This is a reproduction of Sarah’s blog post at https://datapatientaction.wordpress.com/2018/03/09/quick-reflections-on-kmb2018-coproduction-and-cultivation-and-banarama/
I attended the Knowledge Mobilisation Forum this week, and wanted to get down some quick reflections on the pressing questions and key themes from the conference. This is very much a set of reflections for KMb community members, though I will try to write a more accessible version when I have time!
1.There is a tension between recognising that we need to embed KMb, and foster a culture of KMb (so that KMb doesn’t just disappear if the person responsible moves jobs) with recognition that KMb often does happen through specific individuals who have eg. Facilitation skills, time to develop relationships, and willingness to work across boundaries. What are the implications of this? How do individuals help establish a culture that will outlast them, or how do organisations take responsibility to sustain or reinvigorate KMb if individuals move on?
2. Debates persist around what knowledge is or isn’t, although perhaps now less a debate than trying to work out what we *do* with that fact that there are many kinds of knowledge, with unequal power or legitimacy in different settings, expressed in different ways, some explicit and some intangible. Certainly the knowledge pipeline and any automatic privileging of research knowledge are both considered debunked or unhelpful. As someone who worked for years in trials and systematic reviews, I wonder though if this is specific to the KMb community, and are those who produce research knowledge thinking like this? Or is this seen as an ‘implementation problem’ that KMb types are supposed to provide workarounds for (fix that leaky pipeline!)
3. Boundary objects are more complicated than they appear. Defining them, making them, measuring their impact. No, you can’t just say “so basically what I’m looking at is X as a boundary object” for anything in KMb. I am fully guilty of having done this and will stop it now.
4. Coproduction, involving knowledge users, was essential and consistently discussed across pretty much all the workshops, talks, and across all models and methods. How KMb is conducted was impressively diverse, and there isn’t a magic trick that guarantees KMb will happen or a specific method that has *the* KMb stamp of approval – focus groups, stepped wedge trials, statistical models, Lego, were all in play. But whatever the method, the way of working was considered more effective if it was collaborative, working with the knowledge users and coproducing knowledge itself, to avoid making assumptions, to recognise those different types of knowledge, and to understand what problems knowledge is actually needed to solve. This relates to my Banarama Principle of KMb – it ain’t what you do, it’s the way that you do it, and that’s what get results
5. Sorry Health, but you ain’t special. Education, social work, even law, all face similar, challenges, pressures, and we need to learn across different knowledge settings.
6. I’m not the only “lapsed statistician” in KMb who has moved from doing a lot of quantitative work into more qualitative work, often inspired or necessitated by a focus on coproduction. Interestingly I suspect there’s a fair body of quantitative expertise in KMb and do we too often assume everyone is inherently on the soft measurement side? There seemed to be a growing interest regarding how quantitative and participatory approaches may combine, which is something that interests me enormously in terms of Learning Health Systems and how big data and community/patient involvement can work together to improve care.