Behavioral Science Research Research Articles

Individual accountability: holy grail or poisoned chalice?

ABSTRACT New legal regimes in various jurisdictions have increased the accountability of senior individuals in financial services to attempt to prevent wrongdoing. This article critically evaluates these efforts using insights from the behavioural sciences research on accountability, the regulatory theory on enforcement, and the criminological literature on compliance. This inquiry is pursued through an analysis of the Senior Managers and Certification Regime (SMCR) and the Banking Executive Accountability Regime (BEAR) in the UK and Australia respectively. These individual accountability regimes (IARs) are designed to make it easier to identify which senior individual is responsible for failing to address wrongdoing within their area of activity. Though often championed for ratcheting up accountability to tackle perceived impunity for wrongdoing, this article explores the limits of IARs. While acknowledging their strengths and merits, it troubles the expectations that such regimes are necessarily effective ways of priming certain anticipated behaviours and increasing cognitive load to slow decision-making processes. It considers the extent to which these regimes may be ineffective given what is known about the miserly nature of human cognition, the tendency to circumvent efforts to encourage ‘right’ decisions, and the inability of individual accountability regimes to address broader structural issues in financial markets.

Robert (Bob) Anthony Altemeyer (1940-2024).

Robert (Bob) Anthony Altemeyer (June 6, 1940-February 7, 2024) was an influential social psychologist who also shaped personality and political psychology. Born in St. Louis, Missouri, Bob was educated at Yale (undergraduate) and Carnegie-Mellon (graduate). Following a 2-year stint at Doane College in Nebraska, he established his career at the University of Manitoba (Winnipeg, Canada) from 1968 to 2008. In 1986, Bob was awarded the prestigious American Association for the Advancement of Science Prize for Behavioral Science Research, largely for his development of the right-wing authoritarianism (RWA) construct. Whereas the authoritarian personality construct developed by Adorno and colleagues in the 1950s was rooted in psychodynamics, Bob drew on social learning principles and conceptualized authoritarianism as both an attitudinal orientation and trait. Not all of Bob's ideas held true, such as the social learning underpinnings that he proposed for RWA. But true to his inquisitive nature, he wrote that he would be excited to learn of RWA's strong heritable components if uncovered (as they later were). An anticonformist to the core, Bob published almost exclusively in books despite the field prioritizing journal articles, typically as sole author and working with few collaborators, and writing about complex ideas with rather informal and unconventional language. His legacy is proof that fighting the odds and remaining authentic, rather than chasing conventions and norms, can leave an indelible mark on science. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Abstract B121: Enhancing opportunities for underrepresented minorities conducting cancer and cancer health disparities research nationwide: The Transformative Educational Advancement and Mentoring (TEAM) network

Abstract Background: The Transformative Educational Advancement and Mentoring (TEAM) network is a program funded by the Center to Reduce Cancer Health Disparities (CRCHD) of the National Cancer Institute (NCI). In support of the NCI’s goal to reduce cancer health disparities, the CRCHD drives workforce diversity efforts by supporting trainees from backgrounds underrepresented (UR) in cancer biomedical and behavioral sciences research. The two TEAM programs launched at minority-serving institutions (MSIs) in 2023: The Southwest TEAM (STEAM) network and San Diego State University’s (SDSU) Advancing Cancer Careers for ExceLlence (ACCEL). Each program has a unique model designed to best serve trainees in their respective regions, but both provide: 1) Diverse Training Champions (TCs), role models and navigators who act as the primary point of contact for scholars, provide direct services to trainees, ascertain member needs, develop programmatic offerings, and connect trainees to program components; 2) a mentorship program for pre and postdoctoral scholars and early stage investigators (ESIs); 3) targeted recruitment and professional development of UR scholars; and 4) tailored linkages to professional development and employment opportunities, and NCI/CRCHD funding priorities. This work summarizes the strategies planned across both programs to promote recruitment and retention of investigators from UR backgrounds and cultivate professional development opportunities for students and ESIs. Approach: The STEAM network includes the University of New Mexico, New Mexico State University, Northern Arizona University, and Burrell College of Osteopathic Medicine and leverages the unique programs and expertise of these MSIs to collectively train diverse cancer health disparities researchers with interests in serving Native American, Hispanic and rural communities in the Southwestern United States. The ACCEL Program supports excellence, inclusion and transformation of the cancer research science force, expanding and strengthening the pipeline of UR scholars pursuing cancer research. SDSU, a Carnegie Foundation high activity research institution is one of few institutions to be both a Title Ill/Title V eligible institution for diversity. ACCEL works in close collaboration with the University of California San Diego Moores Cancer Center. We will discuss opportunities and approaches employed by each program in support of scholars to increase the number of diverse scholars in these research pipelines and demonstrate the effectiveness of such coordinated efforts. Conclusion: Supporting TCs to focus on scholars within regional programs will prove to be an efficient and effective way to increase recruitment and retention of diverse investigators in NCI research and community programs. Using novel strategies like TCs, the programs will support the attainment of career goals and enhance and diversify workforce capacity across the cancer research and care continuum. Citation Format: Dolores D. Guest, Martha M. Grimes, Andrew L. Sussman, Elizabeth Y. Jimenez, Graciela Unguez, Ahmed Mohammed, Naomi Lee, Jani Ingram, Krystal Charley, Kristin Gosselink, Jill Nery, Avesta Basraki, Natalie Gude, Rebecca Lewison. Enhancing opportunities for underrepresented minorities conducting cancer and cancer health disparities research nationwide: The Transformative Educational Advancement and Mentoring (TEAM) network [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B121.

