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Translational research is messy and dynamic. In this blog, Director of the MSc in Translational Health Sciences, Professor Trish Greenhalgh, introduces us to the MSc and discusses the need for such an interdisciplinary and applied programme that explores downstream elements of knowledge translation, such as human actions and interactions, to improve the success of efforts to implement innovations in complex healthcare systems.

2022 cohort of students on the MSc in Translational Health Sciences © Free Pix

This blog introduces the concept of translational (health) sciences—broadly speaking, approaches to getting research innovations into practice and policy—and explains why we developed an interdisciplinary MSc course to teach this topic. I’ll introduce some of the many different academic disciplines that inform this highly applied field of practice, including ways of influencing human behaviour, introducing and evaluating change in organisations, influencing policy, designing and routinising technologies and ensuring that innovations generate value for all stakeholders. I’ll also explain why we use small-group discussions and real-world case studies as core learning methods.


October 2020—mid pandemic—was perhaps not the best time to launch a new MSc course at the University of Oxford. But launch it we did, with 18 students drawn from 12 countries including UK, USA, Canada, South Africa, Kenya and more. Their professional backgrounds also varied widely and included medicine, allied professions such as physiotherapy, industry, policymaking, law and advocacy. They ranged in age from mid-20s to early 60s. Students worked fully online initially, but once covid restrictions were lifted they convened in Oxford every few weeks for an intensive week of lectures, seminars and small group work.

The students were the first cohort of the MSc in Translational Health Sciences—a course designed to equip them with the confidence, theoretical knowledge and practical skills to help ensure that innovations (such as new research findings or new technologies) are taken up and used, and also mainstreamed in organisations and policymaking. One or two of the students expected to be given a ‘toolkit’ or ‘framework’ which would enable them to effect change in a predictable and standardized way.  Most, however, already knew that there is no such toolkit. Rather, the students were here to learn ‘rules of thumb’ (approaches that tend to work but are not guaranteed to, and which need adapting to each new context) and gain practical wisdom from sharing experiences and working in interdisciplinary small groups. 

The first of those students have now graduated – you can see short videos of some of them here and here. The remainder of this blog introduces translational health sciences as an academic discipline—that is, one which, as well as being highly applied, draws on conceptual and theoretical work from a range of different traditions.   

Translation – of what, for whom, and why?

The term ‘knowledge translation’ originated in the early 2000s as an envisaged solution to a problem that was becoming increasingly common—research discoveries were slow to be taken up into clinical practice, and basic science research (especially the development of new drugs and technologies) was increasingly misaligned with what clinicians did, what patients wanted and needed, and how health services were organised and delivered.

For example, a new diagnostic test might become available for disease X using a miniaturized point-of-care technology (something akin to the lateral flow tests that we all learnt to use during the pandemic, but perhaps using a drop of blood obtained by a fingerprick). This new test might have the potential to transform the way care is delivered for disease X and save money overall. Instead of being referred to a specialist, who sends a blood test away to a lab, the patient could just visit their general practitioner or street-corner pharmacist, or even do the test themselves using a mail-order kit. But quite probably, these changes will never happen! The point-of-care test may exist, and it may have all the necessary regulatory approvals, but nobody invests in it. GPs don’t offer it. Service models remain the same. The entrepreneur who developed the new test finds they cannot sell it. Shareholders lose their investments. Patients continue to make long journeys for medical appointments that could have been avoided. Precious blood (and sweat and tears) continues to be wasted.

Why do situations like this come about—and why are they so common?  Perhaps, in this example, because nobody hears about the new test, or because GPs are too busy to offer it, or because health service funding for disease X is historically allocated to a hospital budget and reallocating that funding to primary care is logistically or politically tricky. Furthermore, if you try to find out why the new point-of-care test for disease X failed to become part of business-as-usual, you’ll get a lot of stories from a lot of people, and they probably won’t agree where the problem lies!  And the barriers to getting the test mainstreamed in one locality may be quite different from the barriers in another locality. We will need to examine fine-grained detail about people, contexts, infrastructure, historical path-dependencies and more. Where might we start in analysing this case?

