Research

Getting reflexive: Reflections from a data loving researcher

By Rebecca Musgrove

I’ve always loved numbers. I have vivid memories of the excitement of solving complicated A-level maths problems. I enjoyed the lack of ambiguity; either the answer was right or it was wrong. It was knowable. Twenty years later, via a career implementing community health projects, where there is rarely one straightforward solution, I have returned to the numbers. I became an Analyst in mental health at NHS England and last year started a PhD using NHS data to examine suicide and self-harm after discharge from inpatient mental health settings. I have found this shift immensely rewarding. I want to generate evidence that will help services to provide timely, respectful and effective support to people who use them. If I’m honest though, part of the satisfaction comes from turning this complex area into statistical problems to be solved, much like my A-level maths problems. But is it really possible to do this?

Traditionally quantitative research is underpinned by a positivist approach that a ‘single tangible reality exists’ [1, p. 691] which can be measured. It also assumes objectivity through study design and statistical technique. Instinctively I know that I am drawn to this. Simultaneously however, it sits uneasily with my understanding of the world and the complex interplay of structural, social and psychological factors that may lead someone to take their own life. As I reflect on my own motivations and biases I also question whether research, no matter how well designed, can truly be objective.

It turns out, of course, that this type of reflection is nothing new. The term ‘reflexivity’ is used extensively by qualitative researchers to describe this process of reflecting on one’s position as a researcher, not just to reduce bias, but also to accept that a researcher cannot be fully separate [see for example, references: 2,3]. Although this often focuses on interactions with research participants, it also relates to testing one’s own assumptions in the design and interpretation of the research. A reflexive statement in a published qualitative paper is an indicator of quality [4]. However a search for reflexivity in quantitative research returns few results. This is perhaps unsurprising; in many ways reflexivity stands in opposition to the positivist ideas of objectivity. Nevertheless, I believe that all researchers should examine and acknowledge our positions and influences and consider how these guide the questions we ask, the data we collect and how we make meaning of that data.

I am not an expert in this, but as a quantitative researcher, I’d like to outline my position and make suggestions from my PhD journey to date in how a reflexive approach may improve research. I hope this is useful to others whose research takes place at a distance, where we might not question our subjectivity.

Understanding and acknowledging my position

I wanted to consider two areas highlighted above; my natural love of data and my job within the NHS. Firstly, I applied for this PhD because I knew it would use routinely collected NHS data. I wanted to learn the best possible techniques for analysing it. I didn’t initially question whether this was the best approach to the topic. In addition, my interest in suicide prevention and mental health largely comes from, and is influenced by, my work within a national NHS body. I have no direct personal or professional experience of inpatient care or suicidality. My motivation and background (and the acknowledged and unknown biases that come with them) are factors in my research. There is nothing inherently wrong with this, we need relevant skills and motivation to become researchers! However by acknowledging this influence, trying not to be defensive and taking steps outlined below I think my understanding and research is richer.

Seek conversations with researchers with different expertise.

In netECR and within the Centre for Mental Health and Safety at the University of Manchester I’ve found an open community of multi-disciplinary researchers. It was largely conversations early in my PhD with these researchers which started this process of reflection and made me realise how many different approaches there could be to my research.

Listen to service users and those involved in mental health services.

When I started my PhD I became overwhelmed as I listened to and read people’s accounts of experiencing inpatient care at conferences and on Twitter. I didn’t want to believe that services didn’t always provide the support that was needed. However, by staying and listening, I am gaining a more nuanced understanding of how people experience services. These accounts made me reflect on how data I am using is collected; whether it always measures what we think it does. I also have the privilege of receiving ongoing guidance from an advisory group who have used mental health services. The group is funded by the National Institute for Health Research Greater Manchester Patient Safety Translational Research Centre (NIHR GM PSTRC) who also fund my PhD. The group have made comments on my research plans and really challenged me to justify my approach. I look forward to hearing their perspectives on the results when I have them.

I have also met with clinical staff that work in mental health services, and I am formally collaborating with two GPs and now have a psychiatrist on my supervisory team. Along with service users they are providing multiple perspectives that have expanded my understanding as well as bringing credibility to my research.

Review literature outside of your discipline.

By including qualitative research in my search criteria alongside epidemiological studies I gained a better picture of the issues relating to hospital discharge and subsequent health service provision. As a consequence of my review I saw where I could add value and design studies to identify rates of suicide and how people access different services including primary care. However, I realised that there were other gaps particularly in our understanding of the quality of those services and how people experience them which could not be answered using existing datasets.

Don’t try and do everything, but don’t ignore what you don’t do.

As PhD students I think we put pressure on ourselves to do everything. I felt that I should do a qualitative study in addition to my planned studies. However after some soul searching I knew that this wasn’t where my primary interests lay. Rather than trying to answer everything I will instead seek out other researchers who are better placed to develop qualitative research in this area.

Write it down.

As a self-proclaimed data person, I am happiest when I’m working with numbers. However I am attempting to keep a diary which includes reflections on my position as a researcher and the changes in my work as a result of these reflections. I’m not comfortable writing blogs but the process of writing this has helped me to distil my thoughts.

Keep reflecting.

Reflexivity isn’t something to do once and then move on. I’ve only spoken here about two aspects of my position and I will continue to uncover how my beliefs influence my research. It is difficult though. The Covid-19 pandemic has made me more insular. I find it harder to reflect without informal face-to-face interactions.It’s easier to hide in my numbers. It’s also challenging as reflexivity isn’t something that I ‘need’ to get my PhD or publish papers. The formats of the journals that have impact in the field rarely allow space to explore these issues. I worry that I don’t have much to ‘show’ for it externally; what ‘evidence’ do I have that my research is stronger because I’ve attempted to be reflexive? These are challenges that I don’t have answers to yet.

What does this mean for my belief in the value of my research?

This process of ongoing reflection has helped me to see the use of national health data as one component of mental health and suicide research. It is not less or more important than other disciplines or research methods; it is one part of the puzzle. Ultimately I believe in my research and the value of using large scale datasets to identify population trends and themes that deserve further interrogation. I have spent a lot of time planning my study and approach to statistical analysis to reduce bias. However we should acknowledge that research will always be situated within the choices made by funders, policy makers, clinicians and researchers. It is important for us to acknowledge this context and continue to have these conversations. I firmly believe that our research will be stronger because of it.

The views expressed in this blog post are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care

References:

1. Park, Y. S., L. Konge and A. R. J. Artino (2020). The Positivism Paradigm of Research. Academic Medicine, 95(5), 690-694.

2. Gouldner, A. W. (1971). The coming crisis of western sociology. New York, Avon books.

3. Dowling, M. (2006). Approaches to reflexivity in qualitative research. Nurse Researcher, 13(3), 7-21.

4. Critical Appraisal Skills Programme. (2018). CASP Qualitative Checklist. Retrieved May 03, 2019, from https://casp-uk.net/casp-tools-checklists/.


Rebecca Musgrove (@beckymus) is currently researching for a PhD in Epidemiology with a focus on suicide and self-harm for people who have recently been discharged from mental health inpatient care. She is also a Senior Analyst in Mental Health at NHS England (Rebecca.musgrove@postgrad.manchester.ac.uk).


*Article featuring photo by RF._.studio from Pexels.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.