Re-use of research data in the social sciences. Use and users of digital data archive.

The aim of this paper is to investigate the re-use of research data deposited in digital data archive in the social sciences. The study examines the quantity, type, and purpose of data downloads by analyzing enriched user log data collected from Swiss data archive. The findings show that quantitative datasets are downloaded increasingly from the digital archive and that downloads focus heavily on a small share of the datasets. The most frequently downloaded datasets are survey datasets collected by research organizations offering possibilities for longitudinal studies. Users typically download only one dataset, but a group of heavy downloaders form a remarkable share of all downloads. The main user group downloading data from the archive are students who use the data in their studies. Furthermore, datasets downloaded for research purposes often, but not always, serve to be used in scholarly publications. Enriched log data from data archives offer an interesting macro level perspective on the use and users of the services and help understanding the increasing role of repositories in the social sciences. The study provides insights into the potential of collecting and using log data for studying and evaluating data archive use.

Can Generative AI improve social science?

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative. I examine how bias in the data used to train these tools can negatively impact social science research-as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.

Exposing omitted moderators: Explaining why effect sizes differ in the social sciences.

Policymakers increasingly rely on behavioral science in response to global challenges, such as climate change or global health crises. But applications of behavioral science face an important problem: Interventions often exert substantially different effects across contexts and individuals. We examine this heterogeneity for different paradigms that underlie many behavioral interventions. We study the paradigms in a series of five preregistered studies across one in-person and 10 online panels, with over 11,000 respondents in total. We find substantial heterogeneity across settings and paradigms, apply techniques for modeling the heterogeneity, and introduce a framework that measures typically omitted moderators. The framework's factors (Fluid Intelligence, Attentiveness, Crystallized Intelligence, and Experience) affect the effectiveness of many text-based interventions, producing different observed effect sizes and explaining variations across samples. Moderators are associated with effect sizes through two paths, with the intensity of the manipulation and with the effect of the manipulation directly. Our results motivate observing these moderators and provide a theoretical and empirical framework for understanding and predicting varying effect sizes in the social sciences.

The long-term expansion and recession of human populations.

Over the last 12,000 y, human populations have expanded and transformed critical earth systems. Yet, a key unresolved question in the environmental and social sciences remains: Why did human populations grow and, sometimes, decline in the first place? Our research builds on 20 y of archaeological research studying the deep time dynamics of human populations to propose an explanation for the long-term growth and stability of human populations. Innovations in the productive capacity of populations fuels exponential-like growth over thousands of years; however, innovations saturate over time and, often, may leave populations vulnerable to large recessions in their well-being and population density. Empirically, we find a trade-off between changes in land use that increase the production and consumption of carbohydrates, driving repeated waves of population growth over thousands of years, and the susceptibility of populations to large recessions due to a lag in the impact of humans on resources. These results shed light on the long-term drivers of human population growth and decline.

THE 3P MODEL AND ORAL HEALTH IMPACT: DEMONSTRATING THE IMPORTANCE OF BEHAVIORAL AND SOCIAL SCIENCES IN THE FUTURE OF EVIDENCE-BASED DENTISTRY.

Oral health impact or oral health related quality of life is an increasingly important and well-known metric in dental care and research. There have been recent calls for greater integration of the behavioral and social sciences into oral health research and practice, including the need for frameworks and theories to guide this work. One such framework for understanding the role of predisposing, precipitating, and perpetuating biopsychosocial mechanisms in health and disease is the "3P" model. Here, the 3P model is described and applied to case examples to help understand the development and maintenance of oral health impact. Additionally, this paper outlines how this conceptualization using the 3P model and oral health impact makes way for greater integration of behavioral interventions to prevent, mitigate, or treat the negative impact that oral, craniofacial, or dental disease may have on individuals. Doing this allows for a broadening of what evidence-based dentistry means for the future and provides a roadmap going forward.

Co-production of health and social science research with vulnerable children and young people: A rapid review.

