What do people know and think about 5G? Creating mental models for #ProjectGOLIAT
Why do some people readily accept 5G and use it without a second thought, while others worry about the potential effects that 5G may have on their physical and psychological health? How do these groups differ from each other, and how can we better respond to their concerns, particularly with tailored risk communication?
Task 6.2 of Project GOLIAT focuses on constructing the mental models of EMF experts and EMF non-experts regarding 5G technology and its effects on health. Our core team consists of Matt White and Nina Vaupotic at the University of Vienna and James Grellier and Leanne Martin at the University of Exeter, with many collaborators around the globe actively contributing to our mental models study.
Our main objective is to create mental models, or representations, of people’s knowledge, beliefs, and opinions about 5G technology and its effects on health among different groups of people: EMF experts, EMF non-experts, and EMF non-experts who self-identify as electrosensitive. We aim to identify (1) the misconceptions of EMF non-experts and (2) aspects of EMF non-experts’ beliefs and concerns that EMF experts may not be aware of.
How do we elicit mental models?
We carried out semi-structured interviews where we encouraged interviewees (EMF experts and EMF non-experts) to share their knowledge, opinions, and thoughts. We first asked the interviewees to do a ranking task on the topic of electromagnetic frequencies, which prepared them for the topic of the interview. We then asked them about different potential benefits and drawbacks of 5G, as well as about their own behaviour in relation to 5G.
In total, we conducted 10 interviews with EMF experts, 23 interviews with EMF non-experts and 3 interviews with EHS individuals. We are grateful to our partners in Spain, Japan and Poland who helped us with conducting interviews in their countries.
How did we analyse the qualitative data?
The interviews were audio-recorded (with the approval of the interviewees) and later transcribed into text. We used MAXQDA 2024 (VERBI Software, 2021) to analyse the transcriptions. In the first step, we familiarized ourselves with the interview data and began to recognize recurring themes such as health effects, economic benefits, and general worries. We investigated whether these themes repeatedly appeared in the interviews. This process led to the development of a coding scheme, which we continuously adapted and tested with new data to ensure its accuracy and relevance. For instance, we had to make sure that the topics one coder recognizes in the interview texts would also be recognized by other independent coders.
How do we construct mental models?
With the assistance of the MAXQDA software, we analysed which topics were frequently mentioned together and by which groups of interviewees. Drawing on connections between topics and literature reviews, we crafted a mental model representing the knowledge, beliefs, and opinions of both EMF experts and non-experts.
Why is it this important?
Comparing the mental models of EMF experts and EMF non-experts enabled us to identify misconceptions regarding 5G and health, which were then integrated into a public survey to estimate their prevalence. Additionally, identifying the factors contributing to public acceptance of 5G can provide valuable insights into understanding why some of EMF non-experts are concerned. Ultimately, we hope to contribute to targeted risk communication, better tailored to respond to EMF non-experts’ beliefs and concerns about 5G.
We are working on our manuscript and look forward to sharing the results with you soon!