Researchers generally do an excellent job tracking the scientific impacts of their scholarship in ways that are relevant for academia (e.g., publications, grants) but too often neglect to focus on broader impacts on population health and equity. The National Cancer Institute’s Implementation Science Centers in Cancer Control (ISC3) includes 7 P50 Centers that are interested in broad measures of impact. We provide an overview of the approach underway within the ISC3 consortium to identify health and social impacts.

ISC3 adapted and applied the Translational Science Benefits Model (TSBM) to identify the impact on the discipline of D&I science and to consider dissemination and implementation (D&I) impacts in the four original TSBM domains: (1) clinical; (2) community; (3) economic; and (4) policy. To collect data from all Centers, we: (1) co-developed a set of detailed impact indicators with examples; (2) created a data collection template; and (3) summarized the impact data from each center.

Based on data from 48 ISC3 pilot studies, cores, or consortium activities, we identified 84 distinct benefits. The most common impacts were shown for implementation science (43%), community (28%), and clinical (18%). Frequent audiences included primary care providers, public health practitioners, and community partners. ISC3 members highlighted the need for product feedback, and storytelling assistance to advance impact.

The ISC3 consortium is using a participatory approach to successfully apply the TSBM, thus seeking to maximize the real-world impacts of D&I science. The D&I field needs to prioritize ways to more fully document and communicate societal impacts.

Demonstrating the impact of implementation science presents a new frontier for the field, and operationalizing downstream impact is challenging. The Translational Science Benefits Model (TSBM) offers a new approach for assessing and demonstrating research impact. Here we describe integration of the TSBM into a mentored training network.

Washington University’s Clinical and Translational Science Awards TSBM team collaborated with a National Institute of Mental Health-supported training program, the Implementation Research Institute (IRI), a 2-year training institute in mental health implementation science. This partnership included three phases: (1) introductory workshop on research impact, (2) workshop on demonstrating impact, and (3) sessions to guide dissemination, including interactive tools and consultation with the TSBM research team. Fifteen IRI alumni were invited to participate in the pilot; six responded agreeing to participate in the training, develop TSBM case studies, and provide feedback about their experiences. Participants applied the tools and gave feedback on design, usability, and content. We present their case studies and describe how the IRI used the results to incorporate TSBM into future trainings.

The case studies identified 40 benefits spanning all four TSBM domains, including 21 community, 11 policy, five economic, and three clinical benefits. Participants reported that TSBM training helped them develop a framework for talking about impact. Selecting benefits was challenging for early-stage projects, suggesting the importance of early training.

The case studies showcased the institute’s impact and the fellows’ work and informed refinement of tools and methods for incorporating TSBM into future IRI training.

There are two main schools of thought about statistical inference: frequentist and Bayesian. The frequentist approach relies solely on available data for predictions, while the Bayesian approach incorporates both data and prior knowledge about the event of interest. Bayesian methods were developed hundreds of years ago; however, they were rarely used due to computational challenges and conflicts between the two schools of thought. Recent advances in computational capabilities and a shift toward leveraging prior knowledge for inferences have led to increased use of Bayesian methods.

Many biostatisticians with expertise in frequentist approaches lack the skills to apply Bayesian techniques. To address this gap, four faculty experts in Bayesian modeling at the University of Michigan developed a practical, customized workshop series. The training, tailored to accommodate the schedules of full-time staff, focused on immersive, project-based learning rather than traditional lecture-based methods. Surveys were conducted to assess the impact of the program.

All 20 participants completed the program and when surveyed reported an increased understanding of Bayesian theory and greater confidence in using these techniques. Capstone projects demonstrated participants’ ability to apply Bayesian methodology. The workshop not only enhanced the participants’ skills but also positioned them to readily apply Bayesian techniques in their work.

Accommodating the schedules of full-time biostatistical staff enabled full participation. The immersive project-based learning approach resulted in building skills and increasing confidence among staff statisticians who were unfamiliar with Bayesian methods and their practical applications.

The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients.

Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub’s EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis.

One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%–98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation.

Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

The institutions (i.e., hubs) making up the National Institutes of Health (NIH)-funded network of Clinical and Translational Science Awards (CTSAs) share a mission to turn observations into interventions to improve public health. Recently, the focus of the CTSAs has turned increasingly from translational research (TR) to translational science (TS). The current NIH Funding Opportunity Announcement (PAR-21-293) for CTSAs stipulates that pilot studies funded through the CTSAs must be “focused on understanding a scientific or operational principle underlying a step of the translational process with the goal of developing generalizable solutions to accelerate translational research.” This new directive places Pilot Program administrators in the position of arbiters with the task of distinguishing between TR and TS projects. The purpose of this study was to explore the utility of a set of TS principles set forth by NCATS for distinguishing between TR and TS.

Twelve CTSA hubs collaborated to generate a list of Translational Science Principles questions. Twenty-nine Pilot Program administrators used these questions to evaluate 26 CTSA-funded pilot studies.

Factor analysis yielded three factors: Generalizability/Efficiency, Disruptive Innovation, and Team Science. The Generalizability/Efficiency factor explained the largest amount of variance in the questions and was significantly able to distinguish between projects that were verified as TS or TR (t = 6.92, p < .001) by an expert panel.

