In the past decade, behavioural science has gained influence in policymaking but suffered a crisis of confidence in the replicability of its findings. Here, we describe a nascent heterogeneity revolution that we believe these twin historical trends have triggered. This revolution will be defined by the recognition that most treatment effects are heterogeneous, so the variation in effect estimates across studies that defines the replication crisis is to be expected as long as heterogeneous effects are studied without a systematic approach to sampling and moderation. When studied systematically, heterogeneity can be leveraged to build more complete theories of causal mechanism that could inform nuanced and dependable guidance to policymakers. We recommend investment in shared research infrastructure to make it feasible to study behavioural interventions in heterogeneous and generalizable samples, and suggest low-cost steps researchers can take immediately to avoid being misled by heterogeneity and begin to learn from it instead. Behavioural science increasingly informs policy, but findings are not always replicated. Bryan et al. describe an emerging heterogeneity revolution. They recommend that researchers use heterogeneity in treatment effects to develop more robust theories of causality and strengthen the field.

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

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world.

/pdf/a-multi-country-test-of-brief-reappraisal-interventions-on-4k6n28khru.pdf

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

Although comprehensive reporting guidelines for implementation strategy use within implementation research exist, they are rarely used by clinical (i.e., efficacy and effectiveness) researchers. In this debate, we argue that the lack of comprehensive reporting of implementation strategy use and alignment of those strategies with implementation outcomes within clinical research is a missed opportunity to efficiently narrow research-to-practice gaps. We review ways that comprehensively specifying implementation strategy use can advance science, including enhancing replicability of clinical trials and reducing the time from clinical research to public health impact. We then propose that revisions to frequently used reporting guidelines in clinical research (e.g., CONSORT, TIDieR) are needed, review current methods for reporting implementation strategy use (e.g., utilizing StaRI), provide pragmatic suggestions on how to both prospectively and retrospectively specify implementation strategy use and align these strategies with implementation outcomes within clinical research, and offer a case study of using these methods. The approaches recommended in this article will not only contribute to shared knowledge and language among clinical and implementation researchers but also facilitate the replication of efficacy and effectiveness research. Ultimately, we hope to accelerate translation from clinical to implementation research in order to expedite improvements in public health.

/pdf/integrating-implementation-science-in-clinical-research-to-2e9llx21k9.pdf

Integrating implementation science in clinical research to maximize public health impact: a call for the reporting and alignment of implementation strategy use with implementation outcomes in clinical research

Subjective perceptions of inequality can substantially influence policy attitudes, public health metrics, and societal well-being, but the lack of consensus in the scientific community on how to best operationalize and measure these perceptions may impede progress on the topic. Here, we provide a theoretical framework for the study of subjective perceptions of inequality, which brings critical differences to light. This framework—which we conceptualize as a series of four guiding questions for studying subjective perceptions of economic inequality—serves as a blueprint for the theoretical and empirical decisions researchers need to address in the study of when, how, and why subjective perceptions of inequality are consequential for individuals, groups, and societies. To lay the foundation for a comprehensive approach to the topic, we offer four theoretical and empirical decisions in studying subjective perceptions of inequality, urging researchers to specify: (1) What kind of inequality? (2) What level of analysis? (3) What part of the distribution? and (4) What comparison group? We subsequently discuss how this framework can be used to organize existing research and highlight its utility in guiding future research across the social sciences in both the theory and measurement of subjective perceptions of inequality. 

/pdf/inequality-in-researchers-minds-four-guiding-questions-for-3f4ysprq.pdf

Inequality in researchers’ minds: Four guiding questions for studying subjective perceptions of economic inequality

To help move researchers away from heuristically dismissing “small” effects as unimportant, recent articles have revisited arguments to defend why seemingly small effect sizes in psychological science matter. One argument is based on the idea that an observed effect size may increase in impact when generalized to a new context because of processes of accumulation over time or application to large populations. However, the field is now in danger of heuristically accepting all effects as potentially important. We aim to encourage researchers to think thoroughly about the various mechanisms that may both amplify and counteract the importance of an observed effect size. Researchers should draw on the multiple amplifying and counteracting mechanisms that are likely to simultaneously apply to the effect when that effect is being generalized to a new and likely more dynamic context. In this way, researchers should aim to transparently provide verifiable lines of reasoning to justify their claims about an effect’s importance or unimportance. This transparency can help move psychological science toward a more rigorous assessment of when psychological findings matter for the contexts that researchers want to generalize to.

/pdf/not-all-effects-are-indispensable-psychological-science-tfk26rph.pdf

Not All Effects Are Indispensable: Psychological Science Requires Verifiable Lines of Reasoning for Whether an Effect Matters

Psychologists are spending a considerable amount of time researching and developing interventions in hopes that their efforts can help to tackle some of society’s pressing problems. Unfortunately, ...

/pdf/do-we-report-the-information-that-is-necessary-to-give-22ki2ivtw0.pdf

Bharathy Premachandra

Papers

Do We Report the Information That Is Necessary to Give Psychology Away? A Scoping Review of the Psychological Intervention Literature 2000-2018.