We’ve asked our #DataImpactFellows to respond to the theme of ‘communicating/translating data-driven research’.
In this post, Bożena Wielgoszewska reflects on the importance of good communication in her collaboration on the Covid-19 Longitudinal Health and Wellbeing National Core Study, a project that is generating new data-driven insights into the Covid-19 furlough scheme.
Could you tell us a bit about your current research project, and which data underpin it?
Over the last few months, together with many other researchers across various UK institutions, our team have been working on the Covid-19 Longitudinal Health and Wellbeing National Core Study project, part of the larger National Core Studies programme funded by UKRI. The aim of this project is to understand the health, social and economic impacts of the Covid-19 pandemic to inform policy.
We aim to do this though uniting established UK population cohorts and national anonymised electronic health records, including the cohorts we host at the Centre for Longitudinal Studies (CLS). These datasets, which include the National Child Development Study, the 1970 British Cohort Study, Next Steps, and the Millennium Cohort Study, are available to researchers via the UK Data Service. We have also utilised data from other longitudinal population studies, such as Understanding Society, the English Longitudinal Study of Aging, the Avon Longitudinal Study of Parents and Children, Generation Scotland, and Twins UK. The first two are also available from the UK Data Service.
All of these studies conducted special data collections, asking people about their experiences during the pandemic, including questions on peoples’ physical and mental health and wellbeing, family and relationships, education, work, and finances.
The part of the project I am involved in predominantly focuses on the new in the UK context Coronavirus Job Retention Scheme, more commonly known as furlough, and its impacts on health behaviours, substance use, and mental and social wellbeing.
How has this interdisciplinary project changed your approach to collaborative research?
I work at the Centre for Longitudinal Studies, where our research team is already very interdisciplinary. However, the pandemic and associated mitigation measures affected many interrelated aspects of people’s lives simultaneously, affecting everyone’s routines. People were unable to work, socialise, send their kids to school, go shopping or to the gym. So to better understand the wide ranging impact of Covid-19, it was necessary for us to develop new collaborations.
The National Core Study programme was specifically created as part of the UK’s response to the pandemic. Researchers with expertise in different fields, ranging from public policy to mental health, and proficient in using different datasets, came together, all united by the goal to answer research questions about Covid-19 and its implications.
Two aspects of my daily work have changed since being part of this programme. Firstly, the team I work in has become much bigger, which has rapidly increased the number of weekly meetings I have to attend. I now have to be much more organised, so I can balance these extra meetings with my existing commitments. Secondly, I am now working directly with health researchers, which up until now I have had little experience of as my previous work was predominantly focused on social and economic inequalities. As the terminology and approaches vary across disciplines, I have had to become more open-minded and quickly adjust to different ways of thinking in this new interdisciplinary team.
What were the key successes and challenges you encountered during the project, in terms of handling the data using cross-disciplinary methods?
For me the key aspect of any interdisciplinary collaboration is clear and open communication. This can be tricky because every discipline has its own terminology and jargon that is familiar to everyone within the discipline, but not immediately obvious to researchers from other disciplines. So it was initially quite intimidating to be in meetings with people using unfamiliar terms.
However, I quickly realised that different disciplines use different terms to essentially describe the same concepts. For example, the method known to sociologists as ‘event history analysis’ in epidemiology is more commonly known as ‘survival analysis’. Luckily for me, the health researchers in our team are very approachable, so it was easy for me to ask for clarification when required. They are also very patient in rephrasing their points, which I appreciate greatly!
We had several challenges in terms of the data too. Most of the studies that we rely upon in this project conducted their own questionnaires as part of the rapid response to the pandemic, but there was little scope to co-ordinate and harmonise these questionnaires at the time. For example, some studies asked about alcohol consumption levels before and during the pandemic, while other studies only asked about change in the quantity or frequency of drinking. This meant that the challenge of finding commonalities across the datasets and deriving harmonised variables came at the analysis stage.
Fortunately, researchers in our team are experienced in working with the specific datasets, which meant that we were able to conduct coordinated analyses that were then pooled together and analysed centrally by the team of lead authors for the given research questions. This was a great way to facilitate efficient and effective collaboration.
What impact do you hope your research will have, and what have you learnt about the process of developing impact as part of this team?
Ultimately a dream of every researcher is to have instrumental impact; to be able to say that they made a demonstratable contribution to developing policy or practice. We hope this will be the case, as the executive team on our project feeds our findings to policymakers directly and our project focuses on the furlough scheme, the consequences of which are not yet well understood. The project has taught me how important it is to build knowledge exchange into the research process from the very beginning, to maximise the chances of impact being achieved.
