Sophie Darke from PHRESH (University of Birmingham) writes about her experience completing a SPHR internship.
The development of statistical testing in the 1700s was ground breaking and soon became an important part of public health research. Quantitative data analysis – using numbers to understand and make decisions about the world – is commonly used by public health professionals. It can be used to quickly and accurately trace diseases, identify risk factors, or predict health outcomes. This makes it hard to dispute its clinical utility and importance.
However, once participants’ responses have been anonymised and sorted into a dataset for analysis, it can become easy to forget that each data point represents a real person’s thoughts, feelings and experiences.
Data humanisation is a process that makes data analysis and interpretation more human-centred. Essentially, helping researchers keep in mind the real-world experiences of the people being studied. Data humanisation suggests that taking the time to create a deeper understanding and connection with the people the data represents it can improve your understanding and contextualise the trends, anomalies and patterns in the data.
These connections can be built through participating in activities that require engagement with the population under study. With the researcher utilising active listening and empathy skills to connect with the population. Activities like meeting people in person, volunteering in communities or participating in communal activities like coffee mornings are all great examples of ways to undergo data humanisation.
“Science is not only a disciple of reason but, one of romance and passion” Stephen Hawking
It has been during my SPHR internship that I have learnt how passionately I feel about data humanisation. My internship involved performing secondary analysis on a large, publicly available dataset from the Trussell Trust 2023 Hunger in the UK report. I was initially overwhelmed by the numbers and found the concept of data analysis daunting. When my supervisor, Dr Kiya Hurley, kindly organised and encouraged me to volunteer at some of the food banks in the Trussell Trust network, I was initially unaware of the impact it would have on my project.
Visiting the Trussell Trust food banks has completely changed my perspective on the project. What once was a computer screen swimming in numbers is now an opportunity to help the kind, funny and appreciative food bank users who I met while volunteering. Other volunteers I met helped provide context and reinforced the importance of ensuring my analysis accurately represented a foodbank user’s experience. Now I have met those behind the data I am more passionate about the work I am doing, and I feel I can more accurately and ethically understand the data. To me the data set is no longer a collection of numbers, it is a collection of experiences and opinions of those I have met, those who have made me smile and laugh, those who have trusted me to help them and felt comfortable enough to share their experiences with me.
With one in seven people in the United Kingdom reporting that they are currently facing hunger – food aid provides an important lifeline to many. This is why it is so important we ensure our food banks are as effective and accessible as possible. With more than 1,400 food banks the Trussell Trust is a major part of the food aid network in the UK, and I am very grateful for their support and feedback on my project and the opportunity to volunteer at their food banks. This volunteering involved activities like helping pack food parcels, checking expiration dates, and collecting feedback from foodbank users.
My research was titled ‘Determining the factors which influence whether food-insecure families access food aid’ which aimed to provide a novel insight into the social and demographic factors which impact the likelihood of food-insecure families accessing food aid. Coming from a background in biomedical science where I primarily spent my time working in labs and studying disease mechanisms, exploring and describing social and demographic factors required a completely new set of skills.
My results identified three main factors which have a significant effect on the likelihood of food insecure families accessing food aid. These were: occurrence of adverse life events, travel destitution, and number of children in a family. I hope this research will prompt further research into why families in need aren’t accessing food aid and investment into ways to target them. I believe mobile food banks could be a promising way to target these families, especially as travel was shown to be statistically related to the likelihood of using foodbanks.
In conclusion, data humanisation may not give your research more power or help you prove a statistical relationship between factors, but the insight you gain from trying to better understand the experiences of the people behind your data has many benefits that help to improve the quality of your research. It can help you understand trends and patterns in your data, uncover relevant research outcomes, and carry out more ethical research practices, whilst perhaps most importantly motivating and inspiring researchers to continue producing impactful research that improves the quality of life for the real people behind your data.
