The CTSA Diversity, Equity, Inclusion, and Accessibility (DEIA) Task Force's recommendations for the CTSA program consortium

The Clinical and Translational Science Award (CTSA) Program recognizes that advancing diversity, equity, inclusion, and accessibility (DEIA) requires moving beyond statements of commitment to transformative actions. In 2021, the CTSA Program created a Task Force (TF) to initiate work in support of structural and transformational initiatives that advance DEIA for the consortium and its individual hubs. We describe the process of forming the expertise-driven (DEIA) TF and our activities to date. We 1) developed and adopted the DEIA Learning Systems Framework to guide our approach; 2) defined a set of recommendations across four focus areas (Institutional; Programmatic; Community-Centered; and Social, Cultural, Environmental); and 3) designed and disseminated a survey to capture the CTSA Program's baseline demographic, community, infrastructural, and leadership diversity. The CTSA Consortium also elevated the TF to a standing Committee to extend our understanding, development, and implementation of DEIA approaches to translational and clinical science. These initial steps provide a foundation for collectively fostering environment that support DEIA across the research continuum.

Big splashes & ripple effects: a narrative review of the short- & long-term impact of publications supported by an NIH CTSA pediatrics program

BACKGROUND AND OBJECTIVE

This review examines a promising new framework for analyzing outputs of pediatric research in the context of translational advancement. We demonstrate a method for evaluating the impact of an NIH Clinical and Translational Science Award's (CTSA) Pediatrics Program through publications that have emerged from supported research. The Georgia CTSA Pediatrics Program provides training, funding, and infrastructure to ensure that researchers have the resources to advance pediatric health. Internal evaluations found that research supported by this program is exceptionally impactful within the academic community and commands high interest within the lay community. Therefore, we examined the impact of this research in both traditional academic and broader community spheres using bibliometrics-the study of supported publications. Bibliometrics describe a pivotal stage in the translational process of bringing scientific discoveries to clinical/community use and include both academic citations and 'altmetric' or non-academic attention. These complementary approaches combine to shed light on the short- and long-term impact of the research on segments of the translational pipeline, including academic literature, community discourse, technological advancement, and public health policy.

METHODS

The authors identified a portfolio of 250 articles supported by the Georgia CTSA Pediatrics Program from 2007-2020. We utilized various bibliometrics to analyze both short-term attention, or 'splash' made by articles, and long-term influence, or 'ripples' made across both academic and public spheres.

KEY CONTENT AND FINDINGS

The short-term splash of the portfolio was indicated through publication in high-impact factor journals, peer faculty recommendations, and Mendeley readership, as well as by early altmetric attention in news stories, blogs, and Twitter posts. The portfolio's long-term ripples were demonstrated by high absolute and relative rates of academic citation and by downstream altmetric influence in public-facing documents, including Wikipedia articles, patent applications, and policy documents.

CONCLUSIONS

This article reviews a useful bibliometric methodology for illustrating the waves of impact made by pediatric research. Whereas splash provides a picture of early interest in a publication, a preliminary indicator of eventual utility and impact, ripples provide a measure of the cumulative influence of an article over time. Both reflect opportunities for a line of research to advance along the translational spectrum.

Scale-up of the Accrual to Clinical Trials (ACT) network across the Clinical and Translational Science Award Consortium: a mixed-methods evaluation of the first 18 months

Introduction

The Clinical and Translational Science Award (CTSA) Program is a Consortium of nearly 60 academic medical research centers across the USA and a natural network for evaluating the spread and uptake of translational research innovation across the Consortium.

Methods

Dissemination of the Accrual to Clinical Trials (ACT) Network, a federated clinical informatics data network for population-based cohort discovery, began January 2018 across the Consortium. Diffusion of innovation theory guided dissemination design and evaluation. Mixed-methods assessed the spread and uptake across the Consortium through July 1, 2019 (n = 48 CTSAs). Methods included prospective time activity tracking (Kaplan-Meier curves), and survey and qualitative interviews.

