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Academic health sciences libraries (“libraries”) offer services that span the entire research lifecycle, positioning them as natural partners in advancing clinical and translational science. Many libraries enjoy active and productive collaborations with Clinical and Translational Science Award (CTSA) Program hubs and other translational initiatives like the IDeA Clinical & Translational Research Network. This article explores areas of potential partnership between libraries and Translational Science Hubs (TSH), highlighting areas where libraries can support the CTSA Program’s five functional areas outlined in the Notice of Funding Opportunity. It serves as a primer for TSH and libraries to explore potential collaborations, demonstrating how libraries can connect researchers to services and resources that support the information needs of TSH.

Research articles in the clinical and translational science literature commonly use quantitative data to inform evaluation of interventions, learn about the etiology of disease, or develop methods for diagnostic testing or risk prediction of future events. The peer review process must evaluate the methodology used therein, including use of quantitative statistical methods. In this manuscript, we provide guidance for peer reviewers tasked with assessing quantitative methodology, intended to complement guidelines and recommendations that exist for manuscript authors. We describe components of clinical and translational science research manuscripts that require assessment including study design and hypothesis evaluation, sampling and data acquisition, interventions (for studies that include an intervention), measurement of data, statistical analysis methods, presentation of the study results, and interpretation of the study results. For each component, we describe what reviewers should look for and assess; how reviewers should provide helpful comments for fixable errors or omissions; and how reviewers should communicate uncorrectable and irreparable errors. We then discuss the critical concepts of transparency and acceptance/revision guidelines when communicating with responsible journal editors.

This in-depth analysis illuminates a translational journey of a community-university research collaboration that examined health disparities among incarcerated pregnant women and spanned the translational spectrum, with the initial collaboration in 2011 paving the way for consequent research grants, publications, practices, programs, and legislation passed years later. The case study utilized data from interviews with research stakeholders, institutional and governmental sources, peer-reviewed publications, and news stories. Identified research and translational challenges included cultural differences between research and prison system; the prison system’s lack of transparency; politics of using and translating research to policy change; and issues of capacity, power, privilege, and opportunity when doing community-engaged research/science. Among the facilitators of translation were the Clinical and Translational Science Award and institutional support; engagement of key stakeholders and influencers; authentic collaboration and team science; researchers as translation catalysts; pragmatic scientific approach; and policies and legislative activities. The research contributed to a variety of community and public health, policy/legislative, clinical/medical, and economic benefits. The case study findings enhance our understanding of translational science principles and processes leading to improved wellbeing and serve as a call for advancing the research agenda addressing health disparities related to criminal and social justice issues.

BackgroundWe employed Innovation Corps (I-Corps™) methods to adaptation of a mobile health (mHealth) short-message-system (SMS) -based interactive obesity treatment approach (iOTA) for adults with severe mentall illness receiving care in community settings.MethodsWe hypothesized “jobs to be done” in three broad stakeholder groups: “decision makers” (DM = state and community clinic administrators), “clinician consumers” (CC = case managers, peer supports, nurses, prescribers) and “service consumers” (SC = patients, peers and family members). Semistructured interviews (N = 29) were recorded and transcribed ver batim and coded based on pragmatic-variant grounded theory methods.ResultsFour themes emerged across groups: education, inertia, resources and ownership. Sub-themes in education and ownership differed between DM and CC groups on implementation ownership, intersecting with professional development, suggesting the importance of training and supervision in scalability. Sub-themes in resources and intertia differed between CC and SC groups, suggesting illness severity and access to healthy food as major barriers to engagement, whereas the SC group identified the need for enhanced emotional support, in addition to pragmatic skills like menu planning and cooking, to promote health behavior change. Although SMS was percieved as a viable education and support tool, CC and DM groups had limited familiarity with use in clinical care delivery.ConclusionsBased on customer discovery, the characteristics of a minimum viable iOTA for implementation, scalability and sustainability include population- and context-specific adaptations to treatment content, interventionist training and delivery mechanism. Successful implementation of an SMS-based intervention will likely require micro-adaptations to fit specific clinical settings.

