Behavioural science is unlikely to change the world without a heterogeneity revolution

Identifying heterogeneity using recursive partitioning: evidence from SMS nudges encouraging voluntary retirement savings in Mexico

Individuals regularly struggle to save for retirement. Using a large-scale field experiment ( N = 97 , 149 ) in Mexico, we test the effectiveness of several behavioral interventions relative to existing policy and each other geared toward improving voluntary retirement savings contributions. We find that an intervention framing savings as a way to secure one's family future significantly improves contribution rates. We leverage recursive partitioning techniques and identify that the overall positive treatment effect masks subpopulations where the treatment is even more effective and other groups where the treatment has a significant negative effect, decreasing contribution rates. Accounting for this variation is significant for theoretical and policy development as well as firm profitability. Our work also provides a methodological framework for how to better design, scale, and deploy behavioral interventions to maximize their effectiveness.

What can machine learning teach us about habit formation? Evidence from exercise and hygiene

We apply a machine learning technique to characterize habit formation in two large panel data sets with objective measures of 1) gym attendance (over 12 million observations) and 2) hospital handwashing (over 40 million observations). Our Predicting Context Sensitivity (PCS) approach identifies context variables that best predict behavior for each individual. This approach also creates a time series of overall predictability for each individual. These time series predictability values are used to trace a habit formation curve for each individual, operationalizing the time of habit formation as the asymptotic limit of when behavior becomes highly predictable. Contrary to the popular belief in a "magic number" of days to develop a habit, we find that it typically takes months to form the habit of going to the gym but weeks to develop the habit of handwashing in the hospital. Furthermore, we find that gymgoers who are more predictable are less responsive to an intervention designed to promote more gym attendance, consistent with past experiments showing that habit formation generates insensitivity to reward devaluation.

Beyond cognitive deficits: how social class shapes social cognition

Lower social class is thought to contribute to poorer executive functioning and working memory. Nevertheless, lower social class individuals consistently outperform their higher-class counterparts on social cognitive tasks that rely on similar underlying cognitive processes (e.g., working memory and executive functioning). Why would lower social class inhibit such processes in one domain, but promote them in another? We argue that features of lower-class communities (e.g., resource scarcity) promote social cognition via cultural processes. We then argue that social cognition involves partially unique task and neural demands that are separate from nonsocial cognition. We conclude that unique task and neural demands, together with the distinctive cognitive proclivities of lower- and higher-class cultures, can explain variable associations between social class and cognitive functioning.

Activation Versus Change as a Principle Underlying Intervention Strategies to Promote Health Behaviors

BACKGROUND AND PURPOSE

Interventions are effective in promoting health behavior change to the extent that (a) intervention strategies modify targets (i.e., mechanisms of action), and (b) modifying targets leads to changes in behavior. To complement taxonomies that characterize the variety of strategies used in behavioral interventions, we outline a new principle that specifies how strategies modify targets and thereby promote behavior change. We distinguish two dimensions of targets-value (positive vs. negative) and accessibility (activation level)-and show that intervention strategies operate either by altering the value of what people think, feel, or want (target change) or by heightening the accessibility of behavior-related thoughts, feelings, and goals (target activation).

METHODS AND RESULTS

We review strategies designed to promote target activation and find that nudges, cue-reminders, goal priming, the question-behavior effect, and if-then planning are each effective in generating health behavior change, and that their effectiveness accrues from heightened accessibility of relevant targets. We also identify several other strategies that may operate, at least in part, via target activation (e.g., self-monitoring, message framing, anticipated regret inductions, and habits).

CONCLUSIONS

The Activation Vs. Change Principle (AVCP) offers a theoretically grounded and parsimonious means of distinguishing among intervention strategies. By focusing on how strategies modify targets, the AVCP can aid interventionists in deciding which intervention strategies to deploy and how to combine different strategies in behavioral trials. We outline a research agenda that could serve to further enhance the design and delivery of interventions to promote target activation.

Factors associated with contact tracing compliance among communities of color in the first year of the COVID-19 pandemic

RATIONALE

The disproportionate impact of COVID-19 on communities of color has raised questions about the unique experiences within these communities not only in terms of becoming infected with COVID-19 but also mitigating its spread. The utility of contact tracing for managing community spread and supporting economic reopening is contingent upon, in part, compliance with contact tracer requests.

OBJECTIVE

We investigated how trust in and knowledge of contact tracers influence intentions to comply with tracing requests and whether or not these relationships and associated antecedent factors differ between communities of color.

METHOD

Data were collected from a U.S. sample of 533 survey respondents from Fall (2020) to Spring 2021. Multi-group SEM tested quantitative study hypotheses separately for Black, AAPI, Latinx, and White sub-samples. Qualitative data were collected via open-ended questions to inform the roles of trust and knowledge in contact tracing compliance.

RESULTS

Trust in contact tracers was associated with increased intentions to comply with tracing requests and significantly mediated the positive relationship between trust in healthcare professionals and government health officials with compliance intentions. Yet, the indirect effects of trust in government health officials on compliance intentions were significantly weaker for the Black, Latinx, and AAPI samples compared to Whites, suggesting this strategy for increasing compliance may not be as effective among communities of color. Health literacy and contact tracing knowledge played a more limited role in predicting compliance intentions directly or indirectly, and one that was inconsistent across racial groups. Qualitative results reinforce the importance of trust relative to knowledge for increasing tracing compliance intentions.