However, in practice, simply sharing the findings is often not sufficient. After all, policies are shaped by inputs from many people. They require negotiation and often reflect the balancing of competing interests, priorities, and ideologies. This is why effective communication is so key – navigating the world of policy change is challenging, but finding the right language to translate research findings so that policy makers listen to and understand them can increase the chances of the research being implemented in policy change.
Our team is also aiming to achieve conceptual impact through improving understanding of key policy issues and reframing debates about the pandemic. The furlough scheme was implemented in the UK for economic reasons – to support those who were unable to work during the national lockdowns. However, in our research we are shifting the focus from the economic aspect of the scheme to the health aspect. We know from previous studies that employment is positively associated with health, as compared to unemployment, so we wanted to find out whether there are similar associations and unintended consequences of furlough. So far, our research highlights the benefits of this scheme, showing that it has not been detrimental for health outcomes, at least in the early stages of the pandemic.
Are you able to reach new audiences that you weren’t able to before working in this team?
Yes, I hope so. I primarily work and publish in social science journals, but for this project we aim to publish our research on the impact of furlough in health-oriented journals. Since I have not published in such journals before, I look forward to reaching new audiences.
On top of the peer reviewed journal articles, we are also publishing briefings aimed at policy makers, as well as infographics, blogs and short videos, which are produced with a non-academic audience in mind. One example is the animation we’ve recently developed, which explains the aims of the project (you can watch it below, or on our YouTube channel). All outputs are generally posted on the @COVID19_LHW Twitter account, so follow us to keep up to date.
I think this aspect of our work is very important, especially because there is so much misinformation online these days, so our work is contributing to enhancing public awareness and understanding of the pandemic and its effects on society.
Could you tell us a little about how you are going about translating your research findings for non-academic audiences?
Communicating academic findings to a non-academic audience in not straightforward. In terms of our project, we are very luckily because when it comes to the Covid-19 pandemic most people are interested in the results and have a general understanding of what we are talking about. However, academic papers are typically very densely written and include a lot of information while infographics, for example, typically communicate one or two main points. Selecting these main points can be challenging, as is the process of adapting the content to suit the needs of different audiences.
For instance, you have to ensure that the chosen points make sense in isolation from the wider context of the academic paper; that they are easily understandable for someone who, for example, does not typically measure things in risk ratios; and that no important information is lost or distorted in the process. Taking the time to understand the needs of your target audience is therefore crucial, as is thinking creatively about the best way to engage that audience.
We are still working on creating our infographic for the Covid-19 LHW project, and the process has encouraged me to reflect on my own methods of communication. I’m now more conscious of avoiding jargon and better understand that making time to engage with wider audiences and using different communication channels enhances the research process.
The project has also taught me that communication is a two-way process, which is essential for any science. As researchers we need to listen to feedback on our work, to suggestions made by policymakers and to the voices of the members of the public. Opening up to collaboration with researchers from other disciplines has made me aware that the way I have been taught to conduct research is not the only correct way of doing so. Through learning to accept this and, despite my (I hope healthy) skepticism, I have became a much more open-minded person.
About the author
Bożena Wielgoszewska is one of our UK Data Service Data Impact Fellows 2019, and is a Research Associate in the Social Research Institute at UCL).
She divides her time between the Centre for Longitudinal Studies (CLS) and the Quantitative Social Sciences (QSS) research group. Within her CLS role she works on a number of projects, one of which aims at harmonisation of the income and earnings data across three cohort studies: National Child Development Study (NCDS), 1970 British Cohort Study (BCS70), and the Millennium Cohort Study (MCS). Within her QSS role she is a co-investigator on the project that uses the cohort studies to investigate the gender wage gap over the lifecourse and across cohorts.
Bozena was awarded a PhD from the University of Edinburgh in July 2019. Her PhD research was supported by the Skills Development Scotland (SDS) and Economic and Social Research Council (ESRC) collaborative funding programme.
Her PhD thesis is entitled ‘Understanding University Graduates’ Social Mobility Trajectories: How Does the Route affect the Outcome?’. In this project, she analysed economic activity histories of a sample of graduates from the 1970 British Cohort Study, mapping the patterns of change in their employment and social class over time.
Featured image by Martin Sanchez on Unsplash.