The limits of doing global, cross-cultural behavioral science research

The limits of doing global, cross-cultural behavioral science research

Tactics of just, equitable, diverse, and inclusive scientific research.

The principles of social justice, equity, diversity, inclusion (JEDI) have received increasing attention in behavior analysis circles, but the conversation has largely centered on implications for applied behavior analysis practice and research. It may be less clear to researchers who conduct basic and translational research how JEDI principles can inform and inspire their work. This article synthesizes publications from behavior analysis and other scientific fields about tactics of JEDI-informed research. We organized this scholarship across five stages of research from developing the research question to sharing findings and curated sources for an audience of behavioral science researchers. We discuss reflexive practice, representation, belongingness, participatory research, quantitative critical theory, and open science, among other topics. Some researchers may have already adopted some of the practices outlined, some may begin new practices, and some may choose to conduct experimental analyses of JEDI problems. Our hope is that those actions will be reinforced by the behavior analysis scientific community. We conclude by encouraging the leadership of this journal to continue to work toward the structural changes necessary to make the experimental analysis of behavior just, equitable, diverse, and inclusive.

Micro-Randomized Trials in Information Systems Research: An Experimental Method for Advancing Knowledge About Our Dynamic and Digitalized World

The increasing ubiquity of individualized digital devices expands the scope of Information Systems (IS) research and offers novel opportunities for investigating and influencing individuals’ intentions, cognitions, and behavior. Based on these technological advancements behavioral science researchers have started to develop experimental methods that allow for rigorously investigating dynamic phenomena in our digitalized world. We introduce an experimental design, called a Micro-Randomized-Trial (MRT), and propose that it is widely applicable in IS research for examining complex and dynamic IS research phenomena. MRTs allow for analyzing causalities and testing theories with dynamic components by considering how time-varying personal and contextual factors influence an experimental treatment’s efficacy over time. The resulting insights can be leveraged to refine theories, improve IS design, influence users’ cognitions and intentions, and steer user behavior. This paper motivates the relevance of the MRT experimental design for IS research, equips interested IS researchers with the required knowledge to conduct MRTs, and demonstrates how MRTs can be deployed to advance IS knowledge by achieving research goals that until now, given the standard IS method assortment, could not be properly addressed.

Mortality due to falls by county, age group, race, and ethnicity in the USA, 2000–19: a systematic analysis of health disparities