CONCLUSIONS

Building trust in contact tracers, more so than increasing knowledge, may be key to encouraging contact tracing compliance. Differences among communities of color and between these communities and Whites inform the policy recommendations provided for improving contact tracing success.

Universities claim to value community-engaged scholarship: So why do they discourage it?

With current crises of academic relevance and legitimacy, there is a need for epistemic equity inherent to community-engaged research. Scholars in science communication and science and technology studies have analyzed, advocated for, and conducted public engagement in pursuit of this goal. However, despite desires to celebrate public engagement, US academic institutions and organizations often present barriers to meaningful community-engaged research. From tenure and promotion requirements, to lack of recognition and resources, universities in the American academic landscape are not currently organized to support such work. In this article, we offer a conceptual framework to examine the complex structural dimensions of academic institutions that have systematically discouraged and devalued faculty participation in community-engaged scholarship. We outline four such structural dimensions: interrogating epistemic biases, neoliberalist tendencies, gendered norms, and colonial-racist defaults. Our goal is to illuminate processes that could inform interventions to bridge the gap between academic aspirations for community-engaged work and current actions in the academy that undermine it.

A manifesto for applying behavioural science

Recent years have seen a rapid increase in the use of behavioural science to address the priorities of public and private sector actors. There is now a vibrant ecosystem of practitioners, teams and academics building on each other's findings across the globe. Their focus on robust evaluation means we know that this work has had an impact on important issues such as antimicrobial resistance, educational attainment and climate change. However, several critiques have also emerged; taken together, they suggest that applied behavioural science needs to evolve further over its next decade. This manifesto for the future of applied behavioural science looks at the challenges facing the field and sets out ten proposals to address them. Meeting these challenges will mean that behavioural science is better equipped to help to build policies, products and services on stronger empirical foundations-and thereby address the world's crucial challenges.

Attachment in individuals with eating disorders compared to community controls: A systematic review and meta-analysis

OBJECTIVE

Individuals with eating disorders are known to have higher rates of insecure attachment compared to community controls, but the factors underlying this finding are poorly understood. We conducted the first meta-analysis comparing attachment in eating disorder samples compared to community controls that included quality assessment, publication bias and moderation analysis.

METHOD

We pre-registered our meta-analysis (CRD42019146799) and followed PRISMA guidelines. We searched PsychINFO, Embase, Medline, CINAHL, and Scopus for publications. Attachment scores were extracted, and Cohen's d calculated for each study using a random effects model.

RESULTS

In total, 35 studies were included in the meta-analysis and six studies were summarized in a narrative review. Eating disorder samples showed higher rates of insecure attachment compared to community controls, with a large effect size, across measurement methods and different attachment dimensions. Blinding of assessors moderated effect sizes for attachment interview studies, but no other moderators were significant.

DISCUSSION

Risk of insecure attachment is elevated in individuals with eating disorders, albeit heterogeneity is high and largely unexplained. Clinicians may need to take this into account in their work, particularly given the association between attachment insecurity and challenges to therapeutic alliance. Future studies comparing eating disorder samples with community samples should control for general psychopathology.

PUBLIC SIGNIFICANCE

Attachment is a broad concept referring to a person's thoughts, feelings and behaviors in relation to close others. This systematic review and meta-analysis found that individuals with eating disorders are lower in attachment security than community controls, regardless of attachment construct or measurement approach. Attachment may be relevant in influencing eating disorder recovery, the development of therapeutic alliance, and potentially clinical outcomes, although more research is needed.

How Do People Come to Judge What Is "Reasonable"? Effects of Legal and Sociological Systems on Human Psychology

How do people decide what is reasonable? People often have to make those judgments, judgments that can influence tremendously consequential decisions-such as whether to indict someone in a legal proceeding. In this article, we take a situated cognition lens to review and integrate findings from social psychology, judgment and decision-making, communication, law, and sociology to generate a new framework for conceptualizing judgments of reasonableness and their implications for how people make decisions, particularly in the context of the legal system. We theorize that differences in structural and social contexts create information asymmetries that shape people's priors about what is and is not reasonable and how they update their priors in the face of new information. We use the legal system as a context for exploring the implications of the framework for both individual and collective decision-making and for considering the practical implications of the framework for inequities in law and social policy.

How Strong Is the Evidence for a Causal Reciprocal Effect? Contrasting Traditional and New Methods to Investigate the Reciprocal Effects Model of Self-Concept and Achievement

The relationship between students’ subject-specific academic self-concept and their academic achievement is one of the most widely researched topics in educational psychology. A large proportion of this research has considered cross-lagged panel models (CLPMs), oftentimes synonymously referred to as reciprocal effects models (REMs), as the gold standard for investigating the causal relationships between the two variables and has reported evidence of a reciprocal relationship between self-concept and achievement. However, more recent methodological research has questioned the plausibility of assumptions that need to be satisfied in order to interpret results from traditional CLPMs causally. In this substantive-methodological synergy, we aimed to contrast traditional and more recently developed methods to investigate reciprocal effects of students’ academic self-concept and achievement. Specifically, we compared results from CLPMs, full-forward CLPMs (FF-CLPMs), and random intercept CLPMs (RI-CLPMs) with two weighting approaches developed to study causal effects of continuous treatment variables. To estimate these different models, we used rich longitudinal data of N = 3757 students from lower secondary schools in Germany. Results from CLPMs, FF-CLPMs, and weighting methods supported the reciprocal effects model, particularly when math self-concept and grades were considered. Results from the RI-CLPMs were less consistent. Implications from our study for the interpretation of effects from the different models and methods as well as for school motivation theory are discussed.