BACKGROUND: The term 'care-experienced' refers to anyone who is currently in care or has been in care at any stage in their life. A complex interplay of factors leads to care-experienced children and young people (CECYP) experiencing poorer oral health and access to dental care than their peers. A rapid review of the co-production of health and social care research with vulnerable children and young people (CYP) was carried out to inform the development of a co-produced research project exploring the oral health behaviours and access to dental services of CECYP. Here, 'co-production' refers to the involvement of CYP in the planning or conduct of research with explicit roles in which they generate ideas, evidence and research outputs. AIM: To learn how to meaningfully involve vulnerable CYP in the co-production of health and social science research. OBJECTIVES: To identify: Different approaches to facilitating the engagement of vulnerable CYP in co-production of health and social science research; different activities carried out in such approaches, challenges to engaging vulnerable CYP in co-production of health and social science research and ways to overcome them and areas of best practice in relation to research co-production with vulnerable CYP. SEARCH STRATEGY: A rapid review of peer-reviewed articles was conducted in six databases (MEDLINE, Embase, SocINDEX, CINAHL, PsycINFO and Web of Science) and grey literature to identify studies that engaged vulnerable CYP in co-approaches to health and social research. MAIN RESULTS: Of 1394 documents identified in the search, 40 were included and analysed. A number of different approaches to co-production were used in the studies. The CYP was involved in a range of activities, chiefly the development of data collection tools, data collection and dissemination. Individual challenges for CYP and researchers, practical and institutional factors and ethical considerations impacted the success of co-production. DISCUSSION AND CONCLUSION: Co-production of health and social science with vulnerable CYP presents challenges to researchers and CYP calling for all to demonstrate reflexivity and awareness of biases, strengths and limitations. Used appropriately and well, co-production offers benefits to researchers and CYP and can contribute to research that reflects the needs of vulnerable CYP. Adherence to the key principles of inclusion, safeguarding, respect and well-being facilitates this approach. PATIENT AND PUBLIC CONTRIBUTION: Members of our patient and public involvement and stakeholder groups contributed to the interpretation of the review findings. This manuscript was written together with a young care leaver, Skye Boswell, who is one of the authors. She contributed to the preparation of the manuscript, reviewing the findings and their interpretation.

The social sciences needs more than integrative experimental designs: We need better theories.

Almaatouq et al.'s prescription for more integrative experimental designs is welcome but does not address an equally important problem: Lack of adequate theories. We highlight two features theories ought to satisfy: "Well-specified" and "grounded." We discuss the importance of these features, some positive exemplars, and the complementarity between the target article's prescriptions and improved theorizing.

Dimensional versus conceptual incommensurability in the social and behavioral sciences.

This commentary analyzes the extent to which the incommensurability problem can be resolved through the proposed alternative method of integrative experiment design. We suggest that, although one aspect of incommensurability is successfully addressed (dimensional incommensurability), the proposed design space method does not yet alleviate another major source of discontinuity, which we call conceptual incommensurability.

The miss of the framework.

The authors rightly critique existing social sciences approaches. However, they are too quick to dismiss the criticism that their proposed paradigm is atheoretical. Social and cognitive theories are indeed incommensurate, often due to the lack of a unifying framework. Without proper integration with theoretical frameworks, their proposal may merely produce a resource-intensive veneer of thoroughness without substantive improvements to understanding.

Phenomena complexity, disciplinary consensus, and experimental versus correlational research in psychological science.

The target article ignores the crucial role of correlational methods in the behavioral and social sciences. Yet such methods are often mandated by the greater complexity of the phenomena investigated. This necessity is especially conspicuous in psychological research where its position in the hierarchy of the sciences implies the need for both experimental and correlational investigations, each featuring distinct assets.

Optimally generate policy-based evidence before scaling.

Social scientists have increasingly turned to the experimental method to understand human behaviour. One critical issue that makes solving social problems difficult is scaling up the idea from a small group to a larger group in more diverse situations. The urgency of scaling policies impacts us every day, whether it is protecting the health and safety of a community or enhancing the opportunities of future generations. Yet, a common result is that, when we scale up ideas, most experience a 'voltage drop'-that is, on scaling, the cost-benefit profile depreciates considerably. Here I argue that, to reduce voltage drops, we must optimally generate policy-based evidence. Optimality requires answering two crucial questions: what information should be generated and in what sequence. The economics underlying the science of scaling provides insights into these questions, which are in some cases at odds with conventional approaches. For example, there are important situations in which I advocate flipping the traditional social science research model to an approach that, from the beginning, produces the type of policy-based evidence that the science of scaling demands. To do so, I propose augmenting efficacy trials by including relevant tests of scale in the original discovery process, which forces the scientist to naturally start with a recognition of the big picture: what information do I need to have scaling confidence?

Social science and teams: a patient perspective.

Teamwork is fundamental for providing high quality care and providing positive experiences for patients. Concepts from organisational behaviour and social science are integrated with a patient perspective to elucidate how various aspects of teams impact on the patient experience. Particular attention is devoted to the team composition, the interaction and communication patterns among team members, how teams are structured, coordination among team members, facilitating a positive team climate, and considering patients as a member of the team.

Which History and Social Science Concepts Should Inform Health Professions Education?

Teaching and learning patient advocacy in academic health centers requires critical engagement with social, political, and cultural conceptions of racial difference. This article considers understandings of race and racism typically drawn upon in health care and suggests which historical and social science-based approaches should be used in health professions teaching and learning.