Complexity leadership in action: a team science case study.

Introduction: This team science case study explores one cross-disciplinary science institute's change process for redesigning a weekly research coordination meeting. The narrative arc follows four stages of the adaptive process in complex adaptive systems: disequilibrium, amplification, emergence, and new order. Methods: This case study takes an interpretative, participatory approach, where the objective is to understand the phenomena within the social context and deepen understanding of how the process unfolds over time and in context. Multiple data sources were collected and analyzed. Results: A new adaptive order for the weekly research coordination meeting was established. The mechanism for the success of the change initiative was best explained by complexity leadership theory. Discussion: Implications for team science practice include generating momentum for change, re-examining power dynamics, defining critical teaming professional roles, building multiple pathways towards team capacity development, and holding adaptive spaces. Promising areas for further exploration are also presented.

Development of a clinical and translational research curriculum for undergraduate students.

Introduction: Research participation during undergraduate years has a powerful influence on career selection and attitudes toward scientific research. Most undergraduate research programs in academic health centers are oriented toward basic research or address a particular disease focus or research discipline. Undergraduate research programs that expose students to clinical and translational research may alter student perceptions about research and influence career selection. Methods: We developed an undergraduate summer research curriculum, anchored upon a clinical and translational research study developed to address a common unmet needs in neonatal nurseries (e.g., assessment of neonatal opioid withdrawal syndrome). Program topics reflected the cross-disciplinary expertise that contributed to the development of this "bedside to bench" study, including opioid addiction, vulnerable populations, research ethics, statistics, data collection and management, assay development, analytical laboratory analysis, and pharmacokinetics. The curriculum was delivered through three offerings over 12 months, using Zoom video-conferencing due to restrictions imposed by the COVID-19 pandemic. Results: Nine students participated in the program. Two-thirds reported the course enhanced their understanding of clinical and translational research. Over three-quarters reported the curriculum topics were very good or excellent. In open-ended questions, students reported that the cross-disciplinary nature of the curriculum was the strongest aspect of the program. Conclusion: The curriculum could be readily adapted by other Clinical and Translational Science Award programs seeking to provide clinical and translational research-oriented programs to undergraduate students. Application of cross-disciplinary research approaches to a specific clinical and translational research question provides students with relevant examples of translational research and translational science.

Team science criteria and processes for promotion and tenure of Health Science University Faculty.

Although team science has expanded with far-reaching benefits, universities generally have not established criteria to recognize its value in faculty promotion and tenure. This paper recommends how institutions might weigh a faculty member's engagement in team science in the promotion and tenure process. Seventeen team science promotion and tenure criteria are recommended based on four sources - an evaluation framework, effectiveness metrics, collaborative influences, and authorship criteria. Suggestions are made for adaptation of the 17 criteria to committee guidelines, faculty team science portfolios, and the roles of individuals and institutions participating in large, cross-disciplinary research projects. Future research recommendations are advanced.

The gulf of cross-disciplinary research collaborations on global river basins is not narrowed.

Using publications in the Web of Science database (WoS), this study investigates the research collaboration on the top 95 most researched global river basins since 1900. The links of both the disciplines involved and the management issues studied between the biophysical, economic, societal, climatic and governance sub-systems of these river basins were examined. We found that research collaborations were dominated within the biophysical sub-system (65.3%) since the knowledge predevelopment period (1900-1983), with continuous increases (by 18.5%) during the rapid development (1984-2000) and the stabilisation (12.9% increase) (2001-2017). However, research collaborations related to the societal sub-system remained marginalised (varied at about 1%), while those related to the governance sub-system expanded in issues studied (32.8%) but were not supported by the core governance disciplines (3.4%). The key findings explained why global river basins are degraded from the perspective of knowledge development and they can assist the strategic planning and management of scientific research for improving governance capacity in modifying the relationship between human and nature on river basins in the Anthropocene. Tackling challenges in the Anthropocene requires transformation of the current pattern of knowledge development, a revolution in the governance of science.

A systems approach to enable effective team science from the internal research program of the National Center for Advancing Translational Sciences.