Low-accruing clinical trials delay translation of research breakthroughs into the clinic, expose participants to risk without providing meaningful clinical insight, increase the cost of therapies, and waste limited resources. By tracking patient accrual, Clinical and Translational Science Awards hubs can identify at-risk studies and provide them the support needed to reach recruitment goals and maintain financial solvency. However, tracking accrual has proved challenging because relevant patient- and protocol-level data often reside in siloed systems. To address this fragmentation, in September 2020 the South Carolina Clinical and Translational Research Institute, with an academic home at the Medical University of South Carolina, implemented a clinical trial management system (CTMS), with its access to patient-level data, and incorporated it into its Research Integrated Network of Systems (RINS), which links study-level data across disparate systems relevant to clinical research. Within the first year of CTMS implementation, 324 protocols were funneled through CTMS/RINS, with more than 2600 participants enrolled. Integrated data from CTMS/RINS have enabled near-real-time assessment of patient accrual and accelerated reimbursement from industry sponsors. For institutions with bioinformatics or programming capacity, the CTMS/RINS integration provides a powerful model for tracking and improving clinical trial efficiency, compliance, and cost-effectiveness.

AbstractIntroduction:High impact biomedical research is increasingly conducted by large, transdisciplinary, multisite teams in an increasingly collaborative environment. Thriving in this environment requires robust teamwork skills, which are not acquired automatically in the course of traditional scientific education. Team science skills training does exist, but most is directed at clinical care teams, not research teams, and little is focused on the specific training needs of early-career investigators, whose early team leadership experiences may shape their career trajectories positively or negatively. Our research indicated a need for team science training designed specifically for early-career investigators.Methods:To address this need, we designed and delivered a 2-day workshop focused on teaching team science skills to early-career investigators. We operationalized team science competencies, sought the advice of team science experts, and performed a needs assessment composed of a survey and a qualitative study. Through these multiple approaches, we identified and grouped training priorities into three broad training areas and developed four robust, hands-on workshop sessions.Results:Attendees comprised 30 pre- and post-doc fellows (TL1) and early-career faculty (KL2 and K12). We assessed impact with a pre- and post-workshop survey adapted from the Team Skills Scale. Results from the pre- and post-test Wilcoxon signed-rank analysis (n = 25) showed statistically significant improvement in team science skills and confidence. Open-ended responses indicated that the workshop focus was appropriate and well targeted to the trainees’ needs.Conclusions:Although team science education is still very much in its infancy, these results suggest that training targeted to early-career investigators improves team skills and may foster improved collaboration.

The COVID-19 pandemic has required many clinical and translational scientists and staff to work remotely to prevent the spread of the virus. To understand the impact on research programs, we assessed barriers to remote work and strategies implemented to support virtual engagement and productivity. A mixed-methods RedCap survey querying the remote work experience was emailed to Colorado Clinical and Translational Sciences Institute (CCTSI) scientists and staff in April 2020. Descriptive analyses, Fisher’s Exact tests, and content analysis were conducted. Respondents (n = 322) were primarily female (n = 240; 75%), 21–73 years old (mean = 42 years) with a PhD (n = 139; 44%) or MD (n = 56; 55%). Prior to COVID-19, 77% (n = 246) never or rarely (0–1 day a week) worked remotely. Remote work somewhat or greatly interfered with 76% (n = 244) of researchers’ programs and 71% (n = 231) reported slowing or stopping their research. Common barriers included missing interactions with colleagues (n = 198; 62%) and the absence of routines (n = 137; 43%). Strategies included videoconferencing (n = 283; 88%), altering timelines and expectations (n = 180; 56%). Scientists and staff experienced interference with their research when they shifted to remote work, causing many to slow or stop research programs. Methods to enhance communication and relationships, support productivity, and collectively cope during remote work are available.