Interventions to Modify Psychological Well-Being: Progress, Promises, and an Agenda for Future Research

Psychological well-being, characterized by feelings, cognitions, and strategies that are associated with positive functioning (including hedonic and eudaimonic well-being), has been linked with better physical health and greater longevity. Importantly, psychological well-being can be strengthened with interventions, providing a strategy for improving population health. But are the effects of well-being interventions meaningful, durable, and scalable enough to improve health at a population-level? To assess this possibility, a cross-disciplinary group of scholars convened to review current knowledge and develop a research agenda. Here we summarize and build on the key insights from this convening, which were: (1) existing interventions should continue to be adapted to achieve a large-enough effect to result in downstream improvements in psychological functioning and health, (2) research should determine the durability of interventions needed to drive population-level and lasting changes, (3) a shift from individual-level care and treatment to a public-health model of population-level prevention is needed and will require new infrastructure that can deliver interventions at scale, (4) interventions should be accessible and effective in racially, ethnically, and geographically diverse samples. A discussion examining the key future research questions follows.

Efficiently exploring the causal role of contextual moderators in behavioral science

Behavioral science interventions have the potential to address longstanding policy problems, but their effects are typically heterogeneous across contexts (e.g., teachers, schools, and geographic regions). This contextual heterogeneity is poorly understood, however, which reduces the field's impact and its understanding of mechanisms. Here, we present an efficient way to interrogate heterogeneity and address these gaps in knowledge. This method a) presents scenarios that vividly represent different moderating contexts, b) measures a short-term behavioral outcome (e.g., an academic choice) that is known to relate to typical intervention outcomes (e.g., academic achievement), and c) assesses the causal effect of the moderating context on the link between the psychological variable typically targeted by interventions and this short-term outcome. We illustrated the utility of this approach across four experiments (total n = 3,235) that directly tested contextual moderators of the links between growth mindset, which is the belief that ability can be developed, and students' academic choices. The present results showed that teachers' growth mindset-supportive messages and the structural opportunities they provide moderated the link between students' mindsets and their choices (studies 1 to 3). This pattern was replicated in a nationally representative sample of adolescents and did not vary across demographic subgroups (study 2), nor was this pattern the result of several possible confounds (studies 3 to 4). Discussion centers on how this method of interrogating contextual heterogeneity can be applied to other behavioral science interventions and broaden their impact in other policy domains.

Motivating Proactive Biorisk Management

Scholars and practitioners of biosafety and biosecurity (collectively, biorisk management or BRM) have argued that life scientists should play a more proactive role in monitoring their work for potential risks, mitigating harm, and seeking help as necessary. However, most efforts to promote proactive BRM have focused on training life scientists in technical skills and have largely ignored the extent to which life scientists wish to use them (ie, their motivation). In this article, we argue that efforts to promote proactive BRM would benefit from a greater focus on life scientists' motivation. We review relevant literature on life scientists' motivation to practice BRM, offer examples of successful interventions from adjacent fields, and outline ideas for possible interventions to promote proactive BRM, along with strategies for iterative development, testing, and scaling.

A Guide for Calculating Study-Level Statistical Power for Meta-Analyses

Meta-analysis is a popular approach in the psychological sciences for synthesizing data across studies. However, the credibility of meta-analysis outcomes depends on the evidential value of studies included in the body of evidence used for data synthesis. One important consideration for determining a study’s evidential value is the statistical power of the study’s design/statistical test combination for detecting hypothetical effect sizes of interest. Studies with a design/test combination that cannot reliably detect a wide range of effect sizes are more susceptible to questionable research practices and exaggerated effect sizes. Therefore, determining the statistical power for design/test combinations for studies included in meta-analyses can help researchers make decisions regarding confidence in the body of evidence. Because the one true population effect size is unknown when hypothesis testing, an alternative approach is to determine statistical power for a range of hypothetical effect sizes. This tutorial introduces the metameta R package and web app, which facilitates the straightforward calculation and visualization of study-level statistical power in meta-analyses for a range of hypothetical effect sizes. Readers will be shown how to reanalyze data using information typically presented in meta-analysis forest plots or tables and how to integrate the metameta package when reporting novel meta-analyses. A step-by-step companion screencast video tutorial is also provided to assist readers using the R package.

What would make cognitive science more useful?

What would make cognitive science more useful? In this essay, I argue that the cognitive sciences could advance theories and be more useful to society if they devoted more effort to conducting research in ways that include a more diverse set of participants and stakeholders than they have historically.