The seven questions in this factor may be useful for informing deliberations regarding whether a study addresses a question that aligns with NCATS’ vision of TS.

A recent literature review revealed no studies that explored teams that used an explicit theoretical framework for multiteam systems in academic settings, such as the increasingly important multi-institutional cross-disciplinary translational team (MCTT) form. We conducted an exploratory 30-interview grounded theory study over two rounds to analyze participants’ experiences from three universities who assembled an MCTT in order to pursue a complex grant proposal related to research on post-acute sequelae of COVID-19, also called “long COVID.” This article considers activities beginning with preliminary discussions among principal investigators through grant writing and submission, and completion of reviews by the National Center for Advancing Translational Sciences, which resulted in the proposal not being scored.

There were two stages to this interview study with MCTT members: pre-submission, and post-decision. Round one focused on the process of developing structures to collaborate on proposal writing and assembly, whereas round two focused on evaluation of the complete process. A total of 15 participants agreed to be interviewed in each round.

The first round of interviews was conducted prior to submission and explored issues during proposal writing, including (1) importance of the topic; (2) meaning and perception of “team” within the MCTT context; and (3) leadership at different levels of the team. The second round explored best practices-related issues including (1) leadership and design; (2) specific proposal assembly tasks; (3) communication; and (4) critical events.

We conclude with suggestions for developing best practices for assembling MCTTs involving multi-institutional teams.

Linking data is a critical feature of precision medicine initiatives that involves integrating information from multiple sources to improve researchers’ and clinicians’ ability to deliver care. We have limited understanding of how individuals perceive linking data as it relates to precision medicine. The aim of this study was to identify how sociodemographics, comorbidities, and beliefs about precision medicine influence two outcomes related to linking data: beliefs about linking data and concerns about linking data among men.

We recruited 124 adult men from primary care practices at a large clinical research university to complete a cross-sectional survey that included questions about sociodemographic characteristics, comorbidities, beliefs, benefits, and limitations of precision medicine, and two outcomes of interest: beliefs about the value of linking data and concerns about linking data. Descriptive statistics, bivariate associations, and multivariable regression were conducted.

Participants had positive beliefs about linking data for precision medicine (M = 4.05/5) and average concern about linking data (M = 2.1/5). Final multivariable models revealed that higher levels of loneliness are associated with more positive beliefs about linking data (β = 0.41, p = 0.027). Races other than African American (β = –0.64, p = 0.009) and those with lower perceived limitations of precision medicine were less likely to be concerned about linking data (β = –0.75, p = 0.0006).

Our results advance the literature about perceptions of linking data for use in clinical and research studies among men. Better understanding of factors associated with more positive perceptions of data linkages could help improve how researchers recruit and engage participants.

A Mass Casualty Incident response (MCI) full scale exercise (FSEx) assures MCI first responder (FR) competencies. Simulation and serious gaming platforms (Simulation) have been considered to achieve and maintain FR competencies. The translational science (TS) T0 question was asked: how can FRs achieve similar MCI competencies as a FSEx through the use of MCI simulation exercises?

T1 stage (Scoping Review): PRISMA-ScR was conducted to develop statements for the T2 stage modified Delphi (mD) study. 1320 reference titles and abstracts were reviewed with 215 full articles progressing for full review leading to 97 undergoing data extraction.

T2 stage (mD study): Selected experts were presented with 27 statements derived from T1 data with instruction to rank each statement on a 7-point linear numeric scale, where 1 = disagree and 7 = agree. Consensus amongst experts was defined as a standard deviation ≤ 1.0.

After 3 mD rounds, 19 statements attained consensus and 8 did not attain consensus.

MCI simulation exercises can be developed to achieve similar competencies as FSEx by incorporating the 19 statements that attained consensus through the TS stages of a scoping review (T1) and mD study (T2), and continuing to T3 implementation, and then T4 evaluation stages.

Health equity research spans various disciplines, crossing formal organizational and departmental barriers and forming invisible communities. This study aimed to map the nomination network of scholars at the University of Rochester Medical Center who were active in racial and ethnic health equity research, education, and social/administrative activities, to identify the predictors of peer recognition.

We conducted a snowball survey of faculty members with experience and/or interest in racial and ethnic health equity, nominating peers with relevant expertise.

Data from a total of 121 individuals (64% doing research on extent and outcomes of racial/ethnic disparities and racism, 48% research on interventions, 55% education, and 50% social/administrative activities) were gathered in six rounds of survey. The overlap between expertise categories was small with coincidence observed between education and social/administrative activities (kappa: 0.27; p < 0.001). Respondents were more likely to nominate someone if both were involved in research (OR: 3.1), if both were involved in education (OR: 1.7), and if both were affiliated with the same department (OR: 3.7). Being involved in health equity research significantly predicted the centrality of an individual in the nomination network, and the most central actors were involved in multiple expertise categories.

Compared with equity researchers, those involved in racial equity social/administrative activities were less likely to be recognized by peers as equity experts.