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

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

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

Scientifc evidence regularly guides policy decisions1 , with behavioural science increasingly part of this process2 . In April 2020, an infuential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to eforts 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 fnding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy efectiveness. 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 efects and there were no efects for highlighting individual benefts or protecting others. No available evidence existed to assess any distinct diferences in efects 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 scientifc evidence in policy formulation and prioritization.Fil: Ruggeri, Kai. New York Air National Guard; Estados Unidos. Columbia University Mailman School of Public Health; Estados Unidos. University of Cambridge; Estados UnidosFil: Stock, Friederike. Max Planck Institute for Human Development; Alemania. Humboldt-Universität zu Berlin; AlemaniaFil: Haslam, S. Alexander. University of Queensland; AustraliaFil: Capraro, Valerio. Università degli Studi di Milano; ItaliaFil: Boggio, Paulo. Universidade Presbiteriana Mackenzie; Brasil. National Institute of Science and Technology on Social and Affective Neuroscience; BrasilFil: Ellemers, Naomi. Utrecht University; Países Bajos. University of Utrecht; Países BajosFil: Cichocka, Aleksandra. University Of Kent; Reino UnidoFil: Douglas, Karen M.. University Of Kent; Reino UnidoFil: Rand, David G.. Massachusetts Institute of Technology; Estados UnidosFil: van der Linden, Sander. University of Cambridge; Estados UnidosFil: Cikara, Mina. Harvard University; Estados UnidosFil: Finkel, Eli J.. Northwestern University; Estados UnidosFil: Druckman, James N.. Northwestern University; Estados UnidosFil: Wohl, Michael J. A.. Carleton University; CanadáFil: Petty, Richard E.. Ohio State University; Estados UnidosFil: Tucker, Joshua A.. University of New York; Estados UnidosFil: Shariff, Azim. University of British Columbia; CanadáFil: Gelfand, Michele. University of Stanford; Estados UnidosFil: Packer, Dominic. Lehigh University; Estados UnidosFil: Jetten, Jolanda. University of Queensland; AustraliaFil: Van Lange, Paul A. M.. Universitat zu Köln; Alemania. Vrije Universiteit Amsterdam; Países BajosFil: Pennycook, Gordon. Cornell University; Estados UnidosFil: Peters, Ellen. University of Oregon; Estados UnidosFil: Navajas Ahumada, Joaquin Mariano. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Papa, Francesca. Organisation for Economic Co-operation and Development; FranciaFil: Galizzi, Matteo M.. The London School of Economics and Political Science; Reino UnidoFil: Milkman, Katherine L.. University of Pennsylvania; Estados UnidosFil: Petrović, Marija. University of Belgrade; SerbiaFil: Van Bavel, Jay J.. University of New York; Estados UnidosFil: Willer, Robb. University of Stanford; Estados Unido

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

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

Comparison of the benefits of cochlear implantation versus contra-lateral routing of signal hearing aids in adult patients with single-sided deafness: study protocol for a prospective within-subject longitudinal trial

Background Individuals with a unilateral severe-to-profound hearing loss, or single-sided deafness, report difficulty with listening in many everyday situations despite having access to well-preserved acoustic hearing in one ear. The standard of care for single-sided deafness available on the UK National Health Service is a contra-lateral routing of signals hearing aid which transfers sounds from the impaired ear to the non-impaired ear. This hearing aid has been found to improve speech understanding in noise when the signal-to-noise ratio is more favourable at the impaired ear than the non-impaired ear. However, the indiscriminate routing of signals to a single ear can have detrimental effects when interfering sounds are located on the side of the impaired ear. Recent published evidence has suggested that cochlear implantation in individuals with a single-sided deafness can restore access to the binaural cues which underpin the ability to localise sounds and segregate speech from other interfering sounds. Methods/Design The current trial was designed to assess the efficacy of cochlear implantation compared to a contra-lateral routing of signals hearing aid in restoring binaural hearing in adults with acquired single-sided deafness. Patients are assessed at baseline and after receiving a contra-lateral routing of signals hearing aid. A cochlear implant is then provided to those patients who do not receive sufficient benefit from the hearing aid. This within-subject longitudinal design reflects the expected care pathway should cochlear implantation be provided for single-sided deafness on the UK National Health Service. The primary endpoints are measures of binaural hearing at baseline, after provision of a contra-lateral routing of signals hearing aid, and after cochlear implantation. Binaural hearing is assessed in terms of the accuracy with which sounds are localised and speech is perceived in background noise. The trial is also designed to measure the impact of the interventions on hearing- and health-related quality of life. Discussion This multi-centre trial was designed to provide evidence for the efficacy of cochlear implantation compared to the contra-lateral routing of signals. A purpose-built sound presentation system and established measurement techniques will provide reliable and precise measures of binaural hearing. Trial registration Current Controlled Trials http://www.controlled-trials.com/ISRCTN33301739 (05/JUL/2013

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

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features

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