Ward et al., 2009 ; Kislov et al., 2016 ). How the first of these is conceptualised varies, depending on how knowledge is thought to transfer from one domain to another: it may be about managing existing ideas, “identify[ing], select[ing] and obtain[ing] information from the environment and efficiently transmit[ting] it within and across the organizations according to needs” (Kislov et al., 2016 p. 474), or be a rather more interpretive role in which brokers have some contribution to creating useful knowledge (Ward et al., 2009 ).

While the value of individual brokers is widely recognised, there are also risks associated with them, principally of individuals acting as “policy advocates” rather than “honest brokers” (Pielke, 2007 ), and of creating inefficiencies by not drawing on a wider range of expertise (Bandola-Gill and Lyall, 2017 ; Dewaele et al., 2021 ). Both drawbacks can be avoided, it is claimed, by moving from an individual to a collective, organisational model (Kislov et al., 2017 ), in which members of both communities work across the boundaries. This may be formalised in “research–practice partnerships”—essentially sites of coproduction of knowledge, which require the development of new practices by all those involved as they engage in joint work across the boundaries (Penuel et al., 2015 ; Vindrola-Padros et al., 2017 ). An alternative, individual way of dispensing with third party brokers and achieving direct communication between researchers and users is through embedding researchers in user organisations (Vindrola-Padros et al., 2017 ; Ward et al., 2021 ).

All of these approaches have an obvious appeal, given the persuasiveness of the “wide and interdisciplinary literature that sees effective knowledge production and ‘research use’ as social, situated and contextually mediated processes” (Ward et al., 2021 pp. 17–18). However, none is straightforward, given the differences between communities and systems that dialogue and brokerage are intended to overcome. They all involve new “boundary practices” (Penuel et al., 2015 ), requiring time, energy and skills, delivered either by specialist third parties or achieved through researchers and users developing new capabilities. These include cultural understanding and sensitivity, and interpersonal and communicative skills (Kislov et al., 2017 ). Some of these are learnable, but to some extent they also come down to “personal characteristics and dispositions” (Vindrola-Padros et al., 2017 , p. 74). Given these factors, along with the very real structural constraints, which inhibit many academics from getting involved in knowledge transfer activities (Matthews et al., 2018 ; Oliver and Cairney, 2019 ; Oliver and Boaz, 2019 ), the norm is probably not interaction but the less resource-demanding (and less effective) processes of “pushing” and “pulling” (Rushmer et al., 2019 ) by academics disseminating their results through their own writing, and potential users gathering published information.

Common to all this literature is the taken for granted difference between creators and users of research, and thus of more-or-less easily crossable boundaries between them. While the more simplistic, linear conceptualisations focus on how best to communicate research outputs, more sophisticated approaches are concerned principally with the social processes of interaction involved in the tasks of translation, facilitation, capacity building and joint working, and not with the cognitive content of “evidence.” Yet to respond to Oliver and Boaz’s call for research on “transforming evidence translation and mobilisation”, we contend that understanding “how evidence is discussed, made sense of, negotiated and communicated” (Oliver and Boaz, 2019 p. 5), and how the manifest barriers to translation actually work (Mitton et al., 2007 ; Oliver et al., 2014 ), must involve a closer look at what is actually done to the substantive content of research outputs as they are transferred into the policy realm. In all but the most naïve conceptualisations of this, some degree of transformation will take place in order to make this transfer possible. In order to examine this more closely, we push the common trope of “translation” further than is usual.

Translation as metaphor or practice?

“Translation” has become a widely used metaphor for what happens to research in its passage from academia to users (Freeman, 2009 ). Often used in a very general sense, without theoretical commitments to what translation might actually involve (see e.g., Bednarek et al., 2018 ; Oliver and Boaz, 2019 ), the term also has a range more specific meanings tied closely to the broader conceptualisations of the nature of the research-policy relationship outlined above (Rushmer et al., 2019 ). Where this relationship is seen as simple and linear, translation is effectively a synonym for “transfer”; this conceptualisation underpins the mass of activity on improving the transfer of “what works” from research to practice (Woolf, 2008 ; Rhodes and Lancaster, 2019 ). However, just as the empirical weakness of the rational policy model has led to its widespread critique and rejection by policy scholars, so there have been two broad critical responses to this conception of “translation”.

Some scholars have followed the radical interpretation of the term emerging from actor-network theory (ANT) and science and technology studies (STS), which emphasises change, rather than the simple “carrying over” of a well-defined entity. ANT’s founder claimed that “to translate is to displace” (Callon, 1986 , p. 223)—faithful translation is impossible, as it involves a “necessary betrayal” (Law, 1997 , p. 1). Ingold and Monaghan ( 2016 ) draw on STS-influenced policy theory (Lendvai and Stubbs, 2007 ) to see research translation as something which “does not need to be entirely faithful to the original and involves a process of replication, imitation and differentiation” ( 2016 , p. 173). Rhodes and Lancaster ( 2019 ) take a more radical ANT approach, abandoning the idea of fidelity altogether and explicitly distancing themselves from the idea that anything substantive endures; for them, research outputs are “transformed”, “worked-with into different things” (p. 2).

The alternative critical response has been to view “translation” as irredeemably attached to linear conceptions of research use, and so to reject the term altogether (Greenhalgh and Wieringa, 2011 ; Penuel et al., 2015 ). Penuel et al. ( 2015 ) claim it leads to “an impoverished way of thinking about the relation of research and practice” (p. 183) and so to inappropriate proposals for closing the gap between them. In its place they favour concepts relating to “interaction” (such as partnerships) and “practice” (such as phronesis ) in order to better capture the “complex, non-linear and locally contingent” processes (Greenhalgh and Wieringa, 2011 p. 507) through which knowledge generated by research is related to practice. This reconceptualisation is inextricable from the consequent normative, practical agenda of promoting interactive approaches to enhance research effectiveness.

Both critical responses are unhelpful in two ways. Firstly, the conclusions in favour of interaction remove other practices from critical analysis. Secondly and more fundamentally, both are problematic in that at the core of the idea of research use must be a concern with that which is “carried over”. Some aspects of academic knowledge must be capable of being preserved as it is brought into the realm of policy making, since otherwise there would be no reason to value research—even if this involves more transformation than is envisaged by the everyday positivism of the policy making and implementation science communities. This criticism does not entail a retreat to the linear model, but takes us to a middle ground, which recognises the force of the critical arguments but maintains a realist commitment to the “element of underlying entity” explicitly rejected by Rhodes and Lancaster—the element captured by Steiner’s notion of “invariance within transformation” (Steiner, 1998 ). Steiner was concerned with literary translation, rather than research use: here we are proposing that useful intellectual resources for understanding the latter can be found in Steiner’s humanities discipline of Translation Studies.

This varied and complex discipline sits at the intersection of linguistics, language studies, comparative literature and cultural studies (among others), drawing on all of these for theoretical resources. Its roots are ancient, going back to classical Roman concerns with translating Greek poetry into Latin, and hard-fought early Christian controversies over Biblical interpretation (Munday, 2012 ). Throughout it has inescapably been concerned with how the content of a source is related to its translation, since while some relationship is constitutive of the idea of translation (as opposed to the creation of originals) this cannot be simple transfer, as by definition the original is not readily intelligible to the target audience (Sakai, 2006 ).

