Through the looking-glass: lessons learned from interdisciplinary working
‘Interdisciplinary’ has probably become one of the biggest buzzwords of the past decade in academia. As researchers, lecturers, teachers and innovators, we are expected to engage in interdisciplinary working. But what is interdisciplinary working, and how do we achieve it? In this blog, I will reflect on my experiences of completing an interdisciplinary PhD project between Psychology and Computer Science, as well as subsequent experiences of working on projects across the two disciplines. I will also highlight my top 3 lessons for interdisciplinary working.
What is ‘interdisciplinarity’?
You might be wondering what the difference is between the different types of inter/multi/cross/pluri etc. — disciplinaries. There are various definitions floating about the literature, suggesting there are theoretical differences. For example, multidisciplinarity involves several people from different disciplines working together by each making their own discipline-specific contribution, while transdisciplinarity involves an integration of knowledge and skills and going beyond the perspectives of the respective disciplines.
What we do in practice, however, rarely falls neatly into any single category. What is clear from various definitions is that there appear to be different levels of interdisciplinarity, from the most basic form (everyone working separately on their own part of the project), to a more integrated, coherent form, in which people learn from each other and expand their horizons.
Sounds grand, doesn’t it? That’s probably also what my PhD supervisors — a psychologist and a computer scientist — thought when they decided to supervise a joint PhD project. My experiences of completing this project range from intriguing and enriching to downright bewildering (much like Alice’s journey through the fantastical world on the other side of the mirror). Despite some initial stumbling blocks, I am now an interdisciplinary enthusiast, and have worked as a researcher across the Schools of Health Sciences and Computer Science. I currently work as part of the “integrated, interdisciplinary, innovative” i3HS Hub, a multidisciplinary project to promote teaching and research across data sciences and health sciences.
Based on these experiences, here are my top 3 tips for interdisciplinary working:
1. Co-location
When I started my PhD, I was able to choose whether I would prefer to set up my workspace in the School of Nursing (where my main supervisor was based) or in the School of Computer Science (where my co-supervisor was based). I opted for the School of Computer Science. This was mostly because I was promised my own workspace in Computer Science (!), whereas the alternative was hot-desking in the School of Nursing. As it turned out, this was the best decision I could have made. Throughout the years that followed, I learned to understand the world through a very different lens, simply by spending time with and talking to fellow PhD candidates, researchers and lecturers in Computer Science. I began to understand some of the jargon, and I became involved in joint projects. None of these opportunities would have presented themselves if I hadn’t been there physically.
This is corroborated by research which shows that geographical proximity has a strong positive influence on collaboration by creating opportunities for accidental encounters and observations, and by facilitating the development of trustful relations and shared values. In absence of a physical space, virtual platforms may also be helpful, but face-to-face interactions are preferable.
2. Frequent communication
In the past, I have worked on various projects involving researchers, practitioners and industry partners across multiple disciplines. Communication is often hampered because respective parties essentially speak different languages. This becomes especially problematic if respective parties leave meetings with different understandings of what was agreed. If partners within an interdisciplinary team do not communicate their perspectives on aims and work processes frequently, they risk ending up with separate projects which can’t be synthesised into a coherent whole, thus defeating the purpose.
I spent the first year of my PhD trying to figure out how to translate between my psychology and my computer science supervisor. The problem was solved when I found a ‘hybrid’ supervisor with degrees in both disciplines, who joined the team and helped translate. In the absence of a hybrid (a rare and elusive breed), I suggest clear, frequent communication, open discussions about any perceived ambiguities, and, when necessary, reverting to a common language, i.e. plain, lay terms.
3. Mutual understanding
An understanding of each involved discipline and clarity of individual roles is crucial to facilitating functional communication. Research in healthcare settings suggests that an understanding of different roles in multidisciplinary teams is crucial when caring for patients with complex needs, as this helps coordinate care and prevents duplication. Similar principles apply in other projects. For example, in a digital health project, a health researcher doesn’t need to know the ins and outs of programming, but a basic understanding of the back-end work processes a software engineer deals with — such as data storage — is important when discussing work flows and agreeing objectives.
To conclude, interdisciplinary working is challenging, and it is hard work. It requires dedication and commitment; the involved parties need to be willing to enter into common grounds — whether physically or virtually — to open up opportunities for collaboration, and to invest time and resources into communication and joint understanding. But the struggle is worth it — different disciplines can act like different types of fishing nets: each uniquely suited to trap different aspects of the world for inspection. The key is to bring the different fish together. This is what we are hoping to achieve with the i3HS Hub. By combining expertise from various disciplines — including public health, epidemiology, genomics, computer science, bioinformatics, and data sciences — we hope to promote teaching and research and to create novel, innovative solutions.
About the author
Dr Julia Mueller is a Lecturer in Healthcare Sciences at the University of Manchester, based in the Division of Population Health, Health Services Research and Primary Care.
Much of Julia’s research relates to digital health, with a particular focus on health-related behaviour change interventions, such as mobile apps for chronic disease management. She is also interested in early diagnosis and help-seeking behaviour, and the role of the Web in symptom appraisal processes. Julia completed her undergraduate studies in Psychology and a Masters in Health Psychology. In 2018, she completed an MRC-funded PhD research project at the University of Manchester about the role of Web-based information in help-seeking of those worried about lung cancer, co-supervised between Health Sciences and Computer Science. Since July 2018 she has been working as a Lecturer in Healthcare Sciences as part of the i3HS Hub.
