Holly J. Falk-Krzesinski

A Multi-Level Systems Perspective for the Science of Team Science

Understanding how teams function is vital because they are increasingly dominating the production of high-impact science. This Commentary describes recent research progress and professional developments in the study of scientific teamwork, an area of inquiry termed the “science of team science” (SciTS, pronounced “sahyts”). It proposes a systems perspective that incorporates a mixed-methods approach to SciTS that is commensurate with the conceptual, methodological, and translational complexities addressed within the SciTS field. The theoretically grounded and practically useful framework is intended to integrate existing and future lines of SciTS research to facilitate the field’s evolution as it addresses key challenges spanning macro, meso, and micro levels of analysis.

Advancing the Science of Team Science

The First Annual International Science of Team Science (SciTS) Conference was held in Chicago, IL April 22–24, 2010. This article presents a summary of the Conference proceedings. Clin Trans Sci 2010; Volume 3: 263–266.

Mapping a research agenda for the science of team science

An increase in cross-disciplinary, collaborative team science initiatives over the last few decades has spurred interest by multiple stakeholder groups in empirical research on scientific teams, giving rise to an emergent field referred to as the science of team science (SciTS). This study employed a collaborative team science concept-mapping evaluation methodology to develop a comprehensive research agenda for the SciTS field. Its integrative mixed-methods approach combined group process with statistical analysis to derive a conceptual framework that identifies research areas of team science and their relative importance to the emerging SciTS field. The findings from this concept-mapping project constitute a lever for moving SciTS forward at theoretical, empirical, and translational levels.

The team science toolkit

Introduction Research teams, ranging from pairs of collaborators to large networks, are becoming the dominant paradigm in knowledge production. Across all research fields, teams now produce more frequently cited and higher impact research than individual authors. This trend—known as “team science” or “team-based research”—has emerged as a strategy to address increasingly complex scientific problems, often by applying sophisticated conceptual and methodologic approaches that draw on multiple disciplines, fields, and professions. Science teams bring together collaborators with a combined set of expertise that is uniquely suited to address particular scientific problems in innovative and effective ways. These specialized teams may be large in size; may include collaborators distributed across geographic space and organizational boundaries and with expertise that spans multiple disciplines, fields, and professions; and may involve academic, community, and translational partners. These complexities contribute to the potential added value

Opinion: Gender diversity leads to better science

Pick up any recent policy paper on women’s participation in science and you will find assurances that gender diversity enhances knowledge outcomes. Universities and science-policy stakeholders, including the European Commission and the US National Institutes of Health, readily subscribe to this argument (1⇓–3). But is there, in fact, a gender-diversity dividend in science? The data suggest that there is. Under the right conditions, teams may benefit from various types of diversity, including scientific discipline, work experience, gender, ethnicity, and nationality. In this paper, we highlight gender diversity (Fig. 1). Guided by key research findings, we propose the following “mechanisms for innovation” specifying why gender diversity matters for scientific discovery and what managers should do to maximize its benefits (Fig. 2). Encouraging greater diversity is not only the right thing to do: it allows scientific organizations to derive an “innovation dividend” that leads to smarter, more creative teams, hence opening the door to new discoveries. Fig. 1. When it comes to science collaborations, there’s ample data to suggest that gender diversity pays a substantial research and productivity dividend. Image courtesy of Dave Cutler (artist). Well-run, well-performing research teams have become increasingly crucial to the success of modern scientific investigations. Already, experimental research points to positive links between gender diversity and collective problem solving. In a study of group performance, Anita Woolley et al. (4) randomly assigned 699 participants to teams of varying sizes and asked them to solve a set of both simple and complicated tasks (e.g., visual puzzles, brainstorming, making collective moral judgments, and negotiating over limited resources). Through these experiments, the authors found evidence of a collective intelligence factor that predicts group performance better than the IQ of individual group members. Key components of this factor include the group members’ social perceptiveness and parity in conversational turn-taking. Furthermore, … [↵][1]1To whom correspondence should be addressed. Email: mwn{at}stanford.edu. [1]: #xref-corresp-1-1

