Prevalence, patterns and predictors of paranormal beliefs in The Netherlands: a several-analysts approach.

Paranormal beliefs encompass a wide variety of phenomena, including the existence of supernatural entities such as ghosts and witches, as well as extraordinary human abilities such as telepathy and clairvoyance. In the current study, we used a nationally representative sample ( N = 2534 ) to investigate the presence and correlates of paranormal beliefs among the secular Dutch population. The results indicated that most single paranormal phenomena (e.g. belief in clairvoyance) are endorsed by 10-20% of Dutch respondents; however, 55.6% of respondents qualify as paranormal believers based on the preregistered criterion that they believe in at least one phenomenon with considerable certainty. In addition, we invited four analysis teams with different methodological expertise to assess the structure of paranormal beliefs using traditional factor analysis, network analysis, Bayesian network analysis and latent class analysis (LCA). The teams' analyses indicated adequate fit of a four-factor structure reported in a 1985 study, but also emphasized different conclusions across techniques; network analyses showed evidence against strong connectedness within most clusters, and suggested a five-cluster structure. The application of various analytic techniques painted a nuanced picture of paranormal beliefs and believers in The Netherlands and suggests that despite increased secularization, subgroups of the general population still believe in paranormal phenomena.

What is it that you say you do here? Advocating for the critical role of data scientists in research infrastructure.

Clinical and academic research continues to become more complex as our knowledge and technology advance. A substantial and growing number of specialists in biostatistics, data science, and library sciences are needed to support these research systems and promote high-caliber research. However, that support is often marginalized as optional rather than a fundamental component of research infrastructure. By building research infrastructure, an institution harnesses access to tools and support/service centers that host skilled experts who approach research with best practices in mind and domain-specific knowledge at hand. We outline the potential roles of data scientists and statisticians in research infrastructure and recommend guidelines for advocating for the institutional resources needed to support these roles in a sustainable and efficient manner for the long-term success of the institution. We provide these guidelines in terms of resource efficiency, monetary efficiency, and long-term sustainability. We hope this work contributes to-and provides shared language for-a conversation on a broader framework beyond metrics that can be used to advocate for needed resources.

Enhancing Qualitative and Quantitative Data Linkages in Complex Mixed Methods Designs: Illustrations from a Multi-Phase Healthcare Delivery Study.

While mixed methods research is increasingly used to examine determinants of unwarranted variability in healthcare delivery and outcomes, novel integrative approaches are required to meet the needs of mixed methods healthcare delivery research. This article describes novel refining strategies that enhance the linkage between qualitative and quantitative dimensions of a mixed methods healthcare delivery research study. Leveraging our study experiences, this paper demonstrates several refining strategies: (1) using mediated allocation concealment to facilitate qualitative sampling; (2) informing qualitative inquiry through quantitative analytics; and (3) training and immersing multidisciplinary researchers in qualitative data collection and analysis. Developing and implementing strategies in mixed methods healthcare delivery research could advance methodological rigor and strengthen multidisciplinary collaboration.

Conducting Research at the Intersection of HCI and Health: Building and Supporting Teams with Diverse Expertise to Increase Public Health Impact.

Research at the intersection of human-computer interaction (HCI) and health is increasingly done by collaborative cross-disciplinary teams. The need for cross-disciplinary teams arises from the interdisciplinary nature of the work itself-with the need for expertise in a health discipline, experimental design, statistics, and computer science, in addition to HCI. This work can also increase innovation, transfer of knowledge across fields, and have a higher impact on communities. To succeed at a collaborative project, researchers must effectively form and maintain a team that has the right expertise, integrate research perspectives and work practices, align individual and team goals, and secure funding to support the research. However, successfully operating as a team has been challenging for HCI researchers, and can be limited due to a lack of training, shared vocabularies, lack of institutional incentives, support from funding agencies, and more; which significantly inhibits their impact. This workshop aims to draw on the wealth of individual experiences in health project team collaboration across the CHI community and beyond. By bringing together different stakeholders involved in HCI health research, together, we will identify needs experienced during interdisciplinary HCI and health collaborations. We will identify existing practices and success stories for supporting team collaboration and increasing HCI capacity in health research. We aim for participants to leave our workshop with a toolbox of methods to tackle future team challenges, a community of peers who can strive for more effective teamwork, and feeling positioned to make the health impact they wish to see through their work.