Fall-related mortality has increased rapidly over the past two decades in the USA, but the extent to which mortality varies across racial and ethnic populations, counties, and age groups is not well understood. The aim of this study was to estimate age-standardised mortality rates due to falls by racial and ethnic population, county, and age group over a 20-year period. Redistribution methods for insufficient cause of death codes and validated small-area estimation methods were applied to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual fall-related mortality. Estimates from 2000 to 2019 were stratified by county (n=3110) and five mutually exclusive racial and ethnic populations: American Indian or Alaska Native (AIAN), Asian or Pacific Islander (Asian), Black, Latino or Hispanic (Latino), and White. Estimates were corrected for misreporting of race and ethnicity on death certificates using published misclassification ratios. We masked (ie, did not display) estimates for county and racial and ethnic population combinations with a mean annual population of less than 1000. Age-standardised mortality is presented for all ages combined and for age groups 20-64 years (younger adults) and 65 years and older (older adults). Nationally, in 2019, the overall age-standardised fall-related mortality rate for the total population was 13·4 deaths per 100 000 population (95% uncertainty interval 13·3-13·6), an increase of 65·3% (61·9-68·8) from 8·1 deaths per 100 000 (8·0-8·3) in 2000, with the largest increases observed in older adults. Fall-related mortality at the national level was highest across all years in the AIAN population (in 2019, 15·9 deaths per 100 000 population [95% uncertainty interval 14·0-18·2]) and White population (14·8 deaths per 100 000 [14·6-15·0]), and was about half as high among the Latino (8·7 deaths per 100 000 [8·3-9·0]), Black (8·1 deaths per 100 000 [7·9-8·4]), and Asian (7·5 deaths per 100 000 [7·1-7·9]) populations. The disparities between racial and ethnic populations varied widely by age group, with mortality among younger adults highest for the AIAN population and mortality among older adults highest for the White population. The national-level patterns were observed broadly at the county level, although there was considerable spatial variation across ages and racial and ethnic populations. For younger adults, among almost all counties with unmasked estimates, there was higher mortality in the AIAN population than in all other racial and ethnic populations, while there were pockets of high mortality in the Latino population, particularly in the Mountain West region. For older adults, mortality was particularly high in the White population within clusters of counties across states including Florida, Minnesota, and Wisconsin. Age-standardised mortality due to falls increased over the study period for each racial and ethnic population and almost every county. Wide variation in mortality across geography, age, and race and ethnicity highlights areas and populations that might benefit most from efficacious fall prevention interventions as well as additional prevention research. US National Institutes of Health (Intramural Research Program, National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; Intramural Research Program, National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Sciences Research).

Measurement and Uncertainty Preserving Parametric Modeling for Continuous Latent Variables With Discrete Indicators and External Variables

Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has received attention in models for continuous observable variables but to date has not been examined in the context of discrete variables. This work demonstrates that interpretational confounding can occur in models for discrete variables, and develops a multistage Bayesian estimation approach to deal with this problem. The key features of this approach are that it is (a) measurement preserving, in that it precludes the possibility of interpretational confounding, and (b) uncertainty preserving, in that the uncertainty from the earlier stage of estimating the measurement model is propagated to the second stage of estimating the relations between the latent variable(s) and any covariates or outcomes. Previous work on these methods had only considered models for continuous observed variables, and software was limited to models with a single latent variable and either covariates or outcomes. This work extends the approach and software to a more general class of solutions, including discrete variables, illustrating the procedures with analyses of real data. Functions for conducting the analyses in widely available software are provided.

Representation of Social Determinants of Health terminology in medical subject headings: impact of added terms.

To enhance and evaluate the quality of PubMed search results for Social Determinants of Health (SDoH) through the addition of new SDoH terms to Medical Subject Headings (MeSH). High priority SDoH terms and definitions were collated from authoritative sources, curated based on publication frequencies, and refined by subject matter experts. Descriptive analyses were used to investigate how PubMed search details and best match results were affected by the addition of SDoH concepts to MeSH. Three information retrieval metrics (Precision, Recall, and F measure) were used to quantitatively assess the accuracy of PubMed search results. Pre- and post-update documents were clustered into topic areas using a Natural Language Processing pipeline, and SDoH relevancy assessed. Addition of 35 SDoH terms to MeSH resulted in more accurate algorithmic translations of search terms and more reliable best match results. The Precision, Recall, and F measures of post-update results were significantly higher than those of pre-update results. The percentage of retrieved publications belonging to SDoH clusters was significantly greater in the post- than pre-update searches. This evaluation confirms that inclusion of new SDoH terms in MeSH can lead to qualitative and quantitative enhancements in PubMed search retrievals. It demonstrates the methodology for and impact of suggesting new terms for MeSH indexing. It provides a foundation for future efforts across behavioral and social science research (BSSR) domains. Improving the representation of BSSR terminology in MeSH can improve PubMed search results, thereby enhancing the ability of investigators and clinicians to build and utilize a cumulative BSSR knowledge base.