A translational pipeline?

Back in the 2000s, a new programme of ‘translational’ research was deemed necessary to identify the causes of the misalignments such as these in the medical innovation ‘pipeline’ and address what has been termed the ‘know-do gap’ at each stage. A sequence of translations was depicted: T1 – from bench science (e.g., early drug discovery) to clinical trials; T2 – synthesising research findings (e.g., in systematic reviews and guidelines); T3 – addressing the behavioural, organisational and policy challenges of getting research findings into practice; and T4 – using research findings to inform health systems improvement, especially from a global health perspective (Figure 1).

Figure 1: Assumed stages in the knowledge translation ‘pipeline’

The first stage (‘T1’) in this conventional ‘translational pipeline’ involves the laboratory researcher extending his or her scientific repertoire so as better to interface with the world of clinical trials—a process sometimes known as clinical or upstream translational research. The second (‘T2’) stage refers to the desk research that generates systematic reviews, guidelines, decision support tools and other summaries of primary evidence—a domain generally known as evidence-based medicine (or healthcare). These stages are covered in other MSc courses. 

The ‘T3’ and ‘T4’ stages in Figure 1 are where social scientists tend to get involved, studying what might be called downstream translational research, which we define as the study of the human, organisational and societal issues that impact on the adoption, dissemination and mainstreaming of research and other discoveries, and the harnessing of such discoveries to provide effective, efficient and equitable healthcare. It is these latter (downstream) stages of translation that we mainly address in this MSc.

Challenging the pipeline

One of the first things we invite students to do on this MSc is problematise the whole idea of the ‘translational pipeline’ and the T1 to T4 sequence shown in Figure 1. Whilst the pipeline metaphor is a useful starting point for thinking about how research (and innovation more generally) gets into practice, it implies a linear, one-way process starting with basic research and ending with some kind of benefit to the patient or the health system. In reality, progress from the laboratory (or the technology design studio) to the bedside may be circuitous and involve several false starts. Only a fraction of innovations makes it past early laboratory studies (a new diagnostic test, for example, may fail to differentiate people with the disease from people without it). For numerous reasons, not all innovations can be tested in the randomised controlled trial format beloved of evidence-based medicine (and even when they can, the results of such trials may be delayed or contested). And there is often vigorous back-and-forth dialogue between different parts of the system (basic scientists studying a rare disease, for example, might work closely with patients affected by that disease and shape their studies accordingly).  For all these reasons, the actual ‘pipeline’ is, at best, non-linear, two-way and leaky. 

One key defining feature of the downstream elements of knowledge translation is that they involve human actions and interactions. At an individual level, people are not passive recipients of innovations. Rather (and to a greater or lesser extent in different individuals), they seek innovations out, experiment with them, evaluate them, find (or fail to find) meaning in them, develop feelings (positive or negative) about them, challenge them, worry about them, complain about them, work around them, gain experience with them, modify them to fit particular tasks, and try to improve or redesign them—often through dialogue with other users. We need to study the psychology of the human beliefs, attitudes and behaviours involved, and consider how best to try to change them. We also need to feed insights from behavioural science into the design of innovations to make them more likely to be accepted and less likely to be abandoned.

At the interpersonal level, relationships between stakeholders are all-important (indeed, some have called knowledge translation a ‘contact sport’). Different groups—patients and doctors for example, not to mention finance directors and entrepreneurs—are likely to have different perspectives and agendas. The science of knowledge translation includes developing ways to ‘speak to’ these different interest groups—for example how to make research findings relevant (timely, salient, actionable), legitimate (credible, authoritative, reasonable), and accessible (available, understandable, assimilable) to different audiences and take account their different points of departure (assumptions, world views, priorities). If different stakeholders have widely differing priorities and if they interpret evidence differently, there is likely to be conflict—and action will stall until this conflict is addressed. Some contemporary approaches to knowledge translation involve the co-creation of knowledge by researchers and community partners—a methodology that needs careful attention to ground rules, power dynamics and governance in order to reduce or contain conflict between groups which frame a problem in fundamentally different ways.