The resonances with research translation are clear, and our suggestion is that Translation Studies’ central concern with invariance within transformation complements the research use literature. Yet apart from a very brief paper by Engebretsen et al. ( 2017 ), what the discipline has to offer has been curiously ignored by policy scholars, despite Freeman speculating on its value in 2009 (Freeman, 2009 ). In a single paper we clearly cannot explore the entire discipline, nor claim to have identified all the lessons it might have for research use scholarship and practice. Rather we have selected a set of linked concepts— equivalence , function , and loyalty —which have been central to the core question of what it is that makes a good translation (Schäffner, 1997 ).

Following Siggelkow’s argument ( 2007 ) for linking conceptual development with the exposition of cases in order to show how abstract concepts are manifested in reality, we use the rest of the paper to explore how ideas from Translation Studies provide tools for better understanding “research translation”, in the context of empirical material drawn from two linked research projects. In the next section we describe the projects and methods of data collection and analysis. We then examine Translations Studies’ (ultimately unconvincing) attempts to establish equivalence between source and product as the criterion of translation quality. We follow the discipline’s turn to a focus on a translation’s function, but then suggest, following Nord ( 2018 ), that privileging function is also problematic, and show the value of augmenting this with a concern for loyalty, and so for interpersonal rather than intertextual relationships.

The projects were funded by the UK Research Councils’ Connected Communities programme (AHRC, 2012 ). Working collaboratively with researchers from the Department for Communities and Local Government (DCLG: the ministry then responsible for localism, local and community governance, planning and housing in England), the main project focused on the impact of a set of academically authored policy briefings. It also ranged more widely across the production and use of research by the civil servants. The project team comprised academics involved in producing the policy briefings (including Vanderhoven, Richardson, and Connelly), one who had not been involved (Matthews), and a DCLG social researcher (Rutherfoord). The approach was interpretive and ethnographic, exploring both how academics and civil servants understood their roles, and their actual practices. Vanderhoven, Matthews and Rutherfoord interviewed eleven civil servants and all eleven of the academics who produced the policy briefings. Vanderhoven spent three separate weeks observing and interviewing within DCLG, and we ran four workshops on research translation and use with the same groups of civil servants and academics. The interviews were digitally recorded and professionally transcribed. Detailed field notes were taken at the workshops, and by Vanderhoven to record his observations in the DCLG offices.

A follow-on project involved action research by Connelly and Vanderhoven, working with some of the same civil servants to broker connections between potentially relevant civil service policy teams and a wider set of 25 academics funded by the Connected Communities programme. Successful connections took the form of four face-to-face meetings, which were digitally recorded. We have also drawn on “on the record” email communications between these two authors and academics and civil servants, reflecting on the findings of both projects.

Our principal ethical concern was with confidentiality, both to protect individuals and ongoing policy processes. The overall management of this risk was done through continuous discussion about risk between academics and the civil servants most closely connected with the project, minimising individual identifiers in published material, and checking the use of all quotations from civil servants. At the individual level, informed consent for interviews and for the use of emails was obtained through sharing an information sheet and then confirming consent on a standard form. For meetings and participant observation, individual consent forms were not used, but agreement was obtained from all those involved at the outset. Precautions to protect individuals included sharing transcripts and other materials only among the academic team and not with the civil servants most closely involved. Overall ethics approval for the project was obtained from the University of Sheffield Research Ethics Committee.

A first inductive analysis, drawing out insights into how research use was conceptualised and practiced by those involved, and the structural constraints on this, was carried out through manual thematic coding (Braun and Clarke, 2006 ) of all the interview transcripts, field notes and reflective emails. This formed the basis for the project reports. We then reinterpreted the data using a new conceptual framing drawn from Translation Studies, for the reasons outlined above. This re-coding was thus more directed (Hsieh & Shannon, 2005 ) than the original analysis, using as core themes the three concepts taken from Translation Studies theory introduced briefly above and which structure the discussion below: equivalence, function, and loyalty. Sub-codes within this framework, such as ways of dealing with academic texts and judging research quality, were developed inductively. In presenting this we include some quotations taken from the interviews and emails to illustrate the case being made. These are relatively sparse and mainly brief, partly because in the nature of the discussions in the meetings and interviews there were rarely self-explanatory passages, and partly because of the need to protect the individuals and policy processes concerned. They are thus selected to be both representative and intelligible to illustrate and reinforce the points being made. The observational data is presented particularly sparingly: it turned out that the most useful data directly concerned with translation came from the action research, which was also the most sensitive in terms of preserving confidentiality around ongoing policy initiatives.

Taking translation theory seriously

Can “equivalence” be the goal.

We start with the concept of equivalence. On the one hand this resonates with everyday understandings of “translation” and with simple notions of research use, while on the other within Translation Studies it gets to the heart of the difficulties of defining what is carried over, and how this might be done well. Translators’ traditional focus was on preserving as much of the content of a “source text” as possible, but how to do this faithfully was a matter of longstanding debate over whether translation should be word-for-word or “sense-for-sense”—a debate which in the twentieth century matured into a focus on the concept of “equivalence” (Munday, 2012 ). Unsurprisingly, we found the expectation of equivalence (in a rather naïve sense) in good currency in the policy world. We were told by one government social researcher (GSR) that the need for “some sort of translation of these ideas into language and concepts that policymakers can understand” should be met “without losing the richness and the nuance of your findings—we don’t ever want to lose that at all”. This is straightforwardly linear: as noted above, this assumption that academic knowledge can and should be accessible through translation without loss of content is characteristic of policy makers, and built into official accounts of the role of research in policy making.

However, according to Translation Studies, achieving equivalence of every aspect of a source is impossible (Sakai, 2006 ): translation necessarily involves some degree of change, and loss, from the original. What remains “invariant” cannot even be an entirely shared meaning ( contra Freeman, 2009 ), given the different cultural and linguistic settings of the source and target texts (Sakai, 2006 ). Elaborating the concept of equivalence thus involved identifying what is significant in a source and therefore must be maintained (Nida, 1964 ). Within the academic discipline this spawned many different categorisations of equivalence, and the recognition that what was to be preserved differed between types of source text. For research-based texts the idea that a translation should provoke a similar response, an “equivalent effect”, in the target group as the original did for its audience (Nida, 1964 ), seems particularly helpful. Fundamentally such a response should be to comprehend the core ideas and trust them on the basis of some kind of warrant: the translation should make the same case as the original. Newmark ( 1981 ) adds a cultural aspect, suggesting that while a translation of a non-literary text should be accurate in conveying the content of the source, it should also be oriented towards the target audience’s linguistic, stylistic and cultural norms.

However, the fact that within the discipline there was no resolution of the multiplicity of possible choices over what equivalence could mean (Adamska-Sałaciak, 2010 ), and so no consensus over what should be preserved or abandoned in translation, points to a fundamental problem with the approach. In part this arises because the idea of equivalence rests on the challengeable assumption that meaning (as pure content) can be transferred between languages and cultures, independent of the communicative and wider context. This may sometimes be a reasonable approximation: in the research use context, a single simple quantitative “finding” may be easily transferred. For example, the “number of neighbourhood planning projects initiated” has much the same content whether in an academic publication (e.g., Wargent and Parker, 2018 ) or on an infographic poster on a DCLG office wall. Such transfer cannot, however, be generally achievable, as any interpretation of such a finding (or of any more complex idea) depends on the audience’s understanding and needs. In this example, while this number figures in academic discussions on local democracy (e.g., Bradley, 2015 ), for the civil servants its key meaning is to show that the neighbourhood planning policy was successful.