The internal research program of the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health aims to fundamentally transform the preclinical translational research process to get more treatments to more people more quickly. The program develops and implements innovative scientific and operational approaches that accelerate and enhance translation across many diverse projects. Cross-disciplinary team science is a defining feature of our organization, with scientists at all levels engaged in multiple research teams. Here, we share our systems approach to nurturing cross-disciplinary team science, which leverages organizational policies, structures, and processes. Policies including the organizational mission statement, principles for ethical conduct of research, performance review criteria, and training program objectives and approaches reinforce the value of team science to achieve the program's scientific goals. Structures including an organizational structure designed around solving translational problems, co-location of employees in a single state-of-the-art scientific facility, and shared-use laboratories, expertise and instrumentation facilitate collaboration. Processes including fluid team assembly, specialized project management, cross-agency partnerships, and decision making based on clear screening criteria and milestones enable effective team assembly and functioning. We share evidence of the impact of these approaches on the science and commercialization of findings and discuss pathways to broad adoption of similar approaches.

Cross-disciplinary intersections between public health and economics in intimate partner violence research.

Research on intimate partner violence (IPV) has progressed in the last decade in the fields of public health and economics, with under-explored potential for cross-fertilisation. We examine the theoretical perspectives and methodological approaches that each discipline uses to conceptualise and study IPV and offer a perspective on their relative advantages. Public health takes a broad theoretical perspective anchored in the socio-ecological framework, considering multiple and synergistic drivers of IPV, while economics focuses on bargaining models which highlight individual power and factors that shape this power. These perspectives shape empirical work, with public health examining multi-faceted interventions, risk and mediating factors, while economics focuses on causal modelling of specific economic and institutional factors and economic-based interventions. The disciplines also have differing views on measurement and ethics in primary research. We argue that efforts to understand and address IPV would benefit if the two disciplines collaborated more closely and combined the best traditions of both fields.

[Cross-Disciplinary Research in Nursing: Competency and Practice].

Cross-disciplinarity is a current trend in healthcare. With the advancement of science and technology, the expansion of care fields, and the complexity of health problems, cross-disciplinary research has been increasingly emphasized in nursing studies in order to introduce technology into patient care, expand the scope of healthcare research, and improve quality of care. The term cross-disciplinary research typically covers multidisciplinary, interdisciplinary, and transdisciplinary studies. Each of these types of studies differ in terms of connotation, level of research problem addressed, and degree of interaction involved. The main purpose of this article is to describe the significance of cross-disciplinary research in nursing and to distinguish the types and nature of cross-disciplinary studies. Furthermore, reflections and recommendations on cross-disciplinary nursing research are also proposed. The development of cross-disciplinary nursing research is phased in nature and requires the creation of a cross-disciplinary research center and excellent leadership. Conducting cross-disciplinary nursing research is challenging and affected by uncertainty. Researchers may select the type of cross-disciplinary research that best addresses the complexity and commonality of the research problem being addressed. In addition, researchers may expand, communicate, and interact with other disciplines to improve their interdisciplinary research capabilities and opportunities.

Factors Associated with Favorable Changes in Food Preferences After Bariatric Surgery.

PURPOSE: Bariatric surgery may shift food preferences towards less energy-dense foods. Eating behavior is multifactorial, and the mechanisms driving changes in food preferences could be a combination of a physiological response to surgery and social and psychological factors. This exploratory study aimed to identify potential factors explaining the variation in changes in food preferences after bariatric surgery. MATERIALS AND METHODS: Physiological, social, and psychological data were collected before, 6 weeks or 6 months after surgery. All variables were analyzed in combination using LASSO regression to explain the variation in changes in energy density at an ad libitum buffet meal 6 months after bariatric surgery (n=39). RESULTS: The following factors explained 69% of the variation in changes in food preferences after surgery and were associated with more favorable changes in food preferences (i.e., a larger decrease in energy density): female gender, increased secretion of glicentin, a larger decrease in the hedonic rating of sweet and fat and a fatty cocoa drink, a lower number of recent life crises, a low degree of social eating pressure, fulfilling the diagnostic criteria for binge eating disorder, less effort needed to obtain preoperative weight loss, a smaller household composition, a lower degree of self-efficacy and a higher degree of depression, nutritional regime competence, and psychosocial risk level. CONCLUSION: Factors explaining the variation in altered food preferences after bariatric surgery not only include a physiological response to surgery but also social and psychological factors.

