Reflections on Our Human-Centred Qualitative Data Science Journey

Abstract

We are HCI researchers from the University of Waterloo’s School of Public Health Sciences. Many of our colleagues are social science researchers, and are tackling important issues like vaccine hesitancy, mental health, and addiction. These colleagues are very excited about the potential benefits of data science in applied settings, but they are also limited by the technical and programming skills required to engage with current data science practices. Put simply, there is a gap between those with domain expertise and the means to enact positive changes in our health care systems, and those with the technical skills required to currently perform data science.

Towards these ends, over the past 4 years we have explored how we can make computational techniques more acces- sible to our colleagues to support and enhance their qualitative research, and specifically reflexive thematic analysis [2]. We are optimistic that the HCI community — a hub for multi-, trans-, and inter-disciplinary technology research — is a critical venue for human-centred data science to evolve. Yet, we simultaneously have experienced difficulty in engag- ing with the community and have concerns about how the research process has unfolded. To situate these concerns, we first summarize our own research experiences, before discussing interrogations and provocations for the workshop.