The debates continue within Translation Studies, driven by the irresolvable tension between resistance to sacrificing the “richness of the meaning” and “authority” of the source (Newmark, 1991 p. 106) and equivalence’s common-sense attractiveness, and the apparent impossibility of specifying what constitutes equivalence (Adamska-Sałaciak, 2010 ). For us, the concept usefully reinforces a focus on how translation conveys something , and prompts consideration of which aspects of a piece of research are essential for a given audience, as well of that audience’s communicative norms. Yet the lack of resolution within the discipline suggests that seeking an a priori definition of equivalence between source and target texts is ultimately unworkable, and that alternative criteria are needed to characterise and evaluate this elusive thing which is carried over. Within Translation Studies these concerns, reinforced by broader cultural and systems “turns” in the discipline, prompted a reorientation away from a linguistic approach (focused on texts themselves) towards viewing translation as a social practice driven by its function for the target audience (Munday, 2012 ).

Functional translation

The possibility of maintaining equivalent content and also being functional for the user underpins the linear conception of translation in the research use literature. In contrast, functionalist Translation Studies theorising rejects the possibility of specifying what equivalence should mean independent of context. Instead it defines a good translation principally in terms of utility—one which is adequate and appropriate, given its function for the audience (Schäffner, 1997 ). “Adequacy”, “appropriateness” and “function” are seen as always contextualised, determined by a “situation-in-culture” (Nord, 2018 ), and therefore needing to be assessed by translators as knowledgeable actors. One aspect of the context is the broader power structures and externally imposed norms within which translators work, theorised within the discipline by Chesterman ( 1997 ), Lefevere ( 1992 ) and Hermans ( 2000 ) in ways broadly similar to social scientific accounts of power within institutions, including in the context of research translation (see e.g., Freeman, 2009 ; Oliver and Boaz, 2019 ). Our focus here is therefore on an aspect less visible in social scientific accounts, but highlighted by Translation Studies with its focus on the practices of translation. This is the set of norms about the translation process itself, which govern what counts as appropriate translation (Toury, 1995 ).

The core of the functionalist approach is a hierarchical set of rules laid out by Reiss and Vermeer ( 2013 , p. 90). The first of these rules establishes the primacy of function: everything else is secondary to the utility of the translation to the end user. This includes the nature of the relationship between a source text and its translation, which is covered by subordinate rules: Rule 2 defines translation as an “offer of information” in the target language and culture “concerning” an offer of information in the source language; Rule 4 requires “coherence” between the information received by the translator, their interpretation of this and the final text. The obvious vagueness of “concerning” and “coherence” is deliberate, and allows the relationship between the content of a source and its translation to be context-dependent, determined solely by the function (or skopos , in these theorists’ terms) (Nord, 2018 ). “Equivalence” as a requirement has disappeared.

In these terms, the linear policy making model assumes a single skopos uniting impact-hungry scholars and rational, evidence-led policy makers, all seeking to give government policy the best possible knowledge base. However, in the UK central government policy context, there is a third group involved. These are the GSRs or “analysts”, a civil service cadre distinct from the policy teams, who officially “provide government with objective, reliable, relevant and timely social research; support the development, implementation, review and evaluation of policy and delivery; [and] ensure policy debate is informed by the best research evidence and thinking from the social sciences” Civil Service, 2021 ) Footnote 1 .

The GSRs are curiously absent from most accounts of the research-policy relationship (Phoenix et al., 2019 ; Hampton and Adams, 2018 ). Their official role as neutral conveyors of knowledge fits neatly into the government’s linear conception of research transfer, but our own research corroborates that of the few other researchers who have paid attention to the GSRs in showing their creative agency (Cooper, 2016 ; Hampton and Adams, 2018 ; Ingold and Monaghan, 2016 ; Nutley et al., 2007 ; Kattirtzi, 2016 ; Phoenix et al., 2019 ). They are not passive transmitters of material but have important roles as “knowledge managers” (Ward et al., 2009 ) in matching up relevant research findings with policy needs, and in turning research outputs into material usable by the policy teams. They are thus clearly knowledge brokers of a sort, part of whose role is as translators (Mulgan, 2013 ) in the strict sense of people turning material from one language into another. They see themselves as brokers (Phoenix et al., 2019 ), and often have educational and professional backgrounds outside the civil service, which provide the necessary cultural and linguistic competence for this role. Corroborating Wingens’ dismissal of the idea that “social scientists and policy-makers inhabit two separate worlds” ( 1990 , p. 33), many GSRs have academic backgrounds: as one GSR with a doctorate said to us, “Before I was a civil servant? I taught Philosophy”.

However, as civil servants the GSRs are rather unusual brokers, compared to the independent third parties envisaged by the literature discussed above. Although they act as intermediaries between academics and policy teams they are also part of the government system, and so are constrained by its orientation towards decision making (Wingens, 1990 ). So while their skills may enable them to “effectively construct a bridge between the research and policy communities” (Phoenix et al., 2019 , p. 2, quoting Nutley et al., 2007 ) and provide a more permeable boundary between academia and government than might be expected, the Civil Service Code (Civil Service, 2015 ) is very clear that they must be neutral within government: the GSRs are not neutrally positioned between the two communities and are prohibited from working as “issue advocates” (Pielke, 2007 ). Their role involves working across the spectrum from the very interactive (and resource-intensive) engagement envisaged by the boundary spanning and research partnership literature, through to “pulling in” published material (Rushmer et al., 2019 ).

Despite the complexity added by the intermediary role of the GSRs, in practice we found a broadly shared skopos across the three groups. The policy teams were genuinely interested in using research to inform their work. One characteristically described their task as "to be able to marshal the evidence for and against options that are within the sphere of the possible… [When] a minister asks “can we do X? Why can’t we do Y? What are the options for addressing Z?” …we have to come up with a list of bright ideas. Having an easily-accessible and then relatively easily-digestible evidence base to inform that thinking is valuable.”

The GSRs’ purpose was clear, and complemented the policy teams’ aspirations. It was given typical expression by two GSRs: “my ambition is really to make sure the policy team have access to the latest relevant evidence to underpin the policy details” and “we want to be as useful to [the policy teams] as possible and to make things as easy as possible. So it is trying to interpret things and what this could mean”. There were nuances in their aspirations. While for one “whether they choose to use [the ‘latest relevant evidence’] or not use it, that is at their discretion, but at least I’m doing my job to make sure they have access to it”, for others the point was to influence policy making, whether directly or through “enlightenment” effects (Weiss, 1979 ). In pursuit of making the mass of available evidence useful, the analysts deliberately offered new information, in Reiss and Vermeer’s sense, for instance valuing conceptual work such as “think[ing] a bit more creatively and put[ting] a framework around things” in order to stabilise and bring order to the policy teams’ “amorphous and changing” issues.

The extent to which academics’ skopos actually matters depends on how they engage with the policy process. Academics’ aspirations for their scholarly outputs are in principle irrelevant: as source texts, which the GSRs translate, they are simply raw material. However, researchers seeking impact often translate their own work from their academic source languages into something intended to be comprehensible and influential in the policy community—as, for example, some of this paper’s authors did with the policy briefings produced for DCLG. Their purposes may range from the most instrumental desire to communicate specific findings through to changing how the government conceptualises particular issues. Working interactively with the policy world also presupposes similar intentions to inform and influence, as academics enter conversations with policy makers and strive to be understood.