Key opportunities and challenges for the use of big data in migration research and policy.

Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidence-based migration policy. Big data, defined as large, complex data from diverse sources, is regularly proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the United Nations (UN), humanitarian non-governmental organisations (NGOs), policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns around the use of big data; and addressing political narratives. We advocate the need for careful consideration of the challenges faced by the use of big data, as well as increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities.

A critical realist synthesis of cross-disciplinary health policy and systems research: defining characteristic features, developing an evaluation framework and identifying challenges.

BACKGROUND: Health policy and systems research (HPSR) is an inherently cross-disciplinary field of investigation. However, conflicting conceptualisations about inter-, multi- and transdisciplinary research have contributed to confusion about the characteristics of cross-disciplinary approaches in HPSR. This review was conducted to (1) define the characteristic features of context-mechanism-outcome (CMO) configurations in cross-disciplinary HPSR, (2) develop criteria for evaluating cross-disciplinarity and (3) synthesise emerging challenges of the approach. METHOD: The paper is a critical realist synthesis conducted in three phases, as follows: (1) scoping the literature, (2) searching for and screening the evidence, and (3) extracting and synthesising the evidence. Five databases, namely the International Bibliography of the Social Sciences and Web of Science, PubMed central, Embase and CINHAL, and reference lists of studies that qualified for inclusion in the review were searched. The search covered peer-reviewed original research, reviews, commentary papers, and institutional or government reports published in English between January 1998 and January 2020. RESULTS: A total of 7792 titles were identified in the online search and 137 publications, comprising pilot studies as well as anecdotal and empirical literature were selected for the final review. The review draws attention to the fact that cross-disciplinary HPSR is not defined by individual characteristics but by the combination of a particular type of research question and setting (context), a specific way of researchers working together (mechanism), and research output (outcome) that is superior to what could be achieved under a monodisciplinary approach. This CMO framework also informs the criteria for assessing whether a given HPSR is truly cross-disciplinary. The challenges of cross-disciplinary HPSR and their accompanying coping mechanisms were also found to be context driven, originating mainly from conceptual disagreements, institutional restrictions, communication and information management challenges, coordination problems, and resource limitations. CONCLUSION: These findings have important implications. First, the CMO framework of cross-disciplinary HPSR can provide guidance for researchers engaging in new projects and for policy-makers using their findings. Second, the proposed criteria for evaluating theory and practice of cross-disciplinary HPSR may inform the systematic development of new research projects and the structured assessment of existing ones. Third, a better understanding of the challenges of cross-disciplinary HPSR and potential response mechanisms may help researchers to avoid these problems in the future.

Protocol for a randomised controlled trial of a co-produced, complex, health promotion intervention for women with prior gestational diabetes and their families: the Face-it study.

BACKGROUND: Gestational diabetes mellitus (GDM) is associated with an increased risk of future diabetes in both mother, father and offspring. More knowledge is needed about how to effectively reduce the risk of diabetes through sustained behavioural interventions in these families. The Face-it intervention is a complex health promotion intervention embedded in multi-level supportive environments. The aim of the intervention is to reduce type 2 diabetes risk and increase quality of life among families in the first year following a GDM-affected pregnancy by promoting physical activity, healthy dietary behaviours and breastfeeding through a focus on social support, motivation, self-efficacy, risk perception and health literacy. METHODS: This national multicentre study is a two-arm randomised controlled trial including 460 women with GDM in a ratio of 2 (intervention):1 (usual care). The Face-it intervention consists of three main components: 1) additional visits from municipal health visitors, 2) digital health coaching tailored to family needs and 3) a structured cross-sectoral communication system in the health care system. The intervention runs from 3 to 12 months after delivery. The primary outcome is maternal body mass index at 12 months after delivery as a proxy for diabetes risk. The women will be examined at baseline and at follow-up, and this examination will include blood tests, oral glucose tolerance test (OGTT), anthropometrics, blood pressure, self-reported diet and physical activity, breastfeeding, quality of life, health literacy, physical and mental health status, risk perception and social support. Aside from those data collected for OGTT and breastfeeding and offspring parameters, the same data will be collected for partners. Data on offspring anthropometry will also be collected. Information on pregnancy- and birth-related outcomes will be derived from the medical records of the woman and child. DISCUSSION: This randomised controlled trial seeks to demonstrate whether the Face-it intervention, addressing the individual, family and health care system levels, is superior to usual care in reducing diabetes risk for mothers and their families. Coupled with a process evaluation and an economic analysis, the study will provide evidence for policymakers and health services about health promotion among families affected by GDM and the potential for reducing risk of type 2 diabetes and associated conditions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03997773. Registered June 25, 2019 - Retrospectively registered.

