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.

Field interventions for climate change mitigation behaviors: A second-order meta-analysis.

Behavioral change is essential to mitigate climate change. To advance current knowledge, we synthesize research on interventions aiming to promote climate change mitigation behaviors in field settings. In a preregistered second-order meta-analysis, we assess the overall effect of 10 meta-analyses, incorporating a total of 430 primary studies. In addition, we assess subgroup analyses for six types of interventions, five behaviors, and three publication bias adjustments. Results showed that climate change mitigation interventions were generally effective (dunadjusted = 0.31, 95% CI [0.30, 0.32]). A follow-up analysis using only unique primary studies, adjusted for publication bias, provides a more conservative overall estimate (d = 0.18, 95% CI [0.13, 0.24]). This translates into a mean treatment effect of 7 percentage points. Furthermore, in a subsample of adequately powered large-scale interventions (n > 9,000, k = 32), the effect was adjusted downward to approximately 2 percentage points. This discrepancy might be because large-scale interventions often target nonvoluntary participants by less direct techniques (e.g., "home energy reports") while small-scale interventions often target voluntary participants by more direct techniques (e.g., face-to-face interactions). Subgroup analyses showed that interventions based on social comparisons or financial incentives were the most effective, while education or feedback was the least effective. These results provide a comprehensive state-of-the-art summary of climate change mitigation interventions, guiding both future research and practice.

On the Efficacy of Accuracy Prompts Across Partisan Lines: An Adversarial Collaboration.

The spread of misinformation is a pressing societal challenge. Prior work shows that shifting attention to accuracy increases the quality of people's news-sharing decisions. However, researchers disagree on whether accuracy-prompt interventions work for U.S. Republicans/conservatives and whether partisanship moderates the effect. In this preregistered adversarial collaboration, we tested this question using a multiverse meta-analysis (k = 21; N = 27,828). In all 70 models, accuracy prompts improved sharing discernment among Republicans/conservatives. We observed significant partisan moderation for single-headline "evaluation" treatments (a critical test for one research team) such that the effect was stronger among Democrats than Republicans. However, this moderation was not consistently robust across different operationalizations of ideology/partisanship, exclusion criteria, or treatment type. Overall, we observed significant partisan moderation in 50% of specifications (all of which were considered critical for the other team). We discuss the conditions under which moderation is observed and offer interpretations.

Out-group animosity drives engagement on social media.

There has been growing concern about the role social media plays in political polarization. We investigated whether out-group animosity was particularly successful at generating engagement on two of the largest social media platforms: Facebook and Twitter. Analyzing posts from news media accounts and US congressional members (n = 2,730,215), we found that posts about the political out-group were shared or retweeted about twice as often as posts about the in-group. Each individual term referring to the political out-group increased the odds of a social media post being shared by 67%. Out-group language consistently emerged as the strongest predictor of shares and retweets: the average effect size of out-group language was about 4.8 times as strong as that of negative affect language and about 6.7 times as strong as that of moral-emotional language-both established predictors of social media engagement. Language about the out-group was a very strong predictor of "angry" reactions (the most popular reactions across all datasets), and language about the in-group was a strong predictor of "love" reactions, reflecting in-group favoritism and out-group derogation. This out-group effect was not moderated by political orientation or social media platform, but stronger effects were found among political leaders than among news media accounts. In sum, out-group language is the strongest predictor of social media engagement across all relevant predictors measured, suggesting that social media may be creating perverse incentives for content expressing out-group animosity.

The Misinformation Susceptibility Test (MIST): A psychometrically validated measure of news veracity discernment.

Interest in the psychology of misinformation has exploded in recent years. Despite ample research, to date there is no validated framework to measure misinformation susceptibility. Therefore, we introduce Verification done, a nuanced interpretation schema and assessment tool that simultaneously considers Veracity discernment, and its distinct, measurable abilities (real/fake news detection), and biases (distrust/naïvité-negative/positive judgment bias). We then conduct three studies with seven independent samples (Ntotal = 8504) to show how to develop, validate, and apply the Misinformation Susceptibility Test (MIST). In Study 1 (N = 409) we use a neural network language model to generate items, and use three psychometric methods-factor analysis, item response theory, and exploratory graph analysis-to create the MIST-20 (20 items; completion time < 2 minutes), the MIST-16 (16 items; < 2 minutes), and the MIST-8 (8 items; < 1 minute). In Study 2 (N = 7674) we confirm the internal and predictive validity of the MIST in five national quota samples (US, UK), across 2 years, from three different sampling platforms-Respondi, CloudResearch, and Prolific. We also explore the MIST's nomological net and generate age-, region-, and country-specific norm tables. In Study 3 (N = 421) we demonstrate how the MIST-in conjunction with Verification done-can provide novel insights on existing psychological interventions, thereby advancing theory development. Finally, we outline the versatile implementations of the MIST as a screening tool, covariate, and intervention evaluation framework. As all methods are transparently reported and detailed, this work will allow other researchers to create similar scales or adapt them for any population of interest.