However, while we generally found this shared, broad purpose of using research to inform policy, at a more detailed level this is insufficient as a guide to achieving adequate translations. For research to be useful it must be translated to fit specific needs of the policy teams, and these are typically precise, dynamic, and unpredictable. So how might this be achieved?

Functionalist translation theory emphasises the role of the target text receiver in setting the skopos for the translation, ideally through explicit instructions defining a context-specific relationship between source and translated material. The necessity for such a “brief” seems obvious: unless a translator is very familiar with the needs and conventions of the target group, “translating without clear instructions is like swimming without water” (Nord, 2018 , p. 72). Yet academics, including ourselves and many of those with whom we worked in the action research project, are often in this situation. Without a detailed grasp of the policy fields to which they might contribute, or of the complexity of the GSRs’ and policy teams’ worlds (Oliver and Cairney, 2019 ; Phoenix et al., 2019 ), they are unable to produce useful translations of their work. Footnote 2 This is why the GSRs’ role is central, as they search for relevant academic texts and rework these for the policy teams. Their knowledge of both systems is crucial to this translation work: as well as having detailed knowledge of the policy teams’ interests, one GSR described how “I’ve always thought it’s an analyst’s job to be on top of the academic literature”, by, for instance, following relevant journals and academics on social media.

Yet even for well-informed and interculturally competent GSRs, attempting to be more proactive by producing briefs for academics may be challenging. Language and cultural issues can create barriers to communication into the academic world: writing a brief requires an understanding of that world and translation of policy needs into language intelligible to academics. So, for example, an analyst’s attempt to define for us their immediate research needs contained (from our academic perspective) a mix of genuinely researchable questions, questions which would require unfeasibly large resources to answer, and normative/evaluative questions, which are not easily researched (such as “how can we best support the creation of more integrated communities?”).

Nord’s proposed solution to the problem of inadequate briefs is clarity through dialogue (Nord, 2018 ), in the same way that interactive approaches should enhance research translation. The GSRs saw interaction as core to their effectiveness in translating for the policy teams, since “if we don’t understand the policy issues they’re facing on a day-to-day basis, we can’t respond.” Interaction across this boundary was relatively simple, particularly when GSRs and policy teams were co-located. Academics may mirror this through sustained partnerships (Penuel et al., 2015 ) or in the role of embedded researchers, able to interact regularly, both formally and informally, with users and so produce relevant research (Vindrola-Padros et al., 2017 ). Less formally, the DCLG GSRs had close relationships with a very few academics, like the one characterised as being “really good at coming in and just having a chat and offering to do seminars and that kind of thing.” As noted above, however, resource and other constraints preclude this for many, probably most, academics.

Face to face meetings are seen as a more feasible, albeit second-best, alternative for enabling academics to keep abreast of policy developments. However, neither meetings nor co-location and coproduction remove the process of translation from the process, but rather make it oral (rather than written) and immediate. Even where there is a shared language (or at least mutual comprehension) between academics and civil servants, the differences in their primary concerns (Wingens, 1990 ) still affect how they can make sense of each other. This was very visible in the meetings we organised bringing academics, GSRs and policy teams together. When (following normal practice) academics presented first, translating their own work without a detailed brief, civil servants almost always struggled to see its relevance. In contrast, when we reorganised and started with civil servants presenting their current concerns, academics generally were better able to respond by translating their knowledge instantly into something comprehensible and useful.

When research is commissioned or coproduced, the closer relationship between academics and civil servants might plausibly help the former to be more adept at translating their own work. Yet even then they may struggle to write effectively. Doing so requires making the relevance to the civil servants’ work obvious. A GSR contrasted two of our responses to the same brief: one which in setting out “principles of democratic problem solving…is potentially very helpful to guide policy”, while the other was criticised for being “out of step with current policy debates…For the unfamiliar reader, why is Truth relevant?” Where the academic authors of the latter had aimed for a major reframing of the issue, through unsettling existing conceptualisations, the GSR response was to ask “whether some more thought could go into making the policy recommendations more in tune with where local and national policy makers see their key problems at the present time”. Even clear briefs can be interpreted in ways which lead to inadequate translations of academic knowledge.

Being functional also means aligning with the civil servants’ language (Reiss and Vermeer, 2013 ), and even the most policy-oriented academics may find this hard, in part because of concerns over what is lost in translation (Freeman, 2009 ). One such scholar reflected that “you default to these modes of communication and structures of communication like the report or a journal article. Moreover, actually presenting it in a different way [to policy makers] can be quite a challenge”. Another similarly reported how, in producing a policy briefing, their team “struggled…because they were trying to keep the clever and cultivated phrases…rather than just taking little bits and saying ‘look, these are the key points, that bit doesn’t matter’”. The GSRs recognised these concerns, even as they wrestled with “interesting” work in which they could see “academics trying to protect their intellect and not distil their findings into sort of ten key bullet points”.

Overall, from this functionalist perspective the quality of a translation depends on its utility for the end user. The parallels between this idea, from Translation Studies, and the context of research use are obvious. In the latter, this means not only sharing the broad purpose of improving policy making, but also detailed knowledge of context and the possible function that translated research could serve. Empirically we saw how this was challenging for academics, and the difficulties involved in the normal, less interactive and unbriefed attempts to make research relevant show why ongoing engagement and dialogue are so important both for mutual understanding and feedback on translations. It is clear that translation takes place, however research use is organised. The difference between push/pull and interactive approaches is in who is involved, and so exactly where the boundary is across which translation takes place, and the extent to which the approach facilitates more or less functional translation.

However, functionalist translation theory has been criticised for over-emphasising the importance of the target audience’s purposes (Nord, 2018 ). While it recognises the need for coherence, and the possibility that this might be based on equivalence, the hierarchy is clear: how much equivalence, and of what, is defined by the criterion of producing a functional translation. Judging and acting on this is a task for the translator, working with the users’ needs in mind, with no in-principle restriction on creative license (Nord, 2018 ). One can easily see why academics have similar concerns about “policy-based” (Marmot, 2004 ) or “political” (Weiss, 1979 ) uses of their research—concerns reinforced by theorising which emphasises the idea of “betrayal” inherent in translation (Law, 1997 ; Rhodes and Lancaster, 2019 ).

A senior GSR summarised the ideal translation, suggesting the need to resolve the dilemma between the problems of privileging either equivalence or function:

The trick is to get the right balance between substance (showing that this is based on good evidence and/or theory), accessibility (making it easy for a busy person to get the most important messages out of a summary), and policy relevance (what does this mean for what we, or communities, actually do?) [email, original emphasis].

Trustworthiness is what matters here. Another GSR suggested that achieving this ideal does not mean that translations have to be complete: “What you’re getting across often is the kind of tip of the iceberg, and you’ll focus on that tip, but you’re also conscious that you’ve got to have a very deep foundation that underpins that advice”. This returns us to the question of what links source and target text: what might guarantee reliability, particularly in the absence of evidence contained within the translation itself?