Abstract Introduction: Statistical literacy is essential in clinical and translational science (CTS). Statistical competencies have been published to guide coursework design and selection for graduate students in CTS. Here, we describe common elements of graduate curricula for CTS and identify gaps in the statistical competencies. Methods: We surveyed statistics educators using e-mail solicitation sent through four professional organizations. Respondents rated the degree to which 24 educational statistical competencies were included in required and elective coursework in doctoral-level and master’s-level programs for CTS learners. We report competency results from institutions with Clinical and Translational Science Awards (CTSAs), reflecting institutions that have invested in CTS training. Results: There were 24 CTSA-funded respondents representing 13 doctoral-level programs and 23 master’s-level programs. For doctoral-level programs, competencies covered extensively in required coursework for all doctoral-level programs were basic principles of probability and hypothesis testing, understanding the implications of selecting appropriate statistical methods, and computing appropriate descriptive statistics. The only competency extensively covered in required coursework for all master’s-level programs was understanding the implications of selecting appropriate statistical methods. The least covered competencies included understanding the purpose of meta-analysis and the uses of early stopping rules in clinical trials. Competencies considered to be less fundamental and more specialized tended to be covered less frequently in graduate courses. Conclusion: While graduate courses in CTS tend to cover many statistical fundamentals, learning gaps exist, particularly for more specialized competencies. Educational material to fill these gaps is necessary for learners pursuing these activities.

Commercializing biomedical discoveries is a challenging process for many reasons. However, Academic Medical Centers (AMC) that have teaching, patient care, research, and service engrained in their mission are well poised to host these discoveries. These academic discoveries can lead to improvement in patient health and economic development if supported to cross the “valley of death” through institutional assistance, by providing guidance, gap funding and product development expertise. Colorado has a vibrant local startup ecosystem, state support for commercialization and entrepreneurship as well as critical mass of product development expertise. University of Colorado Anschutz Medical Campus, as a major AMC, is an engine for growth for the region. This article discusses innovation efforts at the University of Colorado Anschutz Medical Campus as a case study, which is built around two major efforts: the CCTSI and CU Innovations. I-Corps at CCTSI and the SPARK|REACH program of CU Innovations have been instrumental in fostering innovation, commercialization, and entrepreneurship on the campus.

Abstract Introduction: Clinical and Translational Award (CTSA) programs are developing relevant training for researchers and community stakeholders participating in community-engaged research (CEnR). However, there is limited research exploring the ways community stakeholders and partners with key CEnR experiences can inform and shape training priorities for prospective CEnR scholars to build meaningful and equitable partnerships. Methods: This study conducted and analyzed online individual semi-structured in-depth interviews with community stakeholders (n = 13) engaged in CEnR to identify training priorities for graduate students and emerging scholars. Findings: Thematic analysis of 13 interview transcripts revealed four major training priorities for prospective scholars interested in engaging in CEnR: 1) researcher’s positionality, 2) equitable power sharing, 3) funding, and 4) ethics. Conclusion: Building equitable research partnerships was a central theme woven across all four training priorities. Further research should focus on examining the development, implementation, and evaluation of CEnR training in partnership with community stakeholders and partners with relevant CEnR experience. Adopting a collaborative approach to incorporate both community stakeholders and researchers’ priorities can align training competencies to better prepare scholars to engage in building research partnerships.

