Bringing together behavioural scientists for crisis knowledge management

Without effective treatment or vaccine, social measures remain at the heart of the world’s response to the COVID-19 pandemic. With this, behaviour change remains one of the top three scientific priorities for the coming months, according to the Lancet, and the behavioural sciences are implicated throughout the complex task of bringing societies out of lockdown.
Providing a suitable evidence base for these high-stakes policy decisions means drawing together research across presently, at best, loosely interconnected sub-fields and disciplines, formulating and conducting new research, distilling findings into formats digestible by policymakers, journalists, and the public, and providing expert guidance to decision-making bodies.

What feels like years ago, we wrote a paper to prompt debate on how the behavioural sciences could reconfigure to rise to the challenge by finding new ways of knowledge creation, integration, and dissemination.

A new model of “proper science without the drag that accelerates knowledge production without sacrificing quality is needed. Constructive, critical input at all stages of the research process, from study idea, through design, to data analysis is the obvious way for improving research quality while cutting down on time. The pressing need for such input has already become apparent: early voices warned about the adverse impact of fast research under pressure, and this can now be seen in poor quality studies, needless reduplication of effort, and irresponsible amplification of problematic results through media pick-up.

Welcome or not, pre-prints have become the crisis norm. At the time of writing, there are over 200 COVID-19 themed research articles on PsyArXiv alone. Collaborative, online, alternative review models, under discussion for several years, are becoming a necessity because these pre-prints are already “out there” as part of the emerging evidence-base.

We must also adapt knowledge integration. Critical evaluation never stops with publication, but the ‘normal’ process of integrating new research with existing knowledge, e.g., through review articles, is slow. Twitter, in particular, is playing an ever-greater role in dissemination and in post-publication critique, but does not lend itself to knowledge integration. Aggregation should be a key feature of our response. Optimal responding would see a degree of synthesis well beyond the slightly haphazard publication of review papers. And we need summaries suitable for policy-makers and journalists in wiki-style, accessible, formats. Natural language processing and machine learning will feature in that aggregation and integration on a scale not seen before.

In all of this we need to manage expertise. Researchers, policy-makers and journalists need to know who the experts are, but expertise is also changing as a result of the crisis as people take up new topics of research. And we need to bring together scientists from different disciplines and even subfields within a discipline addressing similar questions with different approaches. Current ‘expert databases’ (e.g., from learned societies) are too exclusive, static, and focussed on past achievements not present research.

We believe these challenges can only by a broad, community-based infrastructure that draws together researchers across career stages, institutions, and across the world’s regions. This would make available all of our resources, instead of just drawing on an overstretched, expert few, providing resilience through redundancy, as well as epistemic diversity: There are sound theoretical reasons why diversity is beneficial to outcomes and the crisis is already replete with high-profile opinions and recommendations that were misguided or unrealistic in ways that were readily apparent to others with different, complementary, perspectives or expertise.

transparent, digital, community-based forum for scientific exchange can solve the key challenges just set out: stimulating collaboration and knowledge exchange; boosting the quality of new studies through early feedback; providing rapid post-publication evaluation, critique and integration with extant knowledge; supporting the semi-automated construction of a large-scale knowledge-base; providing an interface for journalists, policy-makers, and citizens seeking to pose questions; and providing a dynamically evolving database of experts, accessible both to other scientists, and to policy-makers and journalists.

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