Resolving the dilemma: function plus loyalty

Writing from within the Translation Studies functionalist tradition, Nord’s response seems apposite in the context of research translation, providing both insight and guidance. Addressing the situation in which the author’s and user’s purposes are different, she invokes the concept of “loyalty” (Nord, 2018 ). In contrast to the inter-textual concept of equivalence, this is inter-personal “responsibility” towards translators’ “partners in translational interaction”, which takes into account the cultural expectations and “legitimate interests” of all those involved—author, translator and users (Nord, 2018 , p. 117). It thus morally constrains a translator’s freedom, to produce a text “compatible with the original author’s intentions” (p. 115). Loyalty is closely bound to trust and reliability but is not the same: it is a moral orientation, which underlies, and is the precondition for, a trusting relationship.

This gets to the heart of why “relationships, trust, and mutual respect” (Oliver et al., 2014 , p. 4) are found to be so important in successful research use (Oliver and Boaz, 2019 ). This was exemplified by one GSR’s first question about us to his colleague, who was acting as our gatekeeper: “how do you know you can trust these people?” Interviewees’ reasons for trusting, even where personal relationships were absent, included a generalised faith in academia as a system oriented towards objectivity and truth (in contrast to think tanks and other “evidence” sources, which were seen as being more politically motivated and biased) (cf. Wingens, 1990 ). There was also an explicit reliance on academics’ descriptions of their research methodology, which are generally comprehensible to the GSRs, if not to the policy teams. In contrast, a generalised lack of trust in government precludes policy engagement for some academics (Pain, 2006 ), such as one who responded to a presentation of findings from this project by characterising the project team as “like Stasi informants”.

Where interaction is involved, rather than merely translation of published research outputs, the personal issues go beyond methodological competence and again take on a moral tone. Sensitivity to the other’s context, and particularly risks, were salient. Academics have to trust the GSRs and policy teams not to misrepresent their research, either with respect to its substantive claims or its validity and scope. Conversely, a policy team member told us

if you say the wrong thing to the wrong person, then that’s a vulnerable, vulnerable thing. So there’s a thing about trust there…And where we have kind of developed relationships, so, you know, we’ve worked with you before, that trust emerges over time doesn’t it? And so we know we can say things to you guys that we might not say to just anyone I walked into, on entering a university building.

So why might translators be loyal in Nord’s sense? There is obvious instrumental gain for GSRs in being seen to be purveying good research to the policy teams, but for many of those we interviewed the reasons went beyond this. Overlapping identities mattered for the civil servants who had been academics, and, crucially, there was something akin to Pain and her colleagues’ “agreed common purpose” (Pain et al., 2015 , p.11), though with a stronger moral connotation. This was captured by one GSR in the notion of a “shared endeavour”: many in both “communities” believed academics and GSRs to be participating in the same project of helping make better policy. Cultivating such an ethos is clearly supported by face to face interaction (Oliver and Boaz, 2019 ) but this is not just about simple contact: personal characteristics and dispositions are important and there is often something intangible about how effective translational relationships are created. One participant in an academic/civil service “speed dating” event summed it up: “it’s intellectual but it’s also personal: it’s ‘who do I connect with?’”.

Conclusions

Overall, our empirical findings are unsurprisingly consistent with many other scholars’ conclusions about the barriers to, and enablers of, the effective use of academic research. The purpose of this paper is, however, to further Oliver and Boaz’s agenda in two linked ways: to broaden the scope of analytical attention beyond interactive approaches (such as knowledge brokering, partnerships and so on) to cover the normal (less than ideal) conditions of research translation, and to do this by putting at the centre of our attention the content, which is translated. We have done this by drawing on concepts drawn from the humanities discipline of Translation Studies, the home of much scholarship on the nature of translation yet almost entirely ignored by the research use community. We have necessarily been selective, and hope that this paper will serve as an introduction, which will prompt other scholars to use these and other ideas and approaches from Translation Studies Footnote 3 .

Of course, the details of how people behave—in our case in one division of one UK government ministry—are context (and thus case study) specific. Steiner ( 1998 ) was right that there can be no general theory of what is done at the moment of translation : it is situated practice, varying between organisational and normative contexts, and between policies and policy fields. However, there was nothing obviously special about the context we studied, and the insights into the nature of translation are very general: the same issues can be expected to recur elsewhere (Maxwell, 2012 ). This enables progress beyond merely providing “narratives of translational praxis” (Steiner, 1998 , p. viii) to a set of middle-range concepts useful for investigating any research translation process. These are both analytical in that they should prompt questions about functions, equivalences and loyalties (and tensions between these), and normative in that these three concepts each lead to evaluative criteria. Future research could very usefully expand the range of our investigation to other fields and institutional settings, and also probe more deeply the nature of translational action in interactive settings involving partnerships and brokers.

Overall we argue that “translation” can be useful in understanding processes of research use, and should not be abandoned, as has been argued by scholars critical of simplistic, linear uses of the metaphor (Greenhalgh and Wieringa, 2011 ). Rather, drawing on the concepts from Translation Studies enables us to contribute to the already extensive research use literature, and in particular to augment the sophisticated study and promotion of interactive approaches. On the one hand, we deepen the analysis of what brokers, embedded researchers or participants in research partnerships actually do with the substantive content of research outputs. On the other, we broaden it to include the empirically dominant but much-criticised non-interactive forms of research transfer, suggesting that all “carrying across” between the academic and policy systems involves similar translation issues. What differs is exactly how the border is crossed, by whom, and what practices are possible to mitigate the inevitable challenges.

The conceptual argument can be summarised in terms of a dilemma and its proposed resolution. Thinking about equivalence between a source and its translation usefully emphasises what remains when a text is translated, and so what might be valued and justify the whole research translation endeavour. Despite its common-sense appeal, specifying what equivalence might entail in any context-independent way is problematic, and led Translation Studies scholars to appeal to function for the end-user as the guide for practice, with the appropriate equivalence between source and translation entirely context-dependent. This second horn of the dilemma is equally problematic, since in principle it allows a complete abandonment of fidelity to the content of a source. We find Nord’s moral (rather than linguistic or semantic) resolution in terms of interpersonal loyalty persuasive and helpful, both in making sense of the importance of human relationships in research translation and in highlighting a more general moral commitment of the translator to all those involved, even in the least interactive research translation practices. By this account, a “good” translation of research would be sufficiently equivalent to the original ideas to be both functional for policy and respectful of the intentions and context of the researcher.

The analysis has practical implications, though we note that the collective understanding of research use tells us that our research will not straightforwardly influence practice. So while we suggest what might be done, we are under no illusions that actioning this will be easy! These implications are the importance of mutual and detailed understanding of, and empathy with, the needs, institutional context and risks of all involved, along with broadly shared fluency in each other’s languages. This explains why face to face meetings and other forms of close interactions are so useful, and in contrast why academics translating their own material and disseminating it often do poorly both in terms of policy relevance and in building relationships. Both could be improved by paying attention to the micro-organisation of interactions to facilitate translation, and by the civil service providing readily accessible briefs on its pressing policy-relevant questions. These, along with some of the solutions frequently proposed in the research use literature (such as academics using more intelligible language) are likely to be necessary but not sufficient, unless academics also align themselves to the function of the civil service, or someone in the latter domain is able to take up academic research products and reorient them. Such intercultural communication work is difficult, and it is not obvious that academics should do it: they may well lack the specialist skills and capacity, and, despite the salience of the “impact agenda”, there are career and reputational risks attached to engaging too closely with the policy world (Oliver and Cairney, 2019 ; Oliver and Boaz, 2019 .)

