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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Early 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.

A 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.

Of 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.

The 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.

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.

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.’

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%).

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.