A synthesis of evidence for policy from behavioural science during COVID-19

dc.contributor.authorRuggeri K.
dc.contributor.authorStock F.
dc.contributor.authorHaslam S.A.
dc.contributor.authorCapraro V.
dc.contributor.authorBoggio P.
dc.contributor.authorEllemers N.
dc.contributor.authorCichocka A.
dc.contributor.authorDouglas K.M.
dc.contributor.authorRand D.G.
dc.contributor.authorvan der Linden S.
dc.contributor.authorCikara M.
dc.contributor.authorFinkel E.J.
dc.contributor.authorDruckman J.N.
dc.contributor.authorWohl M.J.A.
dc.contributor.authorPetty R.E.
dc.contributor.authorTucker J.A.
dc.contributor.authorShariff A.
dc.contributor.authorGelfand M.
dc.contributor.authorPacker D.
dc.contributor.authorJetten J.
dc.contributor.authorVan Lange P.A.M.
dc.contributor.authorPennycook G.
dc.contributor.authorPeters E.
dc.contributor.authorBaicker K.
dc.contributor.authorCrum A.
dc.contributor.authorWeeden K.A.
dc.contributor.authorNapper L.
dc.contributor.authorTabri N.
dc.contributor.authorZaki J.
dc.contributor.authorSkitka L.
dc.contributor.authorKitayama S.
dc.contributor.authorMobbs D.
dc.contributor.authorSunstein C.R.
dc.contributor.authorAshcroft-Jones S.
dc.contributor.authorTodsen A.L.
dc.contributor.authorHajian A.
dc.contributor.authorVerra S.
dc.contributor.authorBuehler V.
dc.contributor.authorFriedemann M.
dc.contributor.authorHecht M.
dc.contributor.authorMobarak R.S.
dc.contributor.authorKarakasheva R.
dc.contributor.authorTunte M.R.
dc.contributor.authorYeung S.K.
dc.contributor.authorRosenbaum R.S.
dc.contributor.authorLep Z.
dc.contributor.authorYamada Y.
dc.contributor.authorHudson S.-K.T.J.
dc.contributor.authorMacchia L.
dc.contributor.authorSoboleva I.
dc.contributor.authorDimant E.
dc.contributor.authorGeiger S.J.
dc.contributor.authorJarke H.
dc.contributor.authorWingen T.
dc.contributor.authorBerkessel J.B.
dc.contributor.authorMareva S.
dc.contributor.authorMcGill L.
dc.contributor.authorPapa F.
dc.contributor.authorVeckalov B.
dc.contributor.authorAfif Z.
dc.contributor.authorBuabang E.K.
dc.contributor.authorLandman M.
dc.contributor.authorTavera F.
dc.contributor.authorAndrews J.L.
dc.contributor.authorBursalioglu A.
dc.contributor.authorZupan Z.
dc.contributor.authorWagner L.
dc.contributor.authorNavajas J.
dc.contributor.authorVranka M.
dc.contributor.authorKasdan D.
dc.contributor.authorChen P.
dc.contributor.authorHudson K.R.
dc.contributor.authorNovak L.M.
dc.contributor.authorTeas P.
dc.contributor.authorRachev N.R.
dc.contributor.authorGalizzi M.M.
dc.contributor.authorMilkman K.L.
dc.contributor.authorPetrovic M.
dc.contributor.authorVan Bavel J.J.
dc.contributor.authorWiller R.
dc.date.accessioned2024-03-12T19:07:13Z
dc.date.available2024-03-12T19:07:13Z
dc.date.issued2024
dc.description.abstract© 2023, The Author(s).Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.
dc.description.firstpage134
dc.description.issuenumber7993
dc.description.lastpage147
dc.description.volume625
dc.identifier.doi10.1038/s41586-023-06840-9
dc.identifier.issn1476-4687
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/33953
dc.relation.ispartofNature
dc.rightsAcesso Aberto
dc.titleA synthesis of evidence for policy from behavioural science during COVID-19
dc.typeArtigo
local.scopus.citations8
local.scopus.eid2-s2.0-85179671440
local.scopus.subjectBehavioral Sciences
local.scopus.subjectCOVID-19
local.scopus.subjectHumans
local.scopus.subjectPandemics
local.scopus.subjectPolicy
local.scopus.updated2024-06-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85179671440&origin=inward
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