In the UK context one implication of this is that the GSR profession should be more valued and more widely known within academia. More generally, investment to promote more effective research transfer should increase (and incentivise) opportunities for all those involved in research translation, as authors, translators or users, to learn about and (wherever possible) to meet the others, with the goals of promoting interpersonal relationships, generalised understanding and trust, and so of developing a basis for mutual loyalty and commitment to a shared endeavour.

Data availability

The materials generated and analysed during the current study are not publicly available, due to the sensitivity of some of the content and the need to preserve the anonymity of the civil servants involved.

More or less similar cadres provide economic and scientific advice. Our research engaged exclusively with the GSRs, and it would be useful to explore the roles of the other specialisms in brokering other forms of knowledge and evidence.

Policy fields differ. While this lack of interaction seems normal in DCLG’s areas of responsibility, in the health and education fields user-defined problems and interactive engagement seem more routine (Penuel et al., 2015 ; Vindrola-Padros et al., 2017 ; Ward et al., 2009 ).

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Acknowledgements

The authors thank the civil servants involved in this study for their time and commitment, and their willingness to go “on the record” about their practices. Jane Woodin of the University of Sheffield’s School of Languages and Cultures provided the all-important introduction to Translation Studies. We also acknowledge the support from the UK Research Councils who funded the Translation across Borders project through Connected Communities grant AH/L013223/1.

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Connelly, S., Vanderhoven, D., Rutherfoord, R. et al. Translating research for policy: the importance of equivalence, function, and loyalty. Humanit Soc Sci Commun 8 , 191 (2021). https://doi.org/10.1057/s41599-021-00873-z

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Associated data.

All data generated or analysed during this study are included in this published article [and its additional information files].

Translational research is an ideology focussed on streamlining the transition of novel research into clinical practice to ultimately benefit populations. Central to this approach is overcoming barriers to research involvement and interdisciplinary collaboration. Identifying barriers has been the subject of several studies focused on communities with large academic hospitals. The Windsor-Essex region is currently built around community hospitals which have less of an emphasis on research, employ fewer physicians holding academic appointments and generally do not provide incentivised time for research and training. In this study, we surveyed clinicians and researchers working in Windsor-Essex to gain insight into barriers to translational research important to those working in smaller sized, community-based research networks.

Using an anonymous close-ended Qualtrics survey distributed via email, we surveyed faculty members from The University of Windsor and clinical care providers from Windsor-Essex (n = 68). This included 24 physicians, 14 allied health professionals, and 30 non-clinician researchers.

Managing competing interests, lack of time, funding, infrastructure, and networks were identified by greater than 75% of participants as barriers to research involvement. 62% of physicians identified the lack of permanent post-graduate medical trainees as a barrier. Clinicians were consistently less experienced in research skills compared to others; particularly in publishing results and applying for funding (p < 0.001). Schedule incompatibility, funding issues and identifying interested collaborators with overlapping interests were identified as barriers to interdisciplinary collaboration by 80% of participants. Moreover, 46% of those surveyed were unhappy with their research involvement and these individuals were 13% more likely to perceive research as important for their career progression (p = 0.244).

Conclusions

This study identifies several important barriers to translational research in Windsor-Essex and suggests that many motivated researchers are unhappy with their current involvement. These results will inform decision making in the research community of Windsor-Essex and provides insight for communities of similar size and research capacity. Ultimately, enabling the translation of clinical research in all communities is required to ensure equitable access to cutting edge care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03097-6.

Windsor, ON is the third-most populous city in Southwestern Ontario. It is home to the 7th largest community teaching hospital, Windsor Regional Hospital (WRH), a post-acute community hospital Hôtel-Dieu Grace Healthcare, and the 14th largest university by enrollment, The University of Windsor (UoW), in the province [ 1 , 2 ]. The city also hosts the Schulich School of Medicine and Dentistry’s lone distributed medical campus and permanent postgraduate medical trainees in family medicine and psychiatry. The health research community continues to grow, with the new translational research institute WE-SPARK Health Institute recently launching in the spring of 2020. Still, compared to the largest research networks in the country, the health research capacity is limited.

Barriers to participation in health research and interdisciplinary collaboration have been the subject of many studies, often in the context of identifying barriers to translational research (TR) [ 3 ]. TR is often described as “bench to bedside” and is focussed on streamlining novel research findings into widespread clinical changes [ 4 , 5 ]. Central to this ideology is a multidisciplinary approach, requiring the input of both clinical care providers and graduate trained researchers. These two groups often experience different barriers to their research goals; likely due to their different educational backgrounds and professional responsibilities [ 6 ].

The generalizability of previous studies are questionable for the following reasons: past studies often take place in large, academic centers [ 3 , 7 , 8 ], they often only include those heavily involved in research [ 3 , 7 , 9 , 10 ], and the resulting barriers are broad and difficult to interpret [ 3 ]. Unlike academic centres, community centres like WRH have less research funding, employ many physicians that lack academic appointments and do not generally provide incentivised protected time for research or training; though a precise definition is lacking [ 11 ]. While most clinical and translational research conducted in Canada takes place in these large academic centres, the benefits of conducting research in community hospitals like WRH is substantial for both researchers and patients [ 11 ]. Given the limitations of previous work, we set out to examine which TR barriers are important to a smaller research community and community-based hospitals. We surveyed clinicians and health researchers from Windsor-Essex and specifically examined participants’ confidence in research tasks, opinions on TR, barriers to health research participation and interdisciplinary collaboration. We identified several major barriers to research and collaboration in our community and found that those struggling with their research involvement perceived barriers differently. Our findings will inform decision-making in the Windsor-Essex research community and contribute to the understanding of TR barriers in smaller centres.

Survey design

We designed an Internet-based survey using Qualtrics, an established survey provider, to examine research experience, opinions on TR, barriers to participation in health research and barriers to interdisciplinary collaboration in Windsor-Essex. The survey was anonymous and close-ended. We first asked participants about their professional backgrounds; with a focus on identifying those with clinical care responsibilities. We also asked participants about their satisfaction with research involvement, faculty appointments, time spent on research and research area. We then divided participants into the following groups: clinical care provider vs non-clinicians, and happy vs unhappy with current research involvement. Much of the questionnaire was adapted from previous studies that explored these barriers in other locations [ 7 – 9 , 12 ]. Others were designed by the research team based upon their experiences working in Windsor-Essex. We choose not to collect certain demographics, such as age, gender, and specific research area, as these are beyond the scope of the study and could potentially allow for individual participants to be discerned in our relatively small research community.

We provided participants with a list of barriers to research participation and interdisciplinary collaboration identified in previous studies [ 8 , 12 ] and asked them to rate the impact of each barrier with the following Likert Scale: Not a barrier (0), Moderate Barrier (1), Major Barrier (2). Clinical care providers were asked about barriers to collaborating with non-clinical care providers and vice versa. Participants that have experienced such collaboration in the past were also provided a list of benefits and selected which benefits they experienced. We also asked participants to rank their confidence in a variety of research tasks (adapted from [ 12 ]) using the following Likert Scale: No (1), Little (2), Some (3), Moderate (4) and Very (5) Experienced.

To assess participants opinions on research productivity and TR, we provided participants with a list of research metrics and achievements (adapted from [ 7 , 9 ]) and asked them to choose no more than 4 that were relevant to their careers. We then provided them with a series of statements on TR and asked them to rank their agreement using this Likert Scale: Strongly disagree (1), Disagree (2), Neither agree or disagree (3), Agree (4), Strongly Agree (5) .