Direct2Experts: A pilot national network to demonstrate interoperability among research-networking platforms

Research-networking tools use data-mining and social networking to enable expertise discovery, matchmaking and collaboration, which are important facets of team science and translational research. Several commercial and academic platforms have been built, and many institutions have deployed these products to help their investigators find local collaborators. Recent studies, though, have shown the growing importance of multiuniversity teams in science. Unfortunately, the lack of a standard data-exchange model and resistance of universities to share information about their faculty have presented barriers to forming an institutionally supported national network. This case report describes an initiative, which, in only 6 months, achieved interoperability among seven major research-networking products at 28 universities by taking an approach that focused on addressing institutional concerns and encouraging their participation. With this necessary groundwork in place, the second phase of this effort can begin, which will expand the networks functionality and focus on the end users.

Editorials and CommentaryThe Team Science Toolkit: Enhancing Research Collaboration Through Online Knowledge Sharing

Introduction Research teams, ranging from pairs of collaborators to large networks, are becoming the dominant paradigm in knowledge production. Across all research fields, teams now produce more frequently cited and higher impact research than individual authors. This trend—known as “team science” or “team-based research”—has emerged as a strategy to address increasingly complex scientific problems, often by applying sophisticated conceptual and methodologic approaches that draw on multiple disciplines, fields, and professions. Science teams bring together collaborators with a combined set of expertise that is uniquely suited to address particular scientific problems in innovative and effective ways. These specialized teams may be large in size; may include collaborators distributed across geographic space and organizational boundaries and with expertise that spans multiple disciplines, fields, and professions; and may involve academic, community, and translational partners. These complexities contribute to the potential added value

Individual motivation and threat indicators of collaboration readiness in scientific knowledge producing teams: a scoping review and domain analysis

This paper identifies a gap in the team science literature that considers intrapersonal indicators of collaboration as motivations and threats to participating in collaborative knowledge producing teams (KPTs). Through a scoping review process, over 150 resources were consulted to organize 6 domains of motivation and threat to collaboration in KPTs: Resource Acquisition, Advancing Science, Building Relationships, Knowledge Transfer, Recognition and Reward, and Maintenance of Beliefs. Findings show how domains vary in their presentation of depth and diversity of motivation and threat indicators as well as their relationship with each other within and across domains. The findings of 51 indicators resulting from the review provide a psychosocial framework for which to establish a hierarchy of collaborative reasoning for individual engagement in KPTs thus allowing for further research into the mechanism of collaborative engagement. The indicators serve as a preliminary step in establishing a protocol for testing of the psychometric properties of intrapersonal measures of collaboration readiness.