On Shared Leadership Modeling: Contrasting Network and Dyadic Approaches

Shared leadership is a dynamic phenomenon that has gained attention in behavioral science and management research over the last two decades. Network modeling is frequently employed to study this phenomenon, with the recent literature favoring a node-based approach over the traditional dyad-based approach. In this study, we investigate the differential impact of these approaches on shared leadership dynamics in student teams, specifically examining their effects on team task cohesion, team social cohesion, and team performance. We utilized multilevel structural equation modeling to compare node-based and dyad-based approaches in modeling shared leadership networks. Our findings indicate that increased leadership interactions positively influenced team performance and cohesion across both approaches. The dyad-based approach demonstrated a greater effect of leadership interactions on team performance, while leadership centrality significantly impacted performance exclusively in the node-based approach. This research contributes to the field by elucidating the differential impacts of node-based and dyad-based approaches, highlighting their strengths in capturing shared leadership dynamics and centrality effects. Our results underscore the critical importance of aligning theoretical foundations and research objectives with methodological choices in shared leadership studies. These insights enhance our understanding of shared leadership measurement and its implications for team outcomes, offering valuable guidance for future empirical investigations in this domain.

Obesity- and diet-induced plasticity in systems that control eating and energy balance.

In April 2023, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), in partnership with the National Institute of Child Health and Human Development, the National Institute on Aging, and the Office of Behavioral and Social Sciences Research, hosted a 2-day online workshop to discuss neural plasticity in energy homeostasis and obesity. The goal was to provide a broad view of current knowledge while identifying research questions and challenges regarding neural systems that control food intake and energy balance. This review includes highlights from the meeting and is intended both to introduce unfamiliar audiences with concepts central to energy homeostasis, feeding, and obesity and to highlight up-and-coming research in these areas that may be of special interest to those with a background in these fields. The overarching theme of this review addresses plasticity within the central and peripheral nervous systems that regulates and influences eating, emphasizing distinctions between healthy and disease states. This is by no means a comprehensive review because this is a broad and rapidly developing area. However, we have pointed out relevant reviews and primary articles throughout, as well as gaps in current understanding and opportunities for developments in the field.

The burden of cirrhosis mortality by county, race, and ethnicity in the USA, 2000–19: a systematic analysis of health disparities

Cirrhosis is responsible for substantial health and economic burden in the USA. Reducing this burden requires better understanding of how rates of cirrhosis mortality vary by race and ethnicity and by geographical location. This study describes rates and trends in cirrhosis mortality for five racial and ethnic populations in 3110 US counties from 2000 to 2019. We estimated cirrhosis mortality rates by county, race and ethnicity, and year (2000-19) using previously validated small-area estimation methods, death registration data from the US National Vital Statistics System, and population data from the US National Center for Health Statistics. Five racial and ethnic populations were considered: American Indian or Alaska Native (AIAN), Asian or Pacific Islander (Asian), Black, Latino or Hispanic (Latino), and White. Cirrhosis mortality rate estimates were age-standardised using the age distribution from the 2010 US census as the standard. For each racial and ethnic population, estimates are presented for all counties with a mean annual population greater than 1000. From 2000 to 2019, national-level age-standardised cirrhosis mortality rates decreased in the Asian (23·8% [95% uncertainty interval 19·6-27·8], from 9·4 deaths per 100 000 population [8·9-9·9] to 7·1 per 100 000 [6·8-7·5]), Black (22·8% [20·6-24·8], from 19·8 per 100 000 [19·4-20·3] to 15·3 per 100 000 [15·0-15·6]), and Latino (15·3% [13·3-17·3], from 26·3 per 100 000 [25·6-27·0] to 22·3 per 100 000 [21·8-22·8]) populations and increased in the AIAN (39·3% [32·3-46·4], from 45·6 per 100 000 [40·6-50·6] to 63·5 per 100 000 [57·2-70·2] in 2000 and 2019, respectively) and White (25·8% [24·2-27·3], from 14·7 deaths per 100 000 [14·6-14·9] to 18·5 per 100 000 [18·4-18·7]) populations. In all years, cirrhosis mortality rates were lowest among the Asian population, highest among the AIAN population, and higher in males than females for each racial and ethnic population. The degree of heterogeneity in county-level cirrhosis mortality rates varied by racial and ethnic population, with the narrowest IQR in the Asian population (median 8·0 deaths per 100 000, IQR 6·4-10·4) and the widest in the AIAN population (55·1, 30·3-78·8). Cirrhosis mortality increased over the study period in almost all counties for the White (2957 [96·9%] of 3051 counties) and AIAN (421 [88·8%] of 474) populations, but in a smaller proportion of counties for the Asian, Black, and Latino populations. For all racial and ethnic populations, cirrhosis mortality rates increased in more counties between 2000 and 2015 than between 2015 and 2019. Cirrhosis mortality increased nationally and in many counties from 2000 to 2019. Although the magnitude of racial and ethnic disparities decreased in some places, disparities nonetheless persisted, and mortality remained high in many locations and communities. Our findings underscore the need to implement targeted and locally tailored programmes and policies to reduce the burden of cirrhosis at both the national and local level. US National Institutes of Health (Intramural Research Program, National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; Intramural Research Program, National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Sciences Research).