Online, cross-disciplinary team science training for health and medical professionals: Evaluation of COALESCE (teamscience.net).

INTRODUCTION: The National Academies of Sciences (NAS) emphasize the need for interdisciplinary team science (TS) training, but few training resources are available. COALESCE, an open-access tool developed with National Institutes of Health support and located at teamscience.net, is considered a gold standard resource but has not previously been evaluated. COALESCE launched four learning modules in 2011. The Science of TS (SciTS) module, an interactive encyclopedia, introduces foundational concepts. Three scenario-based modules simulate TS challenges in behavioral, clinical, and basic biomedical sciences. This study examined user characteristics, usage patterns, and effects of completing the four modules on TS knowledge, attitudes, and skills. METHODS: Repeated measures ANOVA tested for pre-post changes in performance and compared learning by users with biomedical versus other disciplinary backgrounds. RESULTS: From 2011 through 2017, the site attracted 16,280 new users who engaged in 6461 sessions that lasted more than 1 min. The modal registrant identified as working in a biomedical field (47%), in an academic institution (72%), and expressed greater interest in the practice than the SciTS (67%). Those completing pre- and post-tests (n = 989) showed significant improvement in knowledge, attitudes, and skills after taking all scenario-based modules (p < 0.005); knowledge and attitudes were unchanged after the SciTS encyclopedia. Biomedical and other health professionals improved comparably. CONCLUSION: Evaluation of the TS training tool at teamscience.net indicates broad dissemination and positive TS-related outcomes. Site upgrades implemented between 2018 and 2020, including adding five new modules, are expected to increase the robustness and accessibility of the COALESCE training resource.

"Essentially, all models are wrong, but some are useful"-a cross-disciplinary agenda for building useful models in cell biology and biophysics.

Intuition alone often fails to decipher the mechanisms underlying the experimental data in Cell Biology and Biophysics, and mathematical modeling has become a critical tool in these fields. However, mathematical modeling is not as widespread as it could be, because experimentalists and modelers often have difficulties communicating with each other, and are not always on the same page about what a model can or should achieve. Here, we present a framework to develop models that increase the understanding of the mechanisms underlying one's favorite biological system. Development of the most insightful models starts with identifying a good biological question in light of what is known and unknown in the field, and determining the proper level of details that are sufficient to address this question. The model should aim not only to explain already available data, but also to make predictions that can be experimentally tested. We hope that both experimentalists and modelers who are driven by mechanistic questions will find these guidelines useful to develop models with maximum impact in their field.

Behind the scenes: A medical natural language processing project.

Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multidisciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning. In this paper, our team of engineers, clinicians and machine learning experts share their experience and lessons learned from their two-year-long collaboration on a natural language processing (NLP) research project. We highlight specific challenges encountered in cross-disciplinary teamwork, dataset creation for NLP research, and expectation setting for current medical AI technologies.

GIFT-Cloud: A data sharing and collaboration platform for medical imaging research.

OBJECTIVES: Clinical imaging data are essential for developing research software for computer-aided diagnosis, treatment planning and image-guided surgery, yet existing systems are poorly suited for data sharing between healthcare and academia: research systems rarely provide an integrated approach for data exchange with clinicians; hospital systems are focused towards clinical patient care with limited access for external researchers; and safe haven environments are not well suited to algorithm development. We have established GIFT-Cloud, a data and medical image sharing platform, to meet the needs of GIFT-Surg, an international research collaboration that is developing novel imaging methods for fetal surgery. GIFT-Cloud also has general applicability to other areas of imaging research. METHODS: GIFT-Cloud builds upon well-established cross-platform technologies. The Server provides secure anonymised data storage, direct web-based data access and a REST API for integrating external software. The Uploader provides automated on-site anonymisation, encryption and data upload. Gateways provide a seamless process for uploading medical data from clinical systems to the research server. RESULTS: GIFT-Cloud has been implemented in a multi-centre study for fetal medicine research. We present a case study of placental segmentation for pre-operative surgical planning, showing how GIFT-Cloud underpins the research and integrates with the clinical workflow. CONCLUSIONS: GIFT-Cloud simplifies the transfer of imaging data from clinical to research institutions, facilitating the development and validation of medical research software and the sharing of results back to the clinical partners. GIFT-Cloud supports collaboration between multiple healthcare and research institutions while satisfying the demands of patient confidentiality, data security and data ownership.