Peer-Modeled Mindsets: An Approach to Customizing Life Sciences Studying Interventions

Mindset interventions, which shift students' beliefs about classroom experiences, have shown promise for promoting diversity in science, technology, engineering, and mathematics (STEM). Psychologists have emphasized the importance of customizing these interventions to specific courses, but there is not yet a protocol for doing so. We developed a protocol for creating customized "peer-modeled" mindset interventions that elicit advice from former students in videotaped interviews. In intervention activities, clips from these interviews, in which the former students' stories model the changes in thinking about challenge and struggle that helped them succeed in a specific course, are provided to incoming life sciences students. Using this protocol, we developed a customized intervention for three sections of Introductory Biology I at a large university and tested it in a randomized controlled trial (N = 917). The intervention shifted students' attributions for struggle in the class away from a lack of potential to succeed and toward the need to develop a better approach to studying. The intervention also improved students' approaches to studying and sense of belonging and had promising effects on performance and persistence in biology. Effects were pronounced among first-generation college students and underrepresented racial/ethnic minority students, who have been historically underrepresented in the STEM fields.

Picture book reading on the development of preschoolers in rural areas of China: Effects on language, inhibition, and theory of mind

Studies have shown that book reading intervention may scaffold children's language development. However, whether book reading interventions are equally effective for children's cognitive development in a Chinese rural school setting remains to be explored. We conducted a four-month book reading intervention to address these issues in rural Chinese areas. A total of three hundred twenty-one children aged between 2.56 and 6.47 years (M = 4.66 ages, SD = 0.80) were assigned to three groups as follows: (a) control group without donated picture books; (b) active reading control group with donated picture books; and (c) intervention group with a 4-month instructed picture book reading intervention. The findings indicate that the available books could produce significant positive changes in the development of receptive language (F _(1,191) = 14.46, p < 0.01) and inhibitory control (F _(1,190) = 7.64, p = 0.01) of rural children. However, a 4-month intervention was noneffective at boosting participants' performance on these tasks (F _(1,203) = 0.07~2.73, p > 0.10). The results discussed the possible explanations, implications for behavioral intervention researchers, and suggestions for social service organizations or public institutions.

Ubiquitous bias and false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties

Studies of interaction effects are of great interest because they identify crucial interplay between predictors in explaining outcomes. Previous work has considered several potential sources of statistical bias and substantive misinterpretation in the study of interactions, but less attention has been devoted to the role of the outcome variable in such research. Here, we consider bias and false discovery associated with estimates of interaction parameters as a function of the distributional and metric properties of the outcome variable. We begin by illustrating that, for a variety of noncontinuously distributed outcomes (i.e., binary and count outcomes), attempts to use the linear model for recovery leads to catastrophic levels of bias and false discovery. Next, focusing on transformations of normally distributed variables (i.e., censoring and noninterval scaling), we show that linear models again produce spurious interaction effects. We provide explanations offering geometric and algebraic intuition as to why interactions are a challenge for these incorrectly specified models. In light of these findings, we make two specific recommendations. First, a careful consideration of the outcome's distributional properties should be a standard component of interaction studies. Second, researchers should approach research focusing on interactions with heightened levels of scrutiny. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Partner effects on caregiver and care recipient depressed mood: heterogeneity across health condition and relationship type

The well-being of caregivers and their care recipients is interrelated, although conflicting evidence has emerged across different caregiving populations. Using data from the National Health and Aging Trends Study and the National Study of Caregiving (2015 and 2017, n = 742 dyads), we constructed actor-partner interdependence models assessing how spillover (i.e., interdependence) of depressed mood varied by care recipient health condition (specifically cancer, dementia, stroke, and diabetes) and kinship type (spouse/partner, child, other relative, or non-relative). Across condition types, care recipient-to-caregiver partner effects were significantly larger in dyads with vs. without cancer and significantly smaller in dyads with vs. without diabetes (pinteractions < .05). Substantive differences in partner effects were observed by kinship type, although moderation was not statistically significant. The findings highlight potential heterogeneity in caregiver-care recipient interdependence with implications for future research and delivery of supportive care.

Computer passwords as a timely booster for writing-based psychological interventions

Writing-based psychological interventions have been widely implemented to produce adaptive change, e.g., through self-affirmation (reminding people of their most important values). To maintain the long-term effects of these interventions, we developed a form of intervention boosters-using user-customized computer passwords to convey the therapeutic messages. We examined whether computer passwords could enhance the effect of a self-affirmation intervention on the psychological well-being of sexual minority undergraduate students as they begin university. Participants were randomly assigned to either complete a self-affirmation writing exercise and create a self-affirming computer password to use for 6 weeks or complete a control writing exercise and create a control computer password. We found that frequency of password usage moderated the intervention effect, such that frequent use of self-affirming passwords buffered decreases in psychological well-being over the study period. These findings suggest that passwords can serve as a low-cost, low-burden, and timely booster for writing-based psychological interventions.

Leveraging change point detection to discover natural experiments in data

Change point detection has many practical applications, from anomaly detection in data to scene changes in robotics; however, finding changes in high dimensional data is an ongoing challenge. We describe a self-training model-agnostic framework to detect changes in arbitrarily complex data. The method consists of two steps. First, it labels data as before or after a candidate change point and trains a classifier to predict these labels. The accuracy of this classifier varies for different candidate change points. By modeling the accuracy change we can infer the true change point and fraction of data affected by the change (a proxy for detection confidence). We demonstrate how our framework can achieve low bias over a wide range of conditions and detect changes in high dimensional, noisy data more accurately than alternative methods. We use the framework to identify changes in real-world data and measure their effects using regression discontinuity designs, thereby uncovering potential natural experiments, such as the effect of pandemic lockdowns on air pollution and the effect of policy changes on performance and persistence in a learning platform. Our method opens new avenues for data-driven discovery due to its flexibility, accuracy and robustness in identifying changes in data.