Study recruitment

Between July 2nd 2020 and Nov. 30th 2020, participants were recruited via email and community newsletter. Standardized emails including the survey link were sent to faculty members from the Faculty of Arts and Humanities, Kinesiology, Engineering, Science and Nursing at the UoW by their respective deans. Clinical care providers working at Windsor Regional Hospital were distributed emails via the Research Office with permission from the Chief of Staff. The link was also included in newsletters at WRH, UoW and WE-SPARK.

Participants were included in the study if they satisfied the following criteria: (1) the participant worked in Windsor-Essex, (2) was a clinical care provider or had research interests that “May have implications for healthcare policy, clinical care, treatment development or clinical education”, and (3) completed greater than 2/3 of the survey.

Statistical analysis

Data from the questionnaire was imported into Excel 2020 (Microsoft Corporation, USA). Descriptive statistics (Likert values, proportions, frequency counts) were used to capture demographic data for the study population; as well as perceptions on barrier to research and interdisciplinary collaboration, confidence in research skills and opinions on TR. Statistical comparisons between groups (clinical care providers vs non-clinicians; unhappy vs happy with research involvement) was performed with an independent, unpaired t-test. Results were considered significant if p < 0.05.

Ethics statement

This study received clearance from the Research Ethics Board of the UoW and WRH (REB# 37036). Informed consent was obtained from participants before they began the survey.

Study participants and response rate

To assess which barriers to health research participation and interdisciplinary collaboration were important to those working in Windsor-Essex, we recruited clinicians from the area and faculty members from UoW to participate in our survey. 88 respondents completed some of the survey. 20 did not meet the inclusion criteria, leaving 68 participants that were included in the study. 10 of the included participants submitted partial surveys that were greater than 66% complete, the rest were completed in entirety. Amongst faculty members from the targeted faculties at the UoW, we received 40 responses from an estimated 447 members [Response Rate (RR) = 9%]. 24 physicians from WRH responded out of an estimated 485 physicians (RR = 5%). 14 allied health professionals also contributed, but we are unable to estimate a total number of these professionals that were recruited. A total RR is likely higher than each individual RR combined due to physicians that are also UoW faculty.

As seen in Table ​ Table1, 1 , 38 (56%) of the participants were clinical care providers and 30 (44%) were not. Of the clinical care providers, 24 (63%) were physicians and 14 (37%) were allied health professionals, including nurses, social workers, and physiotherapists. 87% of clinical care providers spent less than 20% of their time on research compared to only 10% of non-clinicians. 90% of participants that were non-clinicians had a graduate degree, compared to only 43% of clinicians. Participants carried out a variety of research tasks and had a variety of research interests, but clinical research was the most common, particularly amongst clinical care providers (Additional file 1 : Figure S1).

Characteristics of survey respondents n = 68

Research satisfaction and career development

To assess whether participants were satisfied we asked whether they agreed with the statement “I am happy with my current research involvement.” 46% of participants were unhappy with their current research involvement, including 53% of clinical care providers and 37% of non-clinical care providers (Table ​ (Table1). 1 ). We then asked participants whether they felt research was important for their career progression (Additional file 1 : Figure S2). Interestingly, those that were unhappy with their research involvement were more likely to state that research was important for their career progression when compared to those that were happy with their current research (83% vs 70%, p = 0.244; Additional file 1 : Figure S2).

Barriers to research participation

To determine which barriers to research participation were important to those working in Windsor-Essex, respondents were asked to rank the impact of various barriers using a Likert Scale (Fig.  1 A). Managing competing activities, lack of time, funding and infrastructure were the most impactful barriers in the opinion of the participants (Fig.  1 A); with more than 85% of participants identifying each as a moderate or major barrier. Clinical care providers and non-clinicians perceived the impact of each barrier as relatively equal, with the largest discrepancy being that non-clinicians perceived recruiting and training research staff as significantly more impactful than clinicians (p = 0.0135; Fig.  1 A). Those that were unhappy with their research involvement identified lack of institutional support and mentorship as significantly more impactful than those that were happy with their research involvement (p < 0.05, Fig.  1 A).

Barriers to research participation and experience in research skills. Participants were divided by clinical responsibility (clinical care provider vs non-clinician) and satisfaction with research involvement (happy vs unhappy). Significant differences between groups determined by unpaired t-test; error bars represent SE. A Impact of barriers to research participation rated by mean Likert scale: Not a barrier (0), Moderate barrier (1), Major barrier (2). B Self-perceived experience with various research tasks, rated by mean Likert scale: No (1), Little (2), Some (3), Moderate (4) and Very (5) Experienced. C Impact of absence of permanent postgraduate medical trainees on physician research goals; measured by proportion of physicians (n = 24) who strongly disagreed, disagreed, neither agreed or disagreed, agreed or strongly agreed with the above statement

We also asked participants how experienced they were in a variety of common research tasks using a Likert Scale (Fig.  1 B). Clinicians perceived themselves as significantly less experienced than non-clinicians in all research tasks we included (p < 0.01, Fig.  1 B). The largest differences were in publishing results, applying for research funding, and writing research protocols (Fig.  1 B). There was a similar trend when participants were divided by their satisfaction with research involvement, with those that were happy generally feeling more experienced than those who were unhappy. However, the differences were not as large (Fig.  1 B). 62% of physicians surveyed also felt that the lack permanent postgraduate trainees in the area was a barrier to their research goals (Fig.  1 C).

Barriers to interdisciplinary collaboration

We asked clinicians to rank the impact of various barriers to collaboration with non-clinicians and vice versa using a Likert scale. Most barriers were equally impactful to clinical care providers and non-clinicians, with greater than 80% of participants identifying schedule incompatibility, lack of funding, identifying interested collaborators and lack of shared infrastructure as barriers (Fig.  2 A). Clinicians felt that lack of institutional support was more impactful than non-clinicians, and it was the most impactful barrier identified by this group (p = 0.0115, Fig.  2 A).

Benefits and barriers to interdisciplinary collaboration. Participants were divided by clinical responsibility (clinical care provider vs non-clinician). Significant differences between groups determined by unpaired t-test; error bars represent SE. A Impact of barriers to interdisciplinary collaboration rated by mean Likert scale: Not a barrier (0), Moderate barrier (1), Major barrier (2). B Proportion of participants (with experience in interdisciplinary collaboration) in agreement with each benefit

38% of clinical care providers in our study have collaborated with non-clinicians on research tasks and 40% of non-clinicians have collaborated with clinicians. Interestingly, all barriers to interdisciplinary were ranked as more impactful by those that had experienced interdisciplinary collaboration (NS; Data not shown). Participants who have experienced interdisciplinary collaboration were asked to identify what benefits they experienced (Fig.  2 B). Access to expert opinions/new knowledge, different skills and additional funding were the most frequently cited benefits, while increased publications was the least frequently identified benefit (Fig.  2 B). Non-clinicians were significantly more likely to cite improved access to patient data or tissues as a benefit when compared with clinicians (31% vs 12%; p = 0.049). This was the only significant difference between the two groups.