Abstract Introduction: The Clinical and Translational Science Awards (CTSA) Consortium, about 60 National Institutes of Health (NIH)-supported CTSA hubs at academic health care institutions nationwide, is charged with improving the clinical and translational research enterprise. Together with the NIH National Center for Advancing Translational Sciences (NCATS), the Consortium implemented Common Metrics and a shared performance improvement framework. Methods: Initial implementation across hubs was assessed using quantitative and qualitative methods over a 19-month period. The primary outcome was implementation of three Common Metrics and the performance improvement framework. Challenges and facilitators were elicited. Results: Among 59 hubs with data, all began implementing Common Metrics, but about one-third had completed all activities for three metrics within the study period. The vast majority of hubs computed metric results and undertook activities to understand performance. Differences in completion appeared in developing and carrying out performance improvement plans. Seven key factors affected progress: hub size and resources, hub prior experience with performance management, alignment of local context with needs of the Common Metrics implementation, hub authority in the local institutional structure, hub engagement (including CTSA Principal Investigator involvement), stakeholder engagement, and attending training and coaching. Conclusions: Implementing Common Metrics and performance improvement in a large network of research-focused organizations proved feasible but required substantial time and resources. Considerable heterogeneity across hubs in data systems, existing processes and personnel, organizational structures, and local priorities of home institutions created disparate experiences across hubs. Future metric-based performance management initiatives across heterogeneous local contexts should anticipate and account for these types of differences.

Clinical and translational science is vitally dependent on the nation’s underlying health-care policies and programs. In a reciprocal fashion, data generated by clinical and translational research can inform both health policy and health-care delivery. It is important, therefore, to rate health reform proposals comprehensively on a set of criteria that reflect the broad goals of reform, including the potential impact on clinical and translational science and medical education. I propose that the criteria include achieving universal coverage, reducing administrative costs, retaining one’s chosen primary care physician, encouraging care coordination, empowering physicians, freeing industry from choosing and administering health plans, providing choice of specialists and hospitals, providing patient education, preventing patient overuse of services, rationalizing resource allocation, encouraging competition, limiting government’s role, supporting medical education, training, and research, and freeing industry to make personnel decisions based on business criteria rather than the impact on health-care costs to the company. I discuss the rationale for each element and offer a rating of current proposals relative to a proposal previously made.

IntroductionEarly life exposures affect health and disease across the life course and potentially across multiple generations. The Clinical and Translational Research Institutes (CTSIs) offer an opportunity to utilize and link existing databases to conduct lifespan research.MethodsA survey with Lifespan Domain Taskforce expert input was created and distributed to lead lifespan researchers at each of the 64 CTSIs. The survey requested information regarding institutional databases related to early life exposure, child-maternal health, or lifespan research.ResultsOf 64 CTSI, 88% provided information on a total of 130 databases. Approximately 59% (n=76/130) had an associated biorepository. Longitudinal data were available for 72% (n=93/130) of reported databases. Many of the biorepositories (n=44/76; 68%) have standard operating procedures that can be shared with other researchers.ConclusionsThe majority of CTSI databases and biorepositories focusing on child-maternal health and lifespan research could be leveraged for lifespan research, increased generalizability and enhanced multi-institutional research in the United States.

Introduction It is increasingly essential for medical researchers to be literate in statistics, but the requisite degree of literacy is not the same for every statistical competency in translational research. Statistical competency can range from ‘fundamental’ (necessary for all) to ‘specialized’ (necessary for only some). In this study, we determine the degree to which each competency is fundamental or specialized. Methods We surveyed members of 4 professional organizations, targeting doctorally trained biostatisticians and epidemiologists who taught statistics to medical research learners in the past 5 years. Respondents rated 24 educational competencies on a 5-point Likert scale anchored by ‘fundamental’ and ‘specialized.’ Results There were 112 responses. Nineteen of 24 competencies were fundamental. The competencies considered most fundamental were assessing sources of bias and variation (95%), recognizing one’s own limits with regard to statistics (93%), identifying the strengths, and limitations of study designs (93%). The least endorsed items were meta-analysis (34%) and stopping rules (18%). Conclusion We have identified the statistical competencies needed by all medical researchers. These competencies should be considered when designing statistical curricula for medical researchers and should inform which topics are taught in graduate programs and evidence-based medicine courses where learners need to read and understand the medical research literature.