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.

Introduction: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. Methods: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Results: Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Conclusions: Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.

Subjective evidence evaluation survey for many-analysts studies.

Many-analysts studies explore how well an empirical claim withstands plausible alternative analyses of the same dataset by multiple, independent analysis teams. Conclusions from these studies typically rely on a single outcome metric (e.g. effect size) provided by each analysis team. Although informative about the range of plausible effects in a dataset, a single effect size from each team does not provide a complete, nuanced understanding of how analysis choices are related to the outcome. We used the Delphi consensus technique with input from 37 experts to develop an 18-item subjective evidence evaluation survey (SEES) to evaluate how each analysis team views the methodological appropriateness of the research design and the strength of evidence for the hypothesis. We illustrate the usefulness of the SEES in providing richer evidence assessment with pilot data from a previous many-analysts study.

Individual and team performance in cricket.

Advancements in technology have recently allowed us to collect and analyse large-scale fine-grained data about human performance, drastically changing the way we approach sports. Here, we provide the first comprehensive analysis of individual and team performance in One-Day International cricket, one of the most popular sports in the world. We investigate temporal patterns of individual success by quantifying the location of the best performance of a player and find that they can happen at any time in their career, surrounded by a burst of comparable top performances. Our analysis shows that long-term performance can be predicted from early observations and that temporary exclusions of players from teams are often due to declining performances but are also associated with strong comebacks. By computing the duration of streaks of winning performances compared to random expectations, we demonstrate that teams win and lose matches consecutively. We define the contributions of specialists such as openers, all-rounders and wicket-keepers and show that a balanced performance from multiple individuals is required to ensure team success. Finally, we measure how transitioning to captaincy in the team improves the performance of batsmen, but not that of bowlers. Our work emphasizes how individual endeavours and team dynamics interconnect and influence collective outcomes in sports.

Bridging the Divide: A Review on the Implementation of Personalized Cancer Medicine.

The shift towards personalized cancer medicine (PCM) represents a significant transformation in cancer care, emphasizing tailored treatments based on the genetic understanding of cancer at the cellular level. This review draws on recent literature to explore key factors influencing PCM implementation, highlighting the role of innovative leadership, interdisciplinary collaboration, and coordinated funding and regulatory strategies. Success in PCM relies on overcoming challenges such as integrating diverse medical disciplines, securing sustainable investment for shared infrastructures, and navigating complex regulatory landscapes. Effective leadership is crucial for fostering a culture of innovation and teamwork, essential for translating complex biological insights into personalized treatment strategies. The transition to PCM necessitates not only organizational adaptation but also the development of new professional roles and training programs, underscoring the need for a multidisciplinary approach and the importance of team science in overcoming the limitations of traditional medical paradigms. The conclusion underscores that PCM's success hinges on creating collaborative environments that support innovation, adaptability, and shared vision among all stakeholders involved in cancer care.

Improving Exposure Therapy: Rationale and Design of an International Consortium.

The Exposure Therapy Consortium (ETC) was established to advance the science and practice of exposure therapy. To encourage participation from researchers and clinicians, this article describes the organizational structure and activities of the ETC. Initial research working group experiences and a proof-of-principle study underscore the potential of team science and larger-scale collaborative research in this area. Clinical working groups have begun to identify opportunities to enhance access to helpful resources for implementing exposure therapy effectively. This article discusses directions for expanding the consortium's activities and its impact on a global scale.

The journey from bench to bedside-it takes a science village.