Real-world effectiveness of a social-psychological intervention translated from controlled trials to classrooms

Social-psychological interventions have raised the learning and performance of students in rigorous efficacy trials. Yet, after they are distributed "in the wild" for students to self-administer, there has been little research following up on their translational effectiveness. We used cutting-edge educational technology to tailor, scale up, and track a previously-validated Strategic Resource Use intervention among 12,065 college students in 14 STEM and Economics classes. Students who self-administered this "Exam Playbook" benefitted by an average of 2.17 percentage points (i.e., a standardized effect size of 0.18), compared to non-users. This effect size was 1.65 percentage points when controlling for college entrance exam scores and 1.75 [-1.88] for adding [dropping] the Exam Playbook in stratified matching analyses. Average benefits differed in magnitude by the conduciveness of the class climate (including peer norms and incentives), gender, first-generation status, as well as how often and how early they used the intervention. These findings on how, when, and who naturally adopts these resources address a need to improve prediction, translation, and scalability of social-psychological intervention benefits.

How failure to falsify in high-volume science contributes to the replication crisis

The number of scientific papers published every year continues to increase, but scientific knowledge is not progressing at the same rate. Here we argue that a greater emphasis on falsification - the direct testing of strong hypotheses - would lead to faster progress by allowing well-specified hypotheses to be eliminated. We describe an example from neuroscience where there has been little work to directly test two prominent but incompatible hypotheses related to traumatic brain injury. Based on this example, we discuss how building strong hypotheses and then setting out to falsify them can bring greater precision to the clinical neurosciences, and argue that this approach could be beneficial to all areas of science.

Stress-related psychopathology during the COVID-19 pandemic

The COVID-19 pandemic has introduced widespread societal changes that have required ongoing adaptation. Unsurprisingly, stress-related psychopathology has increased during the pandemic, in both children and adults. We review these patterns through the lens of several leading conceptual models of the link between stress and psychopathology. Some of these models focus on characteristics of environmental stressors-including cumulative risk, specific stressor types, and stress sensitization approaches. Understanding the specific aspects of environmental stressors that are most likely to lead to psychopathology can shed light on who may be in most need of clinical intervention. Other models center on factors that can buffer against the onset of psychopathology following stress and the mechanisms through which stressors contribute to emergent psychopathology. These models highlight specific psychosocial processes that may be most usefully targeted by interventions to reduce stress-related psychopathology. We review evidence for each of these stress models in the context of other widescale community-level disruptions, like natural disasters and terrorist attacks, alongside emerging evidence for these stress pathways from the COVID-19 pandemic. We discuss clinical implications for developing interventions to reduce stress-related psychopathology during the pandemic, with a focus on brief, digital interventions that may be more accessible than traditional clinical services.

A synergistic mindsets intervention protects adolescents from stress

Social-evaluative stressors-experiences in which people feel they could be judged negatively-pose a major threat to adolescent mental health1-3 and can cause young people to disengage from stressful pursuits, resulting in missed opportunities to acquire valuable skills. Here we show that replicable benefits for the stress responses of adolescents can be achieved with a short (around 30-min), scalable 'synergistic mindsets' intervention. This intervention, which is a self-administered online training module, synergistically targets both growth mindsets4 (the idea that intelligence can be developed) and stress-can-be-enhancing mindsets5 (the idea that one's physiological stress response can fuel optimal performance). In six double-blind, randomized, controlled experiments that were conducted with secondary and post-secondary students in the United States, the synergistic mindsets intervention improved stress-related cognitions (study 1, n = 2,717; study 2, n = 755), cardiovascular reactivity (study 3, n = 160; study 4, n = 200), daily cortisol levels (study 5, n = 118 students, n = 1,213 observations), psychological well-being (studies 4 and 5), academic success (study 5) and anxiety symptoms during the 2020 COVID-19 lockdowns (study 6, n = 341). Heterogeneity analyses (studies 3, 5 and 6) and a four-cell experiment (study 4) showed that the benefits of the intervention depended on addressing both mindsets-growth and stress-synergistically. Confidence in these conclusions comes from a conservative, Bayesian machine-learning statistical method for detecting heterogeneous effects6. Thus, our research has identified a treatment for adolescent stress that could, in principle, be scaled nationally at low cost.

Building causal knowledge in behavior genetics

Behavior genetics is a controversial science. For decades, scholars have sought to understand the role of heredity in human behavior and life-course outcomes. Recently, technological advances and the rapid expansion of genomic databases have facilitated the discovery of genes associated with human phenotypes such as educational attainment and substance use disorders. To maximize the potential of this flourishing science, and to minimize potential harms, careful analysis of what it would mean for genes to be causes of human behavior is needed. In this paper, we advance a framework for identifying instances of genetic causes, interpreting those causal relationships, and applying them to advance causal knowledge more generally in the social sciences. Central to thinking about genes as causes is counterfactual reasoning, the cornerstone of causal thinking in statistics, medicine, and philosophy. We argue that within-family genetic effects represent the product of a counterfactual comparison in the same way as average treatment effects (ATEs) from randomized controlled trials (RCTs). Both ATEs from RCTs and within-family genetic effects are shallow causes: They operate within intricate causal systems (non-unitary), produce heterogeneous effects across individuals (non-uniform), and are not mechanistically informative (non-explanatory). Despite these limitations, shallow causal knowledge can be used to improve understanding of the etiology of human behavior and to explore sources of heterogeneity and fade-out in treatment effects.