Oil and gas companies invest in legislators that vote against the environment.

Do campaign contributions from oil and gas companies influence legislators to vote against the environment, or do these companies invest in legislators that have a proven antienvironmental voting record? Using 28 y of campaign contribution data, we find that evidence consistently supports the investment hypothesis: The more a given member of Congress votes against environmental policies, the more contributions they receive from oil and gas companies supporting their reelection.

The effects of communicating uncertainty on public trust in facts and numbers.

Uncertainty is inherent to our knowledge about the state of the world yet often not communicated alongside scientific facts and numbers. In the "posttruth" era where facts are increasingly contested, a common assumption is that communicating uncertainty will reduce public trust. However, a lack of systematic research makes it difficult to evaluate such claims. We conducted five experiments-including one preregistered replication with a national sample and one field experiment on the BBC News website (total n = 5,780)-to examine whether communicating epistemic uncertainty about facts across different topics (e.g., global warming, immigration), formats (verbal vs. numeric), and magnitudes (high vs. low) influences public trust. Results show that whereas people do perceive greater uncertainty when it is communicated, we observed only a small decrease in trust in numbers and trustworthiness of the source, and mostly for verbal uncertainty communication. These results could help reassure all communicators of facts and science that they can be more open and transparent about the limits of human knowledge.

Discussing global warming leads to greater acceptance of climate science.

Climate change is an urgent global issue, with demands for personal, collective, and governmental action. Although a large body of research has investigated the influence of communication on public engagement with climate change, few studies have investigated the role of interpersonal discussion. Here we use panel data with 2 time points to investigate the role of climate conversations in shaping beliefs and feelings about global warming. We find evidence of reciprocal causality. That is, discussing global warming with friends and family leads people to learn influential facts, such as the scientific consensus that human-caused global warming is happening. In turn, stronger perceptions of scientific agreement increase beliefs that climate change is happening and human-caused, as well as worry about climate change. When assessing the reverse causal direction, we find that knowing the scientific consensus further leads to increases in global warming discussion. These findings suggest that climate conversations with friends and family enter people into a proclimate social feedback loop.

Communicating expert consensus increases personal support for COVID-19 mitigation policies.

The Gateway Belief Model (GBM) places perception of a scientific consensus as a key "gateway cognition" with cascading effects on personal beliefs, concern, and ultimately support for public policies. However, few studies seeking to evaluate and extend the model have followed the specification and design of the GBM as originally outlined. We present a more complete test of the theoretical model in a novel domain: the COVID-19 pandemic. In a large multi-country correlational study (N = 7,206) we report that, as hypothesized by the model, perceptions of scientific consensus regarding the threat of COVID-19 predict personal attitudes toward threat and worry over the virus, which are in turn positively associated with support for mitigation policies. We also find causal support for the model in a large pre-registered survey experiment (N = 1,856): experimentally induced increases in perceived consensus have an indirect effect on changes in policy support mediated via changes in personal agreement with the consensus. Implications for the role of expert consensus in science communication are discussed.

How Accurate Are Accuracy-Nudge Interventions? A Preregistered Direct Replication of Pennycook et al. (2020).

As part of the Systematizing Confidence in Open Research and Evidence (SCORE) program, the present study consisted of a two-stage replication test of a central finding by Pennycook et al. (2020), namely that asking people to think about the accuracy of a single headline improves "truth discernment" of intentions to share news headlines about COVID-19. The first stage of the replication test (n = 701) was unsuccessful (p = .67). After collecting a second round of data (additional n = 882, pooled N = 1,583), we found a small but significant interaction between treatment condition and truth discernment (uncorrected p = .017; treatment: d = 0.14, control: d = 0.10). As in the target study, perceived headline accuracy correlated with treatment impact, so that treatment-group participants were less willing to share headlines that were perceived as less accurate. We discuss potential explanations for these findings and an unreported change in the hypothesis (but not the analysis plan) from the preregistration in the original study.

Political polarization on COVID-19 pandemic response in the United States.

Despite calls for political consensus, there is growing evidence that the public response to the COVID-19 pandemic has been politicized in the US. We examined the extent to which this polarization exists among the US public across two national studies. In a representative US sample (N = 699, March 2020) we find that liberals (compared to conservatives) perceive higher risk, place less trust in politicians to handle the pandemic, are more trusting of medical experts such as the WHO, and are more critical of the government response. We replicate these results in a second, pre-registered study (N = 1000; April 2020), and find that results are similar when considering partisanship, rather than political ideology. In both studies we also find evidence that political polarization extends beyond attitudes, with liberals consistently reporting engaging in a significantly greater number of health protective behaviors (e.g., wearing face masks) than conservatives. We discuss the possible drivers of polarization on COVID-19 attitudes and behaviors, and reiterate the need for fostering bipartisan consensus to effectively address and manage the COVID-19 pandemic.