Opinions on research productivity and TR

To assess the participants’ opinions on research productivity, we asked each to identify which metrics and achievements were important to them. Traditional achievements, including conference presentations and publications, were identified by more than 50% of our participants as important for career progression (Fig.  3 A). Clinicians were more likely to identify first author publications as more important than other publications (Fig.  3 A). Publications that were neither first nor last author were identified as important by non-clinicians more so than clinicians (70% vs 40%; p = 0.0189). Non-clinicians were significantly more likely to use number of citations and awards/grants to measure the impact of their research (p < 0.01; Fig.  3 B).

Opinions on Research Productivity. Participants were divided by clinical responsibility (clinical care provider vs non-clinician). Significant differences between groups determined by unpaired t-test; error bars represent SE. A Proportion of participants identifying research achievements that were important for their career. Participants were able to select no more than four from the list provided. B Proportion of participants using the listed research metrics to measure the impact of their research. Participants were able to select no more than four from the list provided

We next asked participants how confident they were in their understanding of TR. 46% of participants were either confident or very confident in their understanding of TR (Fig.  4 A). Clinicians were more likely to lack confidence than non-clinicians (43% vs 11%; Fig.  4 B). Using a Likert scale to rate agreement with statements about TR, we found that fewer clinical care providers felt that their research would be considered translational or had the training to participate in translational projects as compared to non clinicians (p < 0.001; Fig.  4 B). Most of the participants were unsure whether their research required translation, however, clinicians were less likely to feel that their research goals required translation (p = 0.0029; Fig.  4 B).

Opinions on Translational Research. Participants were divided by clinical responsibility (clinical care provider vs non-clinician). Significant differences between groups determined by unpaired t-test; error bars represent SE. A Participant confidence in understanding of translational research. B Participants were asked whether they agree with the listed the statements. Agreement of each group was measured via mean Likert scale: Strongly disagree (1), Disagree (2), Neither agree or disagree (3), Agree (4), Strongly Agree (5)

In this study, we surveyed health researchers from a mid-sized comprehensive University that lacks a full medical school campus to assess the barriers to research participation and interdisciplinary collaboration. To our knowledge, this is the first such study in a smaller, Canadian research community that contains only community hospitals. Key findings are summarized in Fig.  5 . We also sorted our findings into 3 of the 5 thematic barriers identified in the narrative synthesis by Fudge et al. [ 3 ]; including “Research Process”, “Interdisciplinary Collaboration” and “Concepts of Translational Research” (Fig.  5 ).

Barriers to Translational Research in Windsor, ON. Summary of barriers to translational research important to clinicians and non-clinicians surveyed in our study (n = 68). Barriers are sorted into three of the thematic barriers to translational research broadly identified initially by Fudge et al . [ 3 ]; including “Research Process”, “Interdisciplinary Collaboration” and “Concepts of Translational Research.”

We included participants with various research interests from clinical and non-clinical backgrounds across a range of disciplines; reflecting the growing group of professionals that contribute to health research [ 4 ] (Additional file 1 : Figure S1, Table ​ Table1). 1 ). 46% of study participants felt unhappy with their current research involvement and 83% of these individuals felt that research was important for their career progression (Additional file 1 : Figure S2)(Table ​ S2)(Table1). 1 ). This suggests that there is a group of motivated but dissatisfied researchers in this community. This group felt that lack of mentorship and institutional support were significantly more impactful than those that were happy with their current research involvement (Fig.  1 A). The data agrees with a survey of Canadian respiratory workers, which suggested that lack of mentorship was a more important barrier for those not involved in research when compared to those actively engaged in research [ 12 ]. Clinicians and non-clinicians ranked barriers to research participation relatively equally (Fig.  1 A), with more than 85% of participants selecting managing competing activities, lack of time, funding, and infrastructure as a barrier. These barriers have been frequently cited as important to researchers in studies from other geographic areas [ 8 , 10 , 12 , 13 ].

Study participants with clinical responsibilities were significantly less confident than non-clinicians in research skills, with the largest disparity being in applying for research funding and publishing results (Fig.  1 B). Previous studies have also suggested that this lack of confidence could be a barrier to participating in interdisciplinary collaboration, particularly with colleagues who are more research focussed [ 9 , 12 ]. Clinicians and non-clinicians generally agreed on the benefits and barriers to interdisciplinary collaboration (Fig.  2 A); with 80% identifying schedule incompatibility, lack of funding, identifying interested collaborators and lack of shared infrastructure as a moderate or major barrier. Both groups also tended to find traditional research metrics, such as publications, presentations, and citations, as the most significant metric of research productivity (Fig.  3 ). This is in contrast to previous work which suggested that clinical researchers found incorporation into clinical guidelines as more important than publications [ 14 ]. Furthermore, non-clinicians were more confident in their understanding of TR and largely felt that they had the necessary training to contribute to TR. Non-clinicians also felt that their research was either already translational or requires translation (Fig.  4 ).

A strength of this study is our broad sample that is inclusive of participants with different research commitments, backgrounds, and clinical responsibilities. However, this sampling strategy did lead to a relatively low response rate and potential response bias. Physicians in particular are known to be difficult to survey, with previous studies reporting response rates as low as 2.7–11.4% [ 15 , 16 ]. In addition, our strategy likely selects for participants with an interest in translational research, which may be a smaller proportion of individuals in a community-based research environment. By dividing participants based on their satisfaction with their research involvement, we assured that our findings weren’t biased by those with particularly favourable or unfavourable views of the research community.

While the close-ended nature of the survey makes drawing conclusions difficult, our data suggests that mentorship and assistance with obtaining grants would benefit researchers in Windsor-Essex. Plans to develop a database of interested researchers to aid with identification of interested collaborators are already underway in our community. Other barriers, such as managing competing activities and schedule incompatibility, are more difficult to address as they are ingrained in the culture of various careers. Future studies should allow for narrative responses to better identify problems and potential solutions.

In summary, we found that while clinicians and non-clinicians from Windsor-Essex perceive similar barriers to research participation and interdisciplinary collaboration, they differ in terms of their confidence in research skills and their opinions on TR. Lack of mentorship, and institutional support were more important barriers to those that were dissatisfied with their current research involvement; but future study is needed to better define these barriers. These findings will inform decision making in Windsor-Essex and similarly sized research communities that are often neglected in these studies.

Acknowledgements

We acknowledge the support of WE-SPARK Health Institute in securing the partnerships that made this work possible. We acknowledge all the participants in this study, given that time is a major constraint for research their time was most appreciated on this work.

Abbreviations

Authors’ contributions.

JS was responsible for data/statistical analysis, survey design, and was a major contributor in writing the manuscript. LP was principal investigator on the project and served as faculty supervisor and covered manuscript costs. LP, KM and IW contributed to the study design, data analysis and editing of the manuscript; they also facilitated survey distribution. KW developed our graphical summary displayed in Fig.  5 . All authors read and approved the final manuscript.

This project was supported by Schulich-UWindsor Opportunities for Research Excellence Program (SWORP) sponsored by the University of Windsor and the Schulich School of Medicine and Dentistry, Western University (Justin Senecal). Publication costs were covered by a Canadian Institutes of Health Research (CIHR) grant #145983 (Lisa A. Porter).

Availability of data and materials

Declarations.

This study received clearance from the Research Ethics Board of the UoW and WRH (REB # 37036). Informed consent was obtained from participants before they began the survey.

No identifying data was collected in the study, but participants consented to the use of the data in publication before they were provided access to the survey.

The authors declare that they have no competing interests.

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Contributor Information

Indryas Woldie, Email: [email protected] .

Lisa A. Porter, Email: ac.rosdniwu@retropl .