An Online Experiment of NHS Information Framing on Mothers’ Vaccination Intention of Children against COVID-19

Children under the age of 5, will likely all be offered vaccination against SARS-CoV-2 soon. Parental concerns over vaccination of children are long standing and could impede the success of a vaccination campaign. In the UK, a trusted source to inform vaccination choices is the NHS website. Here we used a randomized controlled experiment of framing effects in NHS information content for COVID-19 and flu with 550 mothers under the age of 5. We compared both vaccination offers following two commonly used frames in vaccination informational campaigns: alerting to the risks of no vaccination for the child itself vs. those in their community. We find that vaccination intention was twice as high when risks to the child are emphasized, relative to risks to the community. Exploratory analyses suggest that these effects may differ between white and non-white mothers. Whilst communication directed at adult vaccination against COVID-19 generally focuses on risks of infecting others, communication about vaccination of children may benefit from emphasizing risks to the children themselves. This pattern is in line with flu vaccination research from pre-COVID-19 times.

COVID-19 vaccine hesitancy in eight European countries: Prevalence, determinants, and heterogeneity

We examine heterogeneity in COVID-19 vaccine hesitancy across eight European countries. We reveal striking differences across countries, ranging from 6.4% of adults in Spain to 61.8% in Bulgaria reporting being hesitant. We experimentally assess the effectiveness of different messages designed to reduce COVID-19 vaccine hesitancy. Receiving messages emphasizing either the medical benefits or the hedonistic benefits of vaccination significantly increases COVID-19 vaccination willingness in Germany, whereas highlighting privileges contingent on holding a vaccination certificate increases vaccination willingness in both Germany and the United Kingdom. No message has significant positive effects in any other country. Machine learning-based heterogeneity analyses reveal that treatment effects are smaller or even negative in settings marked by high conspiracy beliefs and low health literacy. In contrast, trust in government increases treatment effects in some groups. The heterogeneity in vaccine hesitancy and responses to different messages suggests that health authorities should avoid one-size-fits-all vaccination campaigns.

The Value of Patient Registries to Advance Basic and Translational Research in the Area of Traumatic Brain Injury

The number of individuals affected by traumatic brain injury (TBI) is growing globally. TBIs may cause a range of physical, cognitive, and psychiatric deficits that can negatively impact employment, academic attainment, community independence, and interpersonal relationships. Although there has been a significant decrease in the number of injury related deaths over the past several decades, there has been no corresponding reduction in injury related disability over the same time period. We propose that patient registries with large, representative samples and rich multidimensional and longitudinal data have tremendous value in advancing basic and translational research and in capturing, characterizing, and predicting individual differences in deficit profile and outcomes. Patient registries, together with recent theoretical and methodological advances in analytic approaches and neuroscience, provide powerful tools for brain injury research and for leveraging the heterogeneity that has traditionally been cited as a barrier inhibiting progress in treatment research and clinical practice. We report on our experiences, and challenges, in developing and maintaining our own patient registry. We conclude by pointing to some future opportunities for discovery that are afforded by a registry model.

Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning

BACKGROUND

The context in which a behavioral intervention is delivered is an important source of variability and systematic approaches are needed to identify and quantify contextual factors that may influence intervention efficacy. Machine learning-based phenotyping methods can contribute to a new precision health paradigm by informing personalized behavior interventions. Two primary goals of precision health, identifying population subgroups and highlighting behavioral intervention targets, can be addressed with psychosocial-behavioral phenotypes. We propose a method for psychosocial-behavioral phenotyping that models social determinants of health in addition to individual-level psychological and behavioral factors.

PURPOSE

To demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, the identification of subgroups with similar combinations of psychosocial characteristics.

METHODS

In this secondary analysis of psychosocial and behavioral data from a community cohort (n = 5,883), we optimized a multichannel mixed membership model (MC3M) using Bayesian inference to identify psychosocial-behavioral phenotypes and used logistic regression to determine which phenotypes were associated with elevated weight status (BMI ≥ 25kg/m2).

RESULTS

We identified 20 psychosocial-behavioral phenotypes. Phenotypes were conceptually consistent as well as discriminative; most participants had only one active phenotype. Two phenotypes were significantly positively associated with elevated weight status; four phenotypes were significantly negatively associated. Each phenotype suggested different contextual considerations for intervention design.

CONCLUSIONS

By depicting the complexity of psychological and social determinants of health while also providing actionable insight about similarities and differences among members of the same community, psychosocial-behavioral phenotypes can identify potential intervention targets in context.