I was fortunate enough to start my career at what was the dawn of modern-day molecular biology and to apply it to an important health problem. While my early work focused on fundamental science, the desire to understand human disease better and to find practical applications for research discoveries resulted, over the following decades, in creating a stream of translational research directed specifically toward epithelial cancers. This could only have been possible through multiple collaborations. This type of team science would eventually become a hallmark of my career. With the development of higher throughput molecular techniques, the pace of research and discovery has quickened, and the concept of personalized medicine based on genomics is now coming to fruition. I hope my legacy will not just reflect my published works, but will also include the impact I have had on the development of the next generation of scientists and clinician scientists who inspired me with their dedication, knowledge, and enthusiasm.

Andragogy in Practice: Applying a Theoretical Framework to Team Science Training in Biomedical Research.

This study is the first to apply the theoretical principles of Malcolm Knowles' theory of andragogy to evaluate data collected from learners who participated in team science training workshops in a biomedical research setting. Briefly, andragogy includes six principles: the learner's self-concept, the role of experience, readiness to learn, orientation to learning, the learner's need to know, and intrinsic motivation. Using an embedded study design, the primary focus was on qualitative data, with quantitative data complementing the qualitative findings. The deductive analysis demonstrated that approximately 85% of the qualitative data could be connected to at least one andragogical principle. Participant responses to positive evaluation questions were largely related to two principles: readiness to learn and problem-based learning orientation. Participant responses to negative questions were largely connected to two different principles: the role of experience and self-direction. Inductive analysis found an additional theme: meeting biological needs. Quantitative survey results supported the qualitative findings. The study findings demonstrate that andragogy can serve as a valuable construct to integrate into the development of effective team science training for biomedical researchers.

The Paper Chase: A Team Science Training Exercise.

Over the past few decades of psychological research, there has been an important increase in both the application of multidisciplinary or collaborative science and in training and research that emphasizes social justice and cultural humility. In the current paper, we report on the use of the "Paper Chase" as a team science training and research experience that also facilitates cultural humility in research and when working in teams. The Paper Chase is a synchronous writing exercise originally conceptualized by a cohort of health service psychology interns to reduce lag time between manuscript writing and submission (Schaumberg et al., 2015). The Paper Chase involves a group of trainees coming together for a predetermined amount of time (e.g., 9 or more hours) with the aim of writing and submitting a full manuscript for publication. In the current paper, we extend a previous report on the Paper Chase by formally linking the training experience to the four phases of team science: development, conceptualization, implementation, and translation. We also discuss ways in which the Paper Chase as a training experience can promote cultural humility. Finally, we provide updated recommendations for successfully completing a Paper Chase project. Overall, the authors of this manuscript who were predoctoral psychology interns across two recent cohorts at one academic medical center reported positive experiences from the Paper Chase. In addition, the current study suggests the Paper Chase can be used as one activity that facilitates critical training in team science.

Developing a shared understanding of translational science within CTSA hubs through facilitated retreats: A case study.

Translation of critical and broadly impactful health advancements is stymied by insufficient scientific scrutiny of barriers and roadblocks in the process. The Clinical & Translational Science Award (CTSA) funding opportunity announcement released in July 2021 makes clear the distinction between translational research and translational science (TS) and urges a shift from the former to the latter. This represents a significant shift in the overall scientific direction of the CTSA program and necessitates corresponding shifts in CTSA hub operations. To better support TS, the Team Science Core of the Duke CTSA hub designed and facilitated a virtual retreat for hub personnel that (1) enabled organizational learning about TS and (2) identified anticipated challenges and opportunities. A post-retreat survey was utilized to assess the degree to which the retreat met its stated goals. Our survey received a 62% response rate; 100% of respondents would recommend the session to others. Respondents also reported gains in all areas assessed, with evidence for greater understanding of TS and increased perspective of the value and relevance of TS. In this paper, we provide a roadmap for designing and implementing facilitated TS retreats, which we argue is a key step in TS capacity building through workforce development.

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions.

BACKGROUND: The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. MAIN TEXT: This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. CONCLUSIONS: Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

Is team science valued in the academic promotions process? A mixed-methods case study.

