Here’s the proven template I give science researchers to make their complex research understandable to anyone
When you’re intensely involved in a complicated study for months or years, it’s hard to explain it to anyone else.
When you’re intensely involved in a complicated study for months or years, it’s hard to explain it to anyone else.
I’ve used this template with many researchers to help them talk about their projects.
I know it works.
It zooms out to create a broad brush story of their work. It creates an intro for, say, patients, stakeholder groups, non-scientist members of funding bodies – or semi-informed inlaws.
Generally when people are having trouble understanding something, it’s not because they need more detail. They often have information – but they don’t have context to give it meaning, so they get frustrated and annoyed because they don’t know what to do. They feel like they’re on the receiving end of a dump of random facts.
This template is a way to give them structure as a starting point so they can understand what you mean.
It follows the very broad structure of a lot of journal papers, because it tells a story: here’s the problem, here’s what we did. Tension, followed by resolution.
This is the structure:
I’m very aware that ‘simplified’ is doing a lot of lifting here. I’ll get into ways to simplify in another post.
Here’s a real life example of how this template works: the RECOVERY trial into whether dexamethasone would be effective as a treatment for Covid-19 patients, which I did some communications work on during the pandemic. The study was published in the New England Journal of Medicine.
This isn’t getting into anything steroids or antiflammatories, or multiplatform trials, or using drugs off label, or statistical significance, or any of the many other things that RECOVERY was.
Detail comes later.
It’s a way of explaining what you’re doing, rather than presenting results, although it would be easy enough to tweak the final part to include what you found.
The template is simply aiming to explain what’s going to a non-specialist who’s interested enough to listen for a couple of minutes. In a couple of minutes, you’ve given them context to understand more about your work, and more importantly, a reason to want to know more.
This is why it’s storytelling, not just an infodump. When I’ve used this in training sessions, I’ve got researchers to create summaries for their current work, so feel free to try that.
Shout if you need help or have any thoughts.
Thanks. AI's biggest problem right now is that it (well, the generative models) act like a toddler who's just discovered lying, and do that amusing thing when you challenge them of saying 'oh, you're right, I apologise' and then come up with another few paragraphs of untruths. So nobody trusts a thing it says.
Also, it hasn't yet learned that when a human buys a big expensive thing, like, say, a camera, on Amazon, it doesn't mean that human is now in the market for more expensive cameras. Quite the reverse, in fact.
Love this. Why do we have to make things more complex than they need to be? Transparency in science also means we should make it readable. I have two doctorate degrees and I commonly struggle to reach a conclusion in a research paper. I do think AI can be useful to help explain things at a broader level and hope better tools are coming down the road.