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

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

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

Scientific evidence regularly guides policy decisions 1, with behavioural science increasingly part of this process 2. In April 2020, an influential paper 3 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

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

DATA AVAILABILITY : All data and study material are provided either in the Supplementary information or through the two online repositories (OSF and Tableau Public, both accessible via https://psyarxiv.com/58udn). No code was used for analyses in this work.Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper 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.The National Science Foundation; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education); the Ministry of Science, Technology and Innovation | Conselho Nacional de Desenvolvimento Científico e Tecnológico (National Council for Scientific and Technological Development); National Science Foundation grants; the European Research Council; the Canadian Institutes of Health Research.http://www.nature.com/naturehj2024Gordon Institute of Business Science (GIBS)Non

GNpapo: de volta para o futuro

O GNova, laboratório de inovação da Enap, promoveu, no dia 07 de outubro, uma edição especial do GNpapo. Já no clima da 5ª Semana de Inovação, o bate-papo desta vez contou com quatro especialistas internacionais, referências mundiais em suas áreas de atuação, que nos levarão de volta para o futuro. Aqui estão as apresentações do 1º painel "O futuro das ciências comportamentais em governo" e do 3º painel "O futuro dos laboratórios de inovação no setor público".3 apresentações em slidesOs links para os vídeos no Youtube estão campo "Endereço eletrônico"InovaçãoMark Hallerberg é Pró-reitor de Pesquisa e Professor de Gestão Pública e Economia Política da Hertie School of Governance, em Berlim. Na Hertie School, leciona a disciplina “O que torna os laboratórios de inovação pública efetivos?”, entre outras. Em sua extensa carreira, assessorou várias organizações, inclusive o Banco Central Europeu, a Corporação Alemã de Cooperação Internacional (GIZ), Ernst e Young, o Banco Interamericano de Desenvolvimento, o Fundo Monetário Internacional e o Banco Mundial. Obteve seu PhD em Ciência Política pela Universidade da Califórnia, Los Angeles (UCLA) em 1995.Sabine Junginger é Professora de Negócios e Design da The New Design University. Especialista internacionalmente reconhecida em design centrado no ser humano e seu papel na inovação, ela é autora de dois livros que se se tornaram leitura obrigatória para estudantes de design management ao redor do mundo (The Handbook of Design Management, 2011 e Designing Business and Management, 2016). Em 2017, também publicou o livro Transforming Public Services by Design: Re-orienting Public Policies, Organizations and Services around People. Já aconselhou iniciativas governamentais em diversos países (Mindlab, Dinamarca; DesignGov, Austrália) e é membro do Politics for Tomorrow, uma iniciativa focada em conectar a teoria e a prática do design com a formulação e implementação de políticas públicas

Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation

Standardisation of a new model of H9N2/Escherichia coli challenge in broilers in the Lebanon

Primary infection by low pathogenic avian influenza (LPAI) predisposes for secondary infection by Escherichia coli in poultry, leading to significant economic losses. Future research in control of this ailment requires the establishment of a successful controlled challenge by avian influenza virus (AIV)/E. coli. Six groups of broilers (6 birds/group) were included for the standardisation of the controlled challenge by AIV/E. coli. Birds in groups 1, 2, 3, 4 and 5 received an intra-tracheal challenge of 0.5 ml of two haemagglutinating units of H9N2 virus at 20 days of age. At the age of 23 days, birds in group 1 received an intra-thoracic (right air sac)-E. coli challenge equivalent to 1.6 × 109 colony-forming units (cfu)/0.5 ml/bird, while birds in groups 2, 3, 4 and 5 received E. coli by the same route and in the following respective decreasing order of viable cells: 1.6 × 106, 1.6 × 105, 1.6 × 104 and 1.6 × 103 cfu. Birds in control group 6 were deprived of H9N2 and E. coli challenge. Results showed significant early mortality in group 1 that was challenged with the highest number of E. coli, in comparison to groups 2-6 (p0.05). The frequencies of four signs at 2 days and at 5 days post E. coli challenge (conjunctivitis, diarrhoea, ocular exudates and rales) in the surviving birds of groups 2-5 were most often higher than those observed in control group 6 (p<0.05). These four signs and five gross lesions (abdominal airsacculitis, left thoracic airsacculitis, pericarditis, right thoracic airsacculitis and tracheitis) had a decreasing pattern of frequency related to a decrease in the E. coli count used in the challenge

Evaluating expectations from social and behavioral science about COVID-19 and lessons for the next pandemic

Social and behavioral science research proliferated during the COVID-19 pandemic, reflecting the substantial increase in influence of behavioral science in public health and public policy more broadly. This review presents a comprehensive assessment of 742 scientific articles on human behavior during COVID-19. Two independent teams evaluated 19 substantive policy recommendations (“claims”) on potentially critical aspects of behaviors during the pandemic drawn from the most widely cited behavioral science papers on COVID-19. Teams were made up of original authors and an independent team, all of whom were blinded to other team member reviews throughout. Both teams found evidence in support of 16 of the claims; for two claims, teams found only null evidence; and for no claims did the teams find evidence of effects in the opposite direction. One claim had no evidence available to assess. Seemingly due to the risks of the pandemic, most studies were limited to surveys, highlighting a need for more investment in field research and behavioral validation studies. The strongest findings indicate interventions that combat misinformation and polarization, and to utilize effective forms of messaging that engage trusted leaders and emphasize positive social norms