In science, when we use the ABT narrative structure, it tends to be particularly useful to tell two key times of stories. The first is the story of the research question; what do we not know? The second is the story of the critical takeaway from the research findings, the thing we want the audience to most understand about what has been learned. Thus, I often like to summarize research using what I would call the ABT times two format. Let me walk you through it. The research question ABT, basically flows this way. And: what is known already. But: here's what is not yet known. The problem, the thing we don't know, the knowledge gap. Therefore: because we have this knowledge gap, we need to do a particular study, we need to learn more to resolve the tension that we're now feeling. The research finding ABT, it's a little bit different. We start with talking about the stuff that's been found. That is, what was expected, what was not surprising. Then we make a shift to but. But this is the critical piece, but this is what was surprising. But this is what confirms our hypothesis. Whatever that critical piece is, that is what we want people to focus in on because that critical piece; therefore, leads to the study implications to what we need to do as a result of the science that we've now done. See how simple this is? The ABT structure helps you focus on the two key ideas: the question or hypothesis that you have and the critical finding, that is what you want most want people to pay attention to. Everything else is simply there to support those key ideas. It actually aligns well with the standard structure, it just makes it a little bit clearer. If we take that sort of hourglass structure I had before, big picture context and the background, aligns with what we know. Then, we have to set up what's the hypothesis? What's the thing we don't yet know? Which leads us to what we must do, which is our methods. We must do a study that uses these particular methods to get the data and the data breaks down into two pieces. The stuff that we learned that we knew we were going to learn or the basic background data and the critical finding, the surprising finding, the stuff we want people to focus in on, which leads to the interpretation impact, implications which is therefore of the second ABT story, what does this mean for the world? Now, let me give you an example of the ABT times two structure from my own research. The central message is this, we need to improve how we present laboratory test results to patients. So, here goes. Patients with chronic diseases like diabetes, often need to get laboratory tests, blood tests to check how well they're doing with their disease. This information can help those patients with self management tasks and more and more patients can view their results online by logging onto websites run by their hospital or health system. But those websites present the test results in number heavy tables that we know are very difficult for patients to understand. Therefore, we wanted to see if we could design visual displays of laboratory test results that patients would find easier to understand. We conducted a series of experiments as part of this project and we found not surprisingly, that our visual displays were indeed easier for patients to understand. But we also found that these displays were particularly good at helping patients not worry about results that are just barely not normal and are not medically concerning. Therefore, our results suggest that if hospitals would adopt this type of visual display, patients might be both better informed and have less unnecessary worry about their results. See, you can tell a linear, logical, and engaging story about health science with just And, But, Therefore.