Introduction: Traditionally, research institutions have valued individual achievements such as principal investigator and lead authorship status as primary indicators in the academic promotions process. However, the scientific process increasingly requires collaboration by teams of researchers across multiple disciplines, sometimes including experts outside academia, often referred to as "team science." We sought to determine whether there is agreement about what constitutes team science at our academic institution and whether current promotion processes sufficiently incentivize faculty participation in team science. Methods: We conducted 20 qualitative interviews with academic leaders (N = 24) at the University of California, San Francisco (UCSF) who supervise faculty promotions processes. Participants were asked to share their definitions of team science and the extent to which faculty receive credit for engaging in these activities during the promotions process. A subset of participants also completed a brief survey in which they ranked the importance of participation in team science relative to other factors that are traditionally valued in the promotions process. Interview data were examined by two analysts using structural coding. Descriptive analyses were conducted of survey responses. Results: Though team science is valued at UCSF, definitions of team science and the approach to assigning credit for team science in academic promotions processes varied widely. Participants suggested opportunities to bolster support for team science. Conclusions: Efforts to define and provide transparent faculty incentives for team science should be prioritized at institutions, like UCSF, seeking to advance faculty engagement in collaborative research.

A randomized controlled trial of a team science intervention to enhance collaboration readiness and behavior among early career scholars in the Clinical and Translational Science Award network.

Introduction: Despite the central importance of cross-disciplinary collaboration in the Clinical and Translational Science Award (CTSA) network and the implementation of various programs designed to enhance collaboration, rigorous evidence for the efficacy of these approaches is lacking. We conducted a novel randomized controlled trial (RCT; ClinicalTrials.gov identifier: NCT05395286) of a promising approach to enhance collaboration readiness and behavior among 95 early career scholars from throughout the CTSA network. Methods: Participants were randomly assigned (within two cohorts) to participate in an Innovation Lab, a week-long immersive collaboration experience, or to a treatment-as-usual control group. Primary outcomes were change in metrics of self-reported collaboration readiness (through 12-month follow-up) and objective collaboration network size from bibliometrics (through 21 months); secondary outcomes included self-reported number of grants submitted and, among Innovation Lab participants only, reactions to the Lab experience (through 12 months). Results: Short-term reactions from Innovation Lab participants were quite positive, and controlled evidence for a beneficial impact of Innovation Labs over the control condition was observed in the self-reported number of grant proposals in the intent-to-treat sample. Primary measures of collaboration readiness were near ceiling in both groups, limiting the ability to detect enhancement. Collaboration network size increased over time to a comparable degree in both groups. Conclusions: The findings highlight the need for systematic intervention development research to identify efficacious strategies that can be implemented throughout the CTSA network to better support the goal of enhanced cross-disciplinary collaboration.

Applying Team Science to Collaborative Digital Health Research: Learnings from the Wearable Clinic.

Collaboration across disciplinary boundaries is vital to address the complex challenges and opportunities in Digital Health. We present findings and experiences of applying the principles of Team Science to a digital health research project called 'The Wearable Clinic'. Challenges faced were a lack of shared understanding of key terminology and concepts, and differences in publication cultures between disciplines. We also encountered more profound discrepancies, relating to definitions of "success" in a research project. We recommend that collaborative digital health research projects select a formal Team Science methodology from the outset.

Big team science initiatives: A catalyst for trustworthy advancements in IO psychology.

Keener et al. (2023) raise concerns about the trustworthiness of Industrial/Organizational (IO) Psychology research and related fields due to the low reproducibility and replicability of research findings. The authors provide various solutions to resolve this crisis, such as improving training, realigning incentives, and adopting open science practices. Our commentary elaborates on one solution to which they briefly allude: Big Team Science Initiatives (BTSIs). BTSIs allow scholars to address the trustworthiness of our science by facilitating large sample theory testing, sharing and allocating resources, and selecting appropriate research strategies, all of which support the reproducibility and replication of research. Further, we propose that BTSIs may facilitate researcher training, encourage data sharing and materials, and realign incentives in our field. We discuss how BTSIs could be implemented in IO psychology and related fields, identifying and drawing upon similar BTSIs in related disciplines. Thus, our commentary is an extension of the focal article, encouraging scholars to collaboratively address the "crisis of confidence" facing our field using a big team science approach.