According to Eppler (2001), information misinterpretation occurs when information “is not seen in context”; and, therefore, high quality information includes details about its context:
High-quality information is always presented with its context of origination and its context of use (where did it come from, why is it important and to whom is it important, how should it be used). Through this, the information should become
clearer for the target group because it can understand the information’s background. The target group can also better assess whether the information holds true for the new context and if it is correct even under different circumstances. (p. 335)
5.5.1 Main Finding
Some of my research participants expressed their concerns about misinterpreting some of the visualized information about the social determinants of health by laypeople. Such misinterpretation may happen as a result of not providing enough details about the context, conclusions, and limitations of the research studies as stated by their authors. It could also happen when readers do not have background information about the problems presented in these studies. However, all of these participants rejected the idea of not showing the information and asserted that even with the probability of misinterpretation, presenting the available information is always better and helps in sensitizing people about the social determinants of health and creating public pressure, which could lead to policy action.
5.5.2 Participants’ Voice
Participant#5, an assistant professor, stated that he was more for exchanging knowledge in academia channels such as journals and conferences than in other public channels such as websites and newspaper because research quality criteria are presumably used in producing research papers in academia:
“When you read a research paper it has the kind of the thought and the process behind that data, so that the reader can understand how exactly the researchers have gotten this data, what are they saying and what are the conclusions that they can functionally make. For example, in our research papers we always write our limitations, and in the limitations, we will caution people, by saying that here are the conclusions that you can make from this particular data, and here are the conclusions that you shouldn't be jumping into with this data… So one of the challenges when you just have the data, and you don't have the context around how this data was collected and what the researcher is warning you about with that data; is that people might start jumping to conclusions. And this is the problem with Code
Red, [a web-based tool that presents data and interactive visualization about the social determinants of health in Hamilton in Ontario] because it led to a
stigmatization of particular neighborhoods because people were able to see that there are obvious health differentials. People started avoiding to live in these neighborhoods if they could because these neighborhoods are not safe. So it didn't lead people to this complex understanding [of health inequity causes] but instead, it led them to conclude that these were crappy neighborhoods… I think that this is the challenge of visualization because people may start asking different questions from what the data was meant for.”
However, Participant#5 did not reject the idea of using non-academic publishing channels to present information about the social determinants of health. Rather, he asserted that I, as a developer for this tool, understand the lessons learned from Code Red, which is a web-based tool that presents data and interactive visualization about the social
determinants of health in Hamilton in Ontario:
“The lesson that people learned from Code Red is that if you just give people the data without pushing them back to the theoretical understanding, there is a risk that they make their own conclusion…With each visualization, you need to put the conclusions and the limitations of each study; so that we can see what the
researchers are telling us to see in the data. You also need to make it explicit that each of these visualizations is part of the CSDH framework…. those results need always to point back to the framework so that people understand the complexity of the [health equity] problem.”
Participant#6, a health records specialist, asserted that although misinterpretation could occur, it does not mean that information should not be published:
“I think that you are going to have that problem [of misinterpretation]. People will jump to conclusions, and they might have issues with it. But we are supposed to have a transparent system. We are supposed to have everything available to the public. And if people are not happy with something they should be able to do something about it. So if there is more violence in an area then there will be issues,
and people might not want to live in that area, and that could lead to discrimination. But once people are aware of it and once people are talking about it, then something will be done about it. So I think it is definitely important to the public to be aware of these things.”
Participant#4, who is an epidemiologist and a program manager at a health unit, thought that the possibility of misinterpretation does not mean that data should not be published: “[The health indicator map] was interesting because one of the questions was “do you think you have the healthcare services you need or something like that “, and the map actually showed that rural people thought they have better or more services than urban people, and it is very confusing. So you see that and wonder how
someone, without the background knowledge, can interpret it. So it does make you think: do you have to lead them there, I mean; is the visualization just one step and then you have to lead them to that kind of conclusion because maybe they just don't know what they don't know. So I will be a little worried that someone may
misinterpret that, but this shouldn't stop you from putting the data out there.” Participant#7, who is an assistant professor and epidemiologist, had a similar view, and he believed that Canadian public health could learn from the US public health regarding information disclosure:
“When you share information in a form that allows people to interact with it and organize it and make a comparison, it doesn't always mean that they are going to make correct or legitimate comparisons or arrive at conclusions, which are
supportable based on the evidence, so that is always a danger. For me, that is not a reason not to share information. If you look at where the United States' public health system has gone in terms of its disclosure of information on health, there are open portals where individuals have access to information about their
neighborhoods in terms of disease rates and potential environmental exposures. And there is no guidance in terms of how they might consume or interpret that. So yes that is going to lead to a great deal of turbulence, but I think that is not necessarily a bad thing because at least now you have got bases for engagement. The important
question is does public health have the capacity to participate in that engagement? so that individuals can actually profit and learn from access to the information.”