Publications by authors named "Bavel J"

People generate evaluations of different attitude objects based on their goals and aspects of the social context. Prior research suggests that people can shift between at least three types of evaluations to judge whether something is good or bad: (how costly or beneficial it is), (whether it is aligned with moral norms), and (whether it feels good; Van Bavel et al., 2012).

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In addition to social determinants of health, such as economic resources, education, access to care and various environmental factors, there is growing evidence that political polarization poses a substantial risk to individual and collective well-being. Here we review the impact of political polarization on public health. We describe the different forms of polarization and how they are connected to health outcomes, highlighting the COVID-19 pandemic as a case study of the health risks of polarization.

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Article Synopsis
  • Scholars are concerned that deep partisan divides among the public pose a risk to American democracy.
  • A large study with over 32,000 participants tested 25 different strategies aimed at decreasing partisan animosity and support for undemocratic practices.
  • Results showed that highlighting relatable individuals with differing beliefs and emphasizing shared identities were effective at reducing animosity, while correcting misunderstandings about rival views helped lessen support for undemocratic actions.
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  • The paper explores how modern technology, especially social media, distorts social norms, creating a "funhouse mirror" effect that can lead to extreme perceptions compared to real-life norms.
  • It discusses the phenomenon of pluralistic ignorance, where false beliefs about social norms can mislead individuals about what is actually common behavior or opinion.
  • By integrating findings from various fields, the authors propose a framework to understand and address these distortions, highlighting the negative impacts on both individual and societal levels, like increased misunderstanding and polarization.
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  • The study of intellectual humility (IH) is shifting from a focus on individuals to understanding it at a collective level, recognizing how group members can be aware of each other's intellectual limitations.
  • Since people are often better at identifying others' weaknesses, achieving IH may be easier within groups than individually.
  • The research suggests creating environments that foster a culture of IH, which could persist even as group members change, and highlights potential areas for future research.
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Objectives: Analysis of the association of mediolateral episiotomy (MLE) with obstetric anal sphincter injury (OASI) in women with spontaneous vaginal delivery.

Design: Population-based cohort study with data from the Netherlands Perinatal Registry, describing 541 055 women who delivered a singleton live born infant in cephalic presentation spontaneously at term. Risk indicators for OASI were tested using univariate and multivariate analysis.

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  • Scientists did a big survey with over 59,000 people from 63 countries to understand how people think about climate change!
  • They tested different ways to encourage people to believe in climate change and support actions to help the environment!
  • The study includes lots of information and data that can help others learn more about what influences people's actions on climate change around the world!
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  • The research investigates the use of GPT models for automated psychological text analysis across 15 datasets consisting of nearly 48,000 annotated tweets and news headlines in 12 languages.
  • Results indicate that GPT outperformed traditional English-language dictionary analysis and sometimes matched or exceeded the performance of advanced machine learning models, particularly benefiting lesser-spoken languages.
  • The study suggests that GPT simplifies and democratizes automated text analysis, making it more accessible for researchers with little coding experience, and encourages further research in understudied languages.
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Although much of human morality evolved in an environment of small group living, almost 6 billion people use the internet in the modern era. We argue that the technological transformation has created an entirely new ecosystem that is often mismatched with our evolved adaptations for social living. We discuss how evolved responses to moral transgressions, such as compassion for victims of transgressions and punishment of transgressors, are disrupted by two main features of the online context.

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Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems.

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The tendency for people to consider themselves morally good while behaving selfishly is known as moral hypocrisy. Influential work by Valdesolo and DeSteno (2007) found evidence for intergroup moral hypocrisy such that people were more forgiving of transgressions when they were committed by an in-group member than an out-group member. We conducted two experiments to examine moral hypocrisy and group membership in an online paradigm with Prolific workers from the United States: a direct replication of the original work with minimal groups ( = 610; nationally representative) and a conceptual replication with political groups ( = 606; 50% Democrats and 50% Republicans).

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Social media takes advantage of people's predisposition to attend to threatening stimuli by promoting content in algorithms that capture attention. However, this content is often not what people expressly state they would like to see. We propose that social media companies should weigh users' expressed preferences more heavily in algorithms.