An emerging science and praxis for research and practice teams

BACKGROUNDA meta-trend, observable over the past severaldecades, is that work is being conducted increasinglyby teams. The proportion of scientific publicationsauthoredbygroupsratherthansoloauthorshasmorethandoubledinthepast50years[1,2].Asthevolumeofscientificknowledgehasexpandedovertime,ithasbecome increasingly difficultfora single individual tohave deep expertise in multiple disciplines. Forexample, Galileo defined modern physics while alsocreating the telescope that launched observationalastronomy,andDescartesshapedmodernphilosophywhilealsoinventinganalyticgeometry.ThesekindsofRenaissance era contributions made by individualsworking alone have become increasingly rare, and —we believe—for a good reason. Solving complexproblemsnowroutinelyrequirescollaborationamongexperts from different specialties working to reachshared understandings that integrate specializedknowledge bases [3, 4].In modern health care as well, solo practices aredwindling [5]. More than 50 % of practicing USphysicians are employed by hospitals or integratedcare systems, where they need to collaborate withother practitioners, and that number is expected torise to 75 % in the near future [6]. The rising costs ofequipment, legal compliance, insurance, and newIT requirements coupled with dwindling or stablereimbursements have made it increasingly challeng-ing for sole practitioners to stay afloat financially.However, the movement toward team-based care isdriven by more than financial exigency; its aim isalso to provide higher quality care.Interprofessional teams are integral to new mod-els of care implementation that emerged as part ofUS health care reform. The so-called “patient-centered health home” elevates the role of thepatient in health decision-making and encouragespartnership with primary care providers aroundshared goals [7]. Chief responsibility to coordinatecare among stakeholders is ascribed to primarycare, with debate still ongoing about which memberof the primary care team is best suited to assume thecoordination role (e.g., primary care physician,nurse, physician assistant, and social worker).Relevant stakeholders include the patient, family,and caregivers, as well as health care generalists,specialists, residential placements, and the commu-nity. Health care professionals are expected to sharedata and to optimize their management of theprimary care team, such that all health professionalsare communicating effectively and working at thetop of their training. Good evidence suggests that,when these conditions can be attained, interprofes-sional team-based care provides higher quality andmore efficient care delivery, in addition to being lesscostly. Effective team-based care has been shown toimprove patient satisfaction, reduce patient waitingtime, decrease emergency room use and rehospital-ization, and reduce cost per patient while improvingpatient satisfaction [7–9].There is growing consensus that solutions tocomplex scienti fic and practical problems bene tfrom the efforts of specialists from diverse back-grounds working across disciplinary silos. Mountingevidencefromorganizational,management,andteamresearch supports the premise that, when done well,cross-disciplinary science and interprofessional prac-tice teams can produce more innovative, moreimpactful results, as compared to solo individuals orteamswhose members represent asingle disciplineorprofession. The purpose of this special section is tohighlight new developments and contributions in-volving the use of a team-based approach to facilitatebehavioralmedicineresearchandpracticetranslation.THE SCIENCE OF TEAM SCIENCEConceptual and theoretical models are needed tohelp organize the behavioral, social, organizational,and management domains of knowledge that in-form the emerging science of team science (SciTS).Delineation of models that abstract beyond a localevidence base enables the validity of theoreticalprinciples to be tested against new observations.Generalizable principles that stand up across di-verse contexts serve as building blocks for aconceptual framework. A theoretical conceptualiza-tion that explains existing evidence generates newscientific hypotheses and supports translation fromresearch to practice and policy applications.Drawing from a range of literatures, Hall andcolleagues [10] present a four-phase model oftransdisciplinary research. After outlining the models

Pilot analysis of the Motivation Assessment for Team Readiness, Integration, and Collaboration (MATRICx) using Rasch analysis

Healthcare services and the production of healthcare knowledge are increasingly dependent on highly functioning, multidisciplinary teams, requiring greater awareness of individuals’ readiness to collaborate in translational science teams. Yet, there is no comprehensive tool of individual motivations and threats to collaboration that can guide preparation of individuals for work on well-functioning teams. This prospective pilot study evaluated the preliminary psychometric properties of the Motivation Assessment for Team Readiness, Integration, and Collaboration (MATRICx). We examined 55 items of the MATRICx in a sample of 125 faculty, students and researchers, using contemporary psychometric methods (Rasch analysis). We found that the motivator and threat items formed separate constructs relative to collaboration readiness. Further, respondents who identified themselves as inexperienced at working on collaborative projects defined the motivation construct differently from experienced respondents. These results are consistent with differences in strategic alliances described in the literature—for example, inexperienced respondents reflected features of cooperation and coordination, such as concern with sharing information and compatibility of goals. In contrast, the more experienced respondents were concerned with issues that reflected a collective purpose, more typical of collaborative alliances. While these different types of alliances are usually described as representing varying aspects along a continuum, our findings suggest that collaboration might be better thought of as a qualitatively different state than cooperation or coordination. These results need to be replicated in larger samples, but the findings have implications for the development and design of educational interventions that aim to ready scientists and clinicians for greater interdisciplinary work.