Moral Foundational Framing and Its Impact on Attitudes and Behaviours

This preregistered, randomized controlled experiment attempts to distil the effects of Moral Foundational Framing on attitudes and behaviours towards refugees in the UK. First, moral foundations were found to robustly predict both attitudes and behaviours practised towards refugees. Next, a degree of support was found for the effectiveness of moral foundational framing in adjusting attitudes, but not behaviour, toward refugees in the UK. Individuals who scored highly on certain morals were susceptible to influence by moral foundational framing, but not always in ways that may have been expected. We conclude that the robust relationship found between moral foundations and attitudes towards refugees stresses the importance of actively shaping moral foundations. This notion is strengthened by the comparatively less robust effects of framing. Findings could be used to inform the practice of individuals interested in influencing opinion and behaviour, particularly in support of refugee acceptance.

A scalable empathic-mindset intervention reduces group disparities in school suspensions

Suspensions remove students from the learning environment at high rates throughout the United States. Policy and theory highlight social groups that face disproportionately high suspension rates-racial-minoritized students, students with a prior suspension, and students with disabilities. We used an active placebo-controlled, longitudinal field experiment (N_teachers = 66, N_students = 5822) to test a scalable "empathic-mindset" intervention, a 45- to 70-min online exercise to refocus middle school teachers on understanding and valuing the perspectives of students and on sustaining positive relationships even when students misbehave. In preregistered analyses, this exercise reduced suspension rates especially for Black and Hispanic students, cutting the racial disparity over the school year from 10.6 to 5.9 percentage points, a 45% reduction. Significant reductions were also observed for other groups of concern. Moreover, reductions persisted through the next year when students interacted with different teachers, suggesting that empathic treatment with even one teacher in a critical period can improve students' trajectories through school.

Healthy eating in the wild: An experience-sampling study of how food environments and situational factors shape out-of-home dietary success

Across many parts of the world, people increasingly eat out-of-home. Simultaneously, many people strive to eat a healthier diet, but it remains unclear to what extent and how eating out helps or hinders people in achieving their dietary goals. The present study investigated how characteristics of the physical micro-environment in out-of-home food outlets (e.g., cafeterias, supermarkets, and restaurants) influence the healthiness of food choices among a sample of German adults with a goal to eat healthier. We used an experience sampling method to obtain detailed information about people's motivation for selecting a specific food outlet and the outlet's micro-environment. We further asked for people's mood, visceral state, and thoughts during their food choice and obtained evaluations of food choices reported near their occurrence and in externally valid conditions. The data was collected via a mobile app over a period of six to eleven days between November and December of 2018 in Germany with a sample of 409 participants (n_obs = 6447). We find that even health-conscious people select food outlets and their respective micro-environments based on short-term goals, such as ease, taste, and speed of a consumption episode rather than long-term health outcomes. Using multiple regression, we show that micro-environments that promote healthy food, make such food more appealing and easier to select facilitate healthy food choices. We further identify some of the psychological mechanisms through which the micro-environment can affect food choices, as well as how individual characteristics moderate the relationship between specific micro-environmental factors and goal success. Taken together, our findings suggest the opportunity for, and arguably also necessity of, reshaping food environments to better facilitate healthier choices and support public health in the face of increasing out-of-home food consumption and the adverse consequences of unhealthy diets.

'People like me don't do well at school': The roles of identity compatibility and school context in explaining the socioeconomic attainment gap

BACKGROUND

School students who are eligible for reduced or free school meals (FSM) - an indicator of economic disadvantage - have lower academic attainment than their peers.

AIMS

We investigated whether identity compatibility - the perceived compatibility between one's social identities and the stereotype of a high-achieving student - contributes to this socioeconomic attainment gap, and whether the association between socioeconomic status and identity compatibility is moderated by school context.

SAMPLE

Our sample was 4,629 students aged 15-16 years old across 29 schools in England.

METHOD

We assessed students' perceptions of identity compatibility via self-report questionnaires 8 months prior to them taking national, standardized exams.

RESULTS

Multilevel regression analyses revealed a negative indirect effect from eligibility for FSM to exam results via identity compatibility. These effects existed even while accounting for students' gender and language status, other psychological variables known to predict academic attainment, and their previous exam results. Furthermore, school context moderated the relationship between FSM eligibility and identity compatibility. In line with the identities in context model of educational inequalities, there was a significant negative association between FSM and identity compatibility only for students attending schools in which there was previously a relatively large socioeconomic attainment gap.

CONCLUSIONS

Our results demonstrate the importance of social psychological variables in explaining educational inequalities, and of the local educational context in determining the educational experience of students from lower socioeconomic status backgrounds.

Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue

Meta-analyses that statistically synthesize evidence from multiple research studies can play an important role in advancing evidence-informed prevention science. When done in the context of a well-conducted systematic review, meta-analysis is a powerful tool for synthesizing evidence and exploring research questions that are difficult to address in individual studies, such as the association of individual study limitations on intervention effect estimates, replicability of empirical findings, and variation of effect estimates across populations and settings. Alongside the rapid growth in the number of published reviews and meta-analyses, there has been a parallel growth in the development of meta-analytic techniques to handle the increasingly complex types of questions and types of evidence relevant to prevention science. Despite this rapid evolution of meta-analytic techniques and approaches, there is still a lag between the development of new techniques and their uptake by researchers in the field. This paper serves as a brief introduction to this special issue of Prevention Science, entitled "Modern Meta-Analytic Methods in Prevention Science," which highlights recent developments in meta-analytic methods and demonstrates their application to prevention research. This special issue makes an important contribution to the field by ensuring these methodological advances are widely accessible to prevention science researchers, thereby improving their uptake and utilization, and ultimately improving the utility and rigor of research syntheses for informing evidence-based decision making in prevention.