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A major barrier to climate change mitigation is the political polarization of climate change beliefs. In a global experiment conducted in 60 countries (N = 51,224), we assess the differential impact of eleven climate interventions across the ideological divide. At baseline, we find political polarization of climate change beliefs and policy support globally, with people who reported being liberal believing and supporting climate policy more than those who reported being conservative (Cohen's d = 0.

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Background: The aim of this study was to investigate the link between parenting and parents' perspectives on health-related decision making for adolescents. During adolescence, there is a gradual increase in responsibility and autonomy, which influences parenting behavior and child development. Understanding how parenting is associated with parents' views on medical decision making is crucial in the context of the parent-child-physician triad.

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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.

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Article Synopsis
  • Effective global behavior change is crucial for reducing climate change, but it's unclear which strategies motivate people to shift their beliefs and actions.
  • A study tested 11 interventions on nearly 60,000 participants across 63 countries, finding small effectiveness primarily among non-skeptics and varied results across different outcomes.
  • Key results showed that reducing psychological distance strengthened beliefs, writing a letter to a future generation increased policy support, and inducing negative emotions encouraged information sharing, but no strategy successfully boosted tree-planting efforts.
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The spread of misinformation threatens democratic societies, hampering informed decision-making. Partisan identity biases perceptions of reality, promoting false beliefs. The Identity-based Model of Political Belief explains how social identity shapes information processing and contributes to misinformation.

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Interventions to counter misinformation are often less effective for polarizing content on social media platforms. We sought to overcome this limitation by testing an identity-based intervention, which aims to promote accuracy by incorporating normative cues directly into the social media user interface. Across three pre-registered experiments in the US ( = 1709) and UK ( = 804), we found that crowdsourcing accuracy judgements by adding a count (next to the count) reduced participants' reported likelihood to share inaccurate information about partisan issues by 25% (compared with a control condition).

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An electrical storm of Torsade de Pointes arrhythmias (TdP) can be reproducibly induced in the anesthetized chronic AV-block (CAVB) dog by infusion of the I-blocker dofetilide. Earlier studies showed that these arrhythmias 1) arise from locations with high spatial dispersion in repolarization (SDR) and 2) can be suppressed by high-rate pacing. We examined whether suppression of TdP by high-rate pacing is established through a decrease in SDR in the CAVB dog.

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  • The paper evaluated 747 research articles on 19 policy recommendations from behavioral science aimed at reducing the impacts of COVID-19.
  • Both independent review teams found evidence supporting 18 out of the 19 claims, with 89% of the claims backed by robust data.
  • Key findings highlighted that cultural factors, misinformation, and trusted leadership significantly influenced policy effectiveness, while targeted messaging showed mixed outcomes.
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Research has shown that severity of depression increased in freshmen during their first months at university due to increased social and academic pressures. Since the start of the COVID-19 pandemic, several cross-sectional studies have suggested that levels of depression in university students are higher than before the pandemic, but longitudinal data are largely lacking. This study investigated severity of depression and negative affect linked to the pandemic among freshmen during their first semester at a large university in Flanders, Belgium.

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Nearly five billion people around the world now use social media, and this number continues to grow. One of the primary goals of social media platforms is to capture and monetize human attention. One means by which individuals and groups can capture attention and drive engagement on these platforms is by sharing morally and emotionally evocative content.

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Polarization has been rising in the United States of America for the past few decades and now poses a significant-and growing-public-health risk. One of the signature features of the American response to the COVID-19 pandemic has been the degree to which perceptions of risk and willingness to follow public-health recommendations have been politically polarized. Although COVID-19 has proven more lethal than any war or public-health crisis in American history, the deadly consequences of the pandemic were exacerbated by polarization.

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Recent studies have documented the type of content that is most likely to spread widely, or go "viral," on social media, yet little is known about people's perceptions of what goes viral or what should go viral. This is critical to understand because there is widespread debate about how to improve or regulate social media algorithms. We recruited a sample of participants that is nationally representative of the U.

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In four experiments covering three different life domains, participants made future predictions in what they considered the most realistic scenario, an optimistic best-case scenario, or a pessimistic worst-case scenario ( = 2,900 Americans). Consistent with a , participants made "realistic" predictions that were much closer to their best-case scenario than to their worst-case scenario. We found the same best-case asymmetry in health-related predictions during the COVID-19 pandemic, for romantic relationships, and a future presidential election.

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