Participants' Utilitarian Choice Is Influenced by Gamble Presentation and Age.

No prior behavioral science research has delved into the impact of gamble presentation (horizontal or vertical) on individuals' utilitarian behavior, despite evidence suggesting that such choices can be influenced by comparing attributes like probability and money in gambles. This article addresses this gap by exploring the influence of gamble presentation on utilitarian behavior. A two-factor independent measures design was employed to explore the influence of the type of gamble presentation and age on participants' utilitarian decision-making preferences. The findings showed a reduced likelihood of participants choosing the non-utilitarian gamble with vertically presented gambles compared to horizontal ones. Consequently, participants' utilitarian behavior was influenced by between-gamble comparisons of available attributes, with utilitarian choices (e.g., choosing Gamble A) being more prevalent in vertical presentations due to a straightforward comparison on the probability attribute. Furthermore, the results also revealed that older participants take more time than their younger counterparts when making utilitarian errors. We attribute this to their abundant knowledge and experience. Future research should explore the comparative psychological processing used by participants in risky decision-making tasks.

Enhancing Model Fit Evaluation in SEM: Practical Tips for Optimizing Chi-Square Tests

This paper aims to advocate for a balanced approach to model fit evaluation in structural equation modeling (SEM). The ongoing debate surrounding chi-square test statistics and fit indices has been characterized by ambiguity and controversy. Despite the acknowledged limitations of relying solely on the chi-square test, its careful application can enhance its effectiveness in evaluating model fit and specification. To illustrate this point, we present three common scenarios relevant to social and behavioral science research using Monte Carlo simulations, where fit indices may inadequately address concerns regarding goodness-of-fit, while the chi-square statistic can offer valuable insights. Our recommendation is to report both the chi-square test and fit indices, prioritizing precise model specification to ensure the reliability of model fit indicators.

Misunderstanding the harms of online misinformation.

The controversy over online misinformation and social media has opened a gap between public discourse and scientific research. Public intellectuals and journalists frequently make sweeping claims about the effects of exposure to false content online that are inconsistent with much of the current empirical evidence. Here we identify three common misperceptions: that average exposure to problematic content is high, that algorithms are largely responsible for this exposure and that social media is a primary cause of broader social problems such as polarization. In our review of behavioural science research on online misinformation, we document a pattern of low exposure to false and inflammatory content that is concentrated among a narrow fringe with strong motivations to seek out such information. In response, we recommend holding platforms accountable for facilitating exposure to false and extreme content in the tails of the distribution, where consumption is highest and the risk of real-world harm is greatest. We also call for increased platform transparency, including collaborations with outside researchers, to better evaluate the effects of online misinformation and the most effective responses to it. Taking these steps is especially important outside the USA and Western Europe, where research and data are scant and harms may be more severe.

Computational and analytical analysis of integral-differential equations for modeling avoidance learning behavior

This work emphasizes the computational and analytical analysis of integral-differential equations, with a particular application in modeling avoidance learning processes. Firstly, we suggest an approach to determine a unique solution to the given model by employing methods from functional analysis and fixed-point theory. We obtain numerical solutions using the approach of Picard iteration and evaluate their stability in the context of minor perturbations. In addition, we explore the practical application of these techniques by providing two examples that highlight the thorough analysis of behavioral responses using numerical approximations. In the end, we examine the efficacy of our suggested ordinary differential equations (ODEs) for studying the avoidance learning behavior of animals. Furthermore, we investigate the convergence and error analysis of the proposed ODEs using multiple numerical techniques. This integration of theoretical and practical analysis enhances the domain of applied mathematics by providing important insights for behavioral science research.