Results

Within 18 months, nearly 80% of CTSAs had joined the data network and two-thirds of CTSAs achieving technical readiness had initiated launch to local clinical investigators. Over 10,000 ACT Network queries are projected for 2019; and by 2020, nearly all CTSAs will have joined the network. Median time-from-technical-readiness-to-local-launch was 154 days (interquartile range: 87-225 days]. Quality improvement processes reduced time-to-launch by 35.2% (64 days, p = 0.0036). Lessons learned include: (1) conceptualize dissemination as two-stage adoption demonstrating value for both CTSA hub service providers and clinical investigators; (2) include institutional trial into dissemination strategies so CTSA hubs can refine internal workflows and gather local user feedback endorsement; (3) embrace designing-for-dissemination during technology development; and (4) sustain adaptive dissemination and customer relationship management to keep CTSA hubs and users engaged.

Conclusions

Scale-up and spread of the ACT Network provides lessons learned for others disseminating innovation across the CTSA Consortium. The Network is primed for embedded implementation research.

Effect of a Clinical and Translational Science Award institute on grant funding in a major research university

INTRODUCTION

Previous studies have examined the impact of Clinical and Translational Science Awards programs on other outcomes, but not on grant seeking. The authors examined the effects on grant seeking of the Michigan Institute for Clinical & Health Research (MICHR), a Clinical and Translational Science Awards institute at the University of Michigan.

METHODS

We assessed over 63,000 grant proposals submitted at the University of Michigan in the years 2002-2012 using data from the university and MICHR's Tracking Metrics and Reporting System. We used a retrospective, observational study of the dynamics of grant-seeking success and award funding. Heckman selection models were run to assess MICHR's relationship with a proposal's success (selection), and subsequently the award's size (outcome). Models were run for all proposals and for clinical and translational research (CTR) proposals alone. Other covariates included proposal classification, type of grant award, academic unit, and year.

RESULTS

MICHR had a positive and statistically significant relationship with success for both proposal types. For all grants, MICHR was associated with a 29.6% increase in award size. For CTR grants, MICHR had a statistically nonsignificant relationship with award size.

CONCLUSIONS

MICHR's infrastructure, created to enable and enhance CTR, has also created positive spillovers for a broader spectrum of research and grant seeking.

Social network analysis of biomedical research collaboration networks in a CTSA institution

BACKGROUND

The popularity of social networks has triggered a number of research efforts on network analyses of research collaborations in the Clinical and Translational Science Award (CTSA) community. Those studies mainly focus on the general understanding of collaboration networks by measuring common network metrics. More fundamental questions about collaborations still remain unanswered such as recognizing "influential" nodes and identifying potential new collaborations that are most rewarding.

METHODS

We analyzed biomedical research collaboration networks (RCNs) constructed from a dataset of research grants collected at a CTSA institution (i.e., University of Arkansas for Medical Sciences (UAMS)) in a comprehensive and systematic manner. First, our analysis covers the full spectrum of a RCN study: from network modeling to network characteristics measurement, from key nodes recognition to potential links (collaborations) suggestion. Second, our analysis employs non-conventional model and techniques including a weighted network model for representing collaboration strength, rank aggregation for detecting important nodes, and Random Walk with Restart (RWR) for suggesting new research collaborations.

RESULTS

By applying our models and techniques to RCNs at UAMS prior to and after the CTSA, we have gained valuable insights that not only reveal the temporal evolution of the network dynamics but also assess the effectiveness of the CTSA and its impact on a research institution. We find that collaboration networks at UAMS are not scale-free but small-world. Quantitative measures have been obtained to evident that the RCNs at UAMS are moving towards favoring multidisciplinary research. Moreover, our link prediction model creates the basis of collaboration recommendations with an impressive accuracy (AUC: 0.990, MAP@3: 1.48 and MAP@5: 1.522). Last but not least, an open-source visual analytical tool for RCNs is being developed and released through Github.

CONCLUSIONS

Through this study, we have developed a set of techniques and tools for analyzing research collaboration networks and conducted a comprehensive case study focusing on a CTSA institution. Our findings demonstrate the promising future of these techniques and tools in understanding the generative mechanisms of research collaborations and helping identify beneficial collaborations to members in the research community.