Regulatory assessment and risk management of chemical mixtures: challenges and ways forward.

This paper summarizes current challenges, the potential use of novel scientific methodologies, and ways forward in the risk assessment and risk management of mixtures. Generally, methodologies to address mixtures have been agreed; however, there are still several data and methodological gaps to be addressed. New approach methodologies can support the filling of knowledge gaps on the toxicity and mode(s) of action of individual chemicals. (Bio)Monitoring, modeling, and better data sharing will support the derivation of more realistic co-exposure scenarios. As knowledge and data gaps often hamper an in-depth assessment of specific chemical mixtures, the option of taking account of possible mixture effects in single substance risk assessments is briefly discussed. To allow risk managers to take informed decisions, transparent documentation of assumptions and related uncertainties is recommended indicating the potential impact on the assessment. Considering the large number of possible combinations of chemicals in mixtures, prioritization is needed, so that actions first address mixtures of highest concern and chemicals that drive the mixture risk. As chemicals with different applications and regulated separately might lead to similar toxicological effects, it is important to consider chemical mixtures across legislative sectors.

"Inoculation" to Resist Misinformation.

This JAMA Insights in the Communicating Medicine series explores the concept of "prebunking," a psychological inoculation technique that could help prevent the spread of misinformation.

The role of climate in human aggression and violence: Towards a broader conception.

The psychological processes that predict aggressive behaviour are also typically associated with violent self-harm (e.g., poor self-control). Yet, although human violence (towards others) appears to increase with proximity to the equator, suicide rates tend to decrease. In the light of this empirical puzzle, I argue that Van Lange et al.'s CLASH model would benefit from a broader conceptualization of human aggression.

When does deprivation motivate future-oriented thinking? The case of climate change.

Pepper & Nettle overstate cross-domain evidence of present-oriented thinking among lower-socioeconomic-status (SES) groups and overlook key social and contextual drivers of temporal decision making. We consider psychological research on climate change - a quintessential intertemporal problem that implicates inequities and extrinsic mortality risk - documenting more future-oriented thinking among low- compared to high-SES groups.

Regulatory assessment of chemical mixtures: Requirements, current approaches and future perspectives.

This paper reviews regulatory requirements and recent case studies to illustrate how the risk assessment (RA) of chemical mixtures is conducted, considering both the effects on human health and on the environment. A broad range of chemicals, regulations and RA methodologies are covered, in order to identify mixtures of concern, gaps in the regulatory framework, data needs, and further work to be carried out. Also the current and potential future use of novel tools (Adverse Outcome Pathways, in silico tools, toxicokinetic modelling, etc.) in the RA of combined effects were reviewed. The assumptions made in the RA, predictive model specifications and the choice of toxic reference values can greatly influence the assessment outcome, and should therefore be specifically justified. Novel tools could support mixture RA mainly by providing a better understanding of the underlying mechanisms of combined effects. Nevertheless, their use is currently limited because of a lack of guidance, data, and expertise. More guidance is needed to facilitate their application. As far as the authors are aware, no prospective RA concerning chemicals related to various regulatory sectors has been performed to date, even though numerous chemicals are registered under several regulatory frameworks.

Highlighting consensus among medical scientists increases public support for vaccines: evidence from a randomized experiment.

BACKGROUND: A substantial minority of American adults continue to hold influential misperceptions about childhood vaccine safety. Growing public concern and refusal to vaccinate poses a serious public health risk. Evaluations of recent pro-vaccine health communication interventions have revealed mixed results (at best). This study investigated whether highlighting consensus among medical scientists about childhood vaccine safety can lower public concern, reduce key misperceptions about the discredited autism-vaccine link and promote overall support for vaccines. METHODS: American adults (N = 206) were invited participate in an online survey experiment. Participants were randomly assigned to either a control group or to one of three treatment interventions. The treatment messages were based on expert-consensus estimates and either normatively described or prescribed the extant medical consensus: "90 % of medical scientists agree that vaccines are safe and that all parents should be required to vaccinate their children". RESULTS: Compared to the control group, the consensus-messages significantly reduced vaccine concern (M = 3.51 vs. M = 2.93, p < 0.01) and belief in the vaccine-autism-link (M = 3.07 vs M = 2.15, p < 0.01) while increasing perceived consensus about vaccine safety (M = 83.93 vs M = 89.80, p < 0.01) and public support for vaccines (M = 5.66 vs M = 6.22, p < 0.01). Mediation analysis further revealed that the public's understanding of the level of scientific agreement acts as an important "gateway" belief by promoting public attitudes and policy support for vaccines directly as well as indirectly by reducing endorsement of the discredited autism-vaccine link. CONCLUSION: These findings suggest that emphasizing the medical consensus about (childhood) vaccine safety is likely to be an effective pro-vaccine message that could help prevent current immunization rates from declining. We recommend that clinicians and public health officials highlight and communicate the high degree of medical consensus on (childhood) vaccine safety when possible.