Comparing researchers' degree of dichotomous thinking using frequentist versus Bayesian null hypothesis testing.

A large amount of scientific literature in social and behavioural sciences bases their conclusions on one or more hypothesis tests. As such, it is important to obtain more knowledge about how researchers in social and behavioural sciences interpret quantities that result from hypothesis test metrics, such as p-values and Bayes factors. In the present study, we explored the relationship between obtained statistical evidence and the degree of belief or confidence that there is a positive effect in the population of interest. In particular, we were interested in the existence of a so-called cliff effect: A qualitative drop in the degree of belief that there is a positive effect around certain threshold values of statistical evidence (e.g., at p = 0.05). We compared this relationship for p-values to the relationship for corresponding degrees of evidence quantified through Bayes factors, and we examined whether this relationship was affected by two different modes of presentation (in one mode the functional form of the relationship across values was implicit to the participant, whereas in the other mode it was explicit). We found evidence for a higher proportion of cliff effects in p-value conditions than in BF conditions (N = 139), but we did not get a clear indication whether presentation mode had an effect on the proportion of cliff effects. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 2 June 2023. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/5CW6P .

A Framework for Enhancing Behavioral Science Research with Human-Guided Language Models

Many behavioral science studies result in large amounts of unstructured data sets that are costly to code and analyze, requiring multiple reviewers to agree on systematically chosen concepts and themes to categorize responses. Large language models (LLMs) have potential to support this work, demonstrating capabilities for categorizing, summarizing, and otherwise organizing unstructured data. In this paper, we consider that although LLMs have the potential to save time and resources performing coding on qualitative data, the implications for behavioral science research are not yet well understood. Model bias and inaccuracies, reliability, and lack of domain knowledge all necessitate continued human guidance. New methods and interfaces must be developed to enable behavioral science researchers to efficiently and systematically categorize unstructured data together with LLMs. We propose a framework for incorporating human feedback into an annotation workflow, leveraging interactive machine learning to provide oversight while improving a language model's predictions over time.

Relevance of Behavioral Research in the Evolving State of Anti-obesity Medications: Is the Glass Half Empty or Half Full?

Obesity continues to be a significant public health challenge. While weight loss medications have been studied and available for several years, the newest generation of highly effective anti-obesity medications (AOMs) will shift how behavioral science professionals approach obesity treatment and research. With the unique skill set of behavioral science professionals, this commentary suggests ways to integrate behavioral science into the rapidly evolving landscape of AOM use to accelerate better obesity care and generate new lines of research. The goal of this commentary is to stimulate discussion and encourage responsive and relevant action to improve population health.

The critical factors in producing high quality and policy-relevant research: insights from international behavioural science units

Background: There has been a rapid increase in the number of, and demand for, organisations offering behavioural science advice to government over the last ten years. Yet we know little of the state of science and the experiences of these evidence providers. Aims and objectives: To identify current practice in this emerging field and the factors that impact on the production of high-quality and policy-relevant research. Methods: A qualitative study using one-to-one interviews with representatives from a purposeful sample of 15 units in the vanguard of international behavioural science research in policy. The data were analysed thematically. Findings: Relationships with policymakers were important in the inception of units, research conduct, implementation and dissemination of findings. Knowledge exchange facilitated a shared understanding of policy issues/context, and of behavioural science. Sufficient funding was crucial to maintain critical capacity in the units’ workforces, build a research portfolio beneficial to policymakers and the units, and to ensure full and transparent dissemination. Discussion and conclusion: Findings highlight the positive impact of strong evidence-provider/user relationships and the importance of governments’ commitment to co-produced research programmes to address policy problems and transparency in the dissemination of methods and findings. From the findings we have created a framework, ‘STEPS’ (Sharing, Transparency, Engagement, Partnership, Strong relationships), of five recommendations for units working with policymakers. These findings will be of value to all researchers conducting research on behalf of government.