The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains

Changing individuals’ behavior is key to tackling some of today’s most pressing societal challenges such as the COVID-19 pandemic or climate change. Choice architecture interventions aim to nudge people toward personally and socially desirable behavior through the design of choice environments. Although increasingly popular, little is known about the overall effectiveness of choice architecture interventions and the conditions under which they facilitate behavior change. Here we quantitatively review over a decade of research, showing that choice architecture interventions successfully promote behavior change across key behavioral domains, populations, and locations. Our findings offer insights into the effects of choice architecture and provide guidelines for behaviorally informed policy making.

Over the past decade, choice architecture interventions or so-called nudges have received widespread attention from both researchers and policy makers. Built on insights from the behavioral sciences, this class of behavioral interventions focuses on the design of choice environments that facilitate personally and socially desirable decisions without restricting people in their freedom of choice. Drawing on more than 200 studies reporting over 450 effect sizes (n = 2,149,683), we present a comprehensive analysis of the effectiveness of choice architecture interventions across techniques, behavioral domains, and contextual study characteristics. Our results show that choice architecture interventions overall promote behavior change with a small to medium effect size of Cohen’s d = 0.45 (95% CI [0.39, 0.52]). In addition, we find that the effectiveness of choice architecture interventions varies significantly as a function of technique and domain. Across behavioral domains, interventions that target the organization and structure of choice alternatives (decision structure) consistently outperform interventions that focus on the description of alternatives (decision information) or the reinforcement of behavioral intentions (decision assistance). Food choices are particularly responsive to choice architecture interventions, with effect sizes up to 2.5 times larger than those in other behavioral domains. Overall, choice architecture interventions affect behavior relatively independently of contextual study characteristics such as the geographical location or the target population of the intervention. Our analysis further reveals a moderate publication bias toward positive results in the literature. We end with a discussion of the implications of our findings for theory and behaviorally informed policy making.

Teacher Mindsets Help Explain Where a Growth-Mindset Intervention Does and Doesn't Work

A growth-mindset intervention teaches the belief that intellectual abilities can be developed. Where does the intervention work best? Prior research examined school-level moderators using data from the National Study of Learning Mindsets (NSLM), which delivered a short growth-mindset intervention during the first year of high school. In the present research, we used data from the NSLM to examine moderation by teachers' mindsets and answer a new question: Can students independently implement their growth mindsets in virtually any classroom culture, or must students' growth mindsets be supported by their teacher's own growth mindsets (i.e., the mindset-plus-supportive-context hypothesis)? The present analysis (9,167 student records matched with 223 math teachers) supported the latter hypothesis. This result stood up to potentially confounding teacher factors and to a conservative Bayesian analysis. Thus, sustaining growth-mindset effects may require contextual supports that allow the proffered beliefs to take root and flourish.

Editorial : Reproducibility and replicability in science: A Sisyphean task

The world received a confidence booster in the power of the scientific method, having witnessed and participated in the recent development of successful vaccines against SARS-COV-2. The world also got a peek into scientific controversies, the clamour for more transparency and data sharing, besides the requirement for rigorous testing, adequate sample sizes, false positives, false negatives, risk probabilities, and population variation. For an interested lay person, or even for a practising scientist, this was the equivalent of a crash course on the world stage on how science is done, warts and all, but where science triumphed in the end....

Social media use and well-being: What we know and what we need to know

Research into the impact of social media use (SMU) on well-being (e.g., happiness) and ill-being (e.g., depression) has exploded over the past few years. From 2019 to August 2021, 27 reviews have been published: nine meta-analyses, nine systematic reviews, and nine narrative reviews, which together included hundreds of empirical studies. The aim of this umbrella review is to synthesize the results of these meta-analyses and reviews. Even though the meta-analyses are supposed to rely on the same evidence base, they yielded disagreeing associations with well- and ill-being, especially for time spent on SM, active SMU, and passive SMU. This umbrella review explains why their results disagree, summarizes the gaps in the literature, and ends with recommendations for future research.

Evaluating the Application of the Mental Model Mapping Tool (M-Tool)

Mental models influence how individuals think and act in relation to their external environment and have been identified as leverage points to address sustainability challenges. Given the importance of mental models, a new tool to assess mental models has been developed: the Mental Model Mapping Tool (M-Tool). M-Tool was designed to have a standardized format and to be user-friendly for low literacy populations, using pictograms and audio instructions. In this paper, we evaluate M-Tool's application in two studies with Tanzanian fishers. In Study 1, we investigated M-tool's convergent validity compared to standard interviewing methods (n = 30). Study 2 investigated M-Tool's construct validity by relating mental model complexity to participants' education level (n = 185), a relationship that has been well established. The findings show that (1) mental models produced with M-Tool are more complex than mental models obtained through interviewing techniques; (2) model composition is similar across the two methods; and (3) participants with higher levels of education tend to produce more complex mental models with M-Tool than participants with lower levels of education, in line with previous research. These findings suggest that M-Tool can successfully capture mental models among diverse participants. This tool offers researchers and practitioners an instrument to map and compare perceptions of (conservation) challenges across groups.