A framework for a joint hydro-meteorological-social analysis of drought.

This article presents an innovative framework for analysing environmental governance challenges by focusing on their Drivers, Responses and Impacts (DRI). It builds on and modifies the widely applied Drivers, Pressures, States, Impacts and Responses (DPSIR) model. It suggests, firstly and most importantly, that the various temporal and spatial scales at which Drivers, Responses and Impacts operate should be included in the DRI conceptual framework. Secondly, the framework focuses on Drivers, Impacts and Responses in order to provide a parsimonious account of a drought system that can be informed by a range of social science, humanities and science data. 'Pressures' are therefore considered as a sub-category of 'Drivers'. 'States' are a sub-category of 'Impacts'. Thirdly, and most fundamentally in order to facilitate cross-disciplinary research of droughts, the DRI framework defines each of its elements, 'Drivers', 'Pressures', 'States', 'Impacts' and 'Responses' as capable of being shaped by both linked natural and social factors. This is different from existing DPSIR models which often see 'Responses' and 'Impacts' as located mainly in the social world, while 'States' are considered to be states within the natural environment only. The article illustrates this argument through an application of the DRI framework to the 1976 and 2003-6 droughts. The article also starts to address how - in cross-disciplinary research that encompasses physical and social sciences - claims about relationships between Drivers as well as Impacts of and Responses to drought over time can be methodologically justified. While the DRI framework has been inductively developed out of research on droughts we argue that it can be applied to a range of environmental governance challenges.

Bioarchitecture: bioinspired art and architecture--a perspective.

Art and architecture can be an obvious choice to pair with science though historically this has not always been the case. This paper is an attempt to interact across disciplines, define a new genre, bioarchitecture, and present opportunities for further research, collaboration and professional cooperation. Biomimetics, or the copying of living nature, is a field that is highly interdisciplinary, involving the understanding of biological functions, structures and principles of various objects found in nature by scientists. Biomimetics can lead to biologically inspired design, adaptation or derivation from living nature. As applied to engineering, bioinspiration is a more appropriate term, involving interpretation, rather than direct copying. Art involves the creation of discrete visual objects intended by their creators to be appreciated by others. Architecture is a design practice that makes a theoretical argument and contributes to the discourse of the discipline. Bioarchitecture is a blending of art/architecture and biomimetics/bioinspiration, and incorporates a bioinspired design from the outset in all parts of the work at all scales. Herein, we examine various attempts to date of art and architecture to incorporate bioinspired design into their practice, and provide an outlook and provocation to encourage collaboration among scientists and designers, with the aim of achieving bioarchitecture.This article is part of the themed issue 'Bioinspired hierarchically structured surfaces for green science'.

Cross-disciplinary research in cancer: an opportunity to narrow the knowledge-practice gap.

Health services researchers have consistently identified a gap between what is identified as "best practice" and what actually happens in clinical care. Despite nearly two decades of a growing evidence-based practice movement, narrowing the knowledge-practice gap continues to be a slow, complex, and poorly understood process. Here, we contend that cross-disciplinary research is increasingly relevant and important to reducing that gap, particularly research that encompasses the notion of transdisciplinarity, wherein multiple academic disciplines and non-academic individuals and groups are integrated into the research process. The assimilation of diverse perspectives, research approaches, and types of knowledge is potentially effective in helping research teams tackle real-world patient care issues, create more practice-based evidence, and translate the results to clinical and community care settings. The goals of this paper are to present and discuss cross-disciplinary approaches to health research and to provide two examples of how engaging in such research may optimize the use of research in cancer care.