Interdisciplinary research needed

The different disciplinary perspectives described briefly above illustrate that translational health research is a broad and applied field which requires an interdisciplinary approach and a practical focus. To use terminology coined by Isaiah Berlin, the successful translational researcher tends to be a ‘fox’ not a ‘hedgehog’. To quote from Tetlock (2005, p. 2):

What experts think matters far less than how they think. If we want realistic odds on what will happen next, coupled to a willingness to admit mistakes, we are better off turning to experts who embody the intellectual traits of Isaiah Berlin’s prototypical fox – those who ‘know many little things’, drawn from an eclectic array of traditions, and accept ambiguity and contradiction as inevitable features of life – than we are turning to Berlin’s hedgehogs – those who ‘know one big thing’, toil devotedly within one tradition, and reach for formulaic solutions to ill-defined problems”.

In the messy and dynamic world of translational research, the ‘fox’ (imaginative, intellectually broad, tolerant of ambiguity) generally outsmarts the ‘hedgehog’ (conceptually narrow, doggedly rule-following, convinced that there is one key ‘robust method’ that will provide all the necessary insights). The ‘hedgehog’ translational researcher may be tempted to embrace the ‘pipeline’ metaphor because it implies a simple, mechanical and linear link between basic discoveries and their uptake and application and is fundamentally a technical process involving—for example—devices to be field-tested, approvals obtained, guidelines written, people educated and ‘barriers’ overcome. Pipeline framings imply specific points in the translation process where more-or-less standardised interventions can be introduced, and much translational research involves developing and testing such interventions.

But the intellectually multi-tasking ‘fox’ researcher rejects the pipeline metaphor because he or she recognises that the reality of uptake and implementation is a messy, socio-political, uncertain, conflict-ridden, protocol-defying and distinctly non-linear process occurring across multiple sectors (and mostly outside the university’s walls). These include industry, front-line clinical settings, third sector (e.g., patient groups) and policy contexts. It is an unfortunate irony that a research proposal offering a clear and direct link between an empirical study and a specific downstream benefit to society may be looked on more favourably by funding panels than one that is honest that the links between research and impact are, in general, unclear, indirect and non-specific (achieved, for example, through complementary assets such as strengthened cross-sector relationships and mutual understanding).

This non-linear dimension of knowledge translation may be poorly understood by biomedical scientists who have been steeped in ‘pipeline’ metaphors. Contrast this with the definition of research impact from a faculty of social science, which emphasises its non-linear and relational dimensions:

“A research impact is a recorded or otherwise auditable occasion of influence from academic research on another actor or organization. [...] It is not the same thing as a change in outputs or activities as a result of that influence, still less a change in social outcomes. Changes in organizational outputs and social outcomes are always attributable to multiple forces and influences. Consequently, verified causal links from one author or piece of work to output changes or to social outcomes cannot realistically be made or measured in the current state of knowledge. [...] However, secondary impacts from research can sometimes be traced at a much more aggregate level, and some macro-evaluations of the economic net benefits of university research are feasible. Improving our knowledge of primary impacts as occasions of influence is the best route to expanding what can be achieved here.”

(LSE Public Policy Group, 2011)

Research questions in translational health—and how this MSc addresses them

Translational science is a complex business, as the examples above illustrate. There is no one research discipline that will improve the success of your efforts to implement innovations in healthcare. Indeed, the skills of ‘downstream’ translational science are highly applied and pragmatic in nature. Broadly speaking, you will need to take an interdisciplinary approach which addresses—among many other things—the following kinds of question. Each of these draws on a different academic discipline or disciplines and is covered in a different module in the course.


