Within the community of people who make their living paying attention to science--science writers, historians of science, science ethics and policy experts, among others--there are a few recurring concerns. One is the lack of scientific literacy in the general public, which is usually seen as being related to a "soundbite culture". Popular media sources need to distill everything down to small, simple, quotable summaries, but the work of science rarely produces small, simple results.
For most people, the most familiar examples of this come from health and medicine reporting, which often makes health science look contradictory and arbitrary: being overweight increases your chance of getting health problems, except when it makes you more likely to survive certain diseases, and losing weight is good for your health, unless you gain in back, because yo-yo-ing is bad for your health, and losing weight is futile because weight is genetically determined, except that people tend to lose weight when their friends do. You should always wear sunscreen, because even small amounts of sun exposure increase your risk for cancer, but not getting enough sun exposure means you're short on Vitamin D, which boosts your immune system. Red wine reduces your chance of heart disease, but drinking alcohol increases it. In all of these cases, the apparent contradictions come from the fact that the basic statements are huge oversimplifications of the research in question, sometimes to the point where the statement no longer accurately reflects even a part of what the research was about.
Part of basic scientific literacy is learning to unpack these statements, learning which questions to ask in order to put everything in the right context. When you're looking at a medical diagnosis, for instance, and someone says, "patients with this condition have a forty percent survival rate." What does that mean? What's the timeline? "Forty percent survival rate" means that forty percent of patients are still alive at some set time after diagnosis--three years, or five years, or ten years. If the timeframe is large enough, then the percentage may not take into account recent changes in treatment options. It can also be hard to tease out information about how early (or late) patients were diagnosed. Generally speaking, medical treatments are more effective in the early stages of an illness, but do the survival rate numbers take that into account?
If you spend a lot of your time dealing with science and medicine, either from the inside or the outside, this kind of thinking becomes second nature. But it can fly away just when you need it most. A few years ago, someone very close to me was diagnosed with cancer. When I first found out, I spent hours researching online, trying to get a handle on the situation. I found websites that explained treatments and prognosis in detail, that avoided all of the sound-bite pitfalls I described above, and I didn't want them. I was reading things like "patients diagnosed while in Stage 2, who enter treatment X immediately, have an eighty-five percent likelihood of still being alive seven years later, and the advent of medication Y has decreased the chance of remission after two years," and I didn't care--the voice in my head was saying, just tell me if it's going to be okay. I kept thinking, I don't want all of this information, I just want the answer. Within a day or two, I was able to appreciate and process all of the detailed information, but right in that moment I finally understood the appeal of the scientific soundbite.
All of this has been on my mind again this week, watching some of my closest friends manage a medical crisis with their infant son. They're lucky enough to have doctors who will explain everything to them, patiently and thoroughly and in detail, and they're being flooded with information, and they're doing a great job of processing all of it. From where I'm standing, half a step back, I'm comforted by having access to so much information, most of the time. Every once in a while, though, all the statistics and probabilities fade into white noise, and the sound of all those numbers turns into that same small voice: just tell me if he's going to be okay.