  1. How can we influence individuals’ behaviour e.g. encourage clinicians to follow guidelines or patients to comply with treatment? (Main discipline: psychology).
  2. How do drugs, medical devices and other innovations generate value (and why do they sometimes fail to do so)? (Main discipline: economics).
  3. How might the prevailing regulatory environment enable or constrain innovation? (Main disciplines: regulatory science and law).
  4. How should we decide what is right and reasonable in knowledge translation? (Main discipline: moral philosophy).
  5. Why do some organisations adopt innovations readily while others do not? How might we change the structure and climate of organisations to support innovation? (Main discipline: organisational science).
  6. How can the policy process support or hinder the introduction of innovations? How might academic institutions work with industry and government to optimise the acceleration of research and innovations into practice? (Main disciplines: policy studies, higher education studies, business studies).
  7. How might the involvement of patients and citizens help generate research findings and innovations that are acceptable and useful to the public? (Main disciplines: political science, public policy).
  8. How can we improve the design of technologies and the care process and pathways they support? (Main disciplines: design, socio-technical studies).
  9. What are the implications for global knowledge transfer of social and cultural differences between settings and health systems? (Main disciplines: cross-cultural studies, development studies).
  10. Given that scientific discovery is itself a social act with its own shared assumptions, meaning-systems and historical path-dependencies, how might these ‘cultural’ aspects of science influence the findings that are generated and the credibility and social and political acceptability of these findings? (Main discipline: science and technology studies). 

Each of these questions maps approximately to the syllabus of a different module in the MSc.

The case study as a unit of learning

Whilst ‘models’ and ‘frameworks’ for supporting translational health research exist, our own (more social science-informed) view is that the most useful way of drawing together interdisciplinary insights is the mixed-method case study, oriented to producing depth and detail about specific translational challenges but stopping short of universal models or formulae (Flyvbjerg, 2006). The in-depth case study includes all the rich, context-dependent detail that allows a problem to be explicated, echoing the complexities and contradictions of real life - including the human reactions, relationships and conflicts that so often determine whether an innovation will ever be translated into practice.  

Selecting which of the above questions to prioritise, and applying it in practice, comes with practice. Learning occurs where the rubber meets the road—that is, where discussion around a real-world case allows particular theoretical lenses to emerge as salient for that case. This is why we place strong emphasis on analysing and discusssing real-world case studies in small groups. Every case study is different theoretically as well as in terms of topic (non-uptake of point-of-care diagnostic tests will have a different combination of interacting causes than a failed national IT programme, for example). In some cases, the behavioural scientist in the group will have the most useful things to say; in other cases, it will be the lawyer. Hence, when students from different academic backgrounds and different work experience come together and discuss a case, theoretical insights combine with practical wisdom and are brought to bear on real-world translational challenges.

Structure of the course

You can take the MSc in Translational Health Sciences full or part time. Full-time students live in Oxford for three terms (about 9 months) and complete the course within a year. Part-time students take modules at their own pace and complete the course in 2-5 years. Whether full or part-time, you will take a total of six 20-credit modules and write a 15000-word dissertation describing a translational science challenge and how you addressed it (or, alternatively, a desk research study of how others addressed it). 

The 20-credit modules all have a similar structure: you begin with some online preparatory activities, then clear your diary of all other commitments to join an intensive on-site ‘Oxford week’, and then join in six weeks of online follow-up activities while you write a 4000-word assignment. The dissertation is a more in-depth piece of work which you do independently with support from a personal supervisor. On completion of the MSc, you can apply to go on to a DPhil in Translational Health Sciences.

Further details of the MSc course are available here. We are accepting applications for entry in 2023.


Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative inquiry, 12(2), 219-245.

LSE Public Policy Group. (2011). Maximising the impacts of your research: A handbook for social sceintists. London School of Economics.  Accessed 24th August 2022 at

Tetlock, P. (2005). Expert Political Judgment. Princeton University Press. 

Opinions expressed are those of the author/s and not of the University of Oxford. Readers' comments will be moderated - see our guidelines for further information.


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