Community advisory boards (CABs) are an established approach to ensuring research reflects community priorities. This paper examines two CABs that are part of the HEALing Communities Study which aims to reduce overdose mortality. This analysis aimed to understand CAB members’ expectations, experiences, and perspectives on CAB structure, communication, facilitation, and effectiveness during the first year of an almost fully remote CAB implementation. Current literature exploring these perspectives is limited.

We collected qualitative and survey data simultaneously from members (n = 53) of two sites’ CABs in the first 9 months of CAB development. The survey assessed trust, communication, and relations; we also conducted 32 semi-structured interviews. We analyzed the survey results descriptively. The qualitative data were analyzed using a deductive codebook based on the RE-AIM PRISM framework. Themes were drawn from the combined qualitative data and triangulated with survey results to further enrich the findings.

CAB members expressed strong commitment to overall study goals and valued the representation of occupational sectors. The qualitative data described a dissonance between CAB members’ commitment to the mission and unmet expectations for influencing the study within an advisory role. Survey results indicated lower satisfaction with the research teams’ ability to create a mutually beneficial process, clear communication, and sharing of power.

Building a CAB on a remote platform, within a study utilizing a community engagement strategy, still presents challenges to fully realizing the potential of a CAB. These findings can inform more effective operationalizing of community-engaged research through enhanced CAB engagement.

Purposeful training and ongoing career support are necessary to meet the evolving and expanding roles of clinical research professionals (CRP). To address the training and employment needs of clinical research coordinators (CRCs), one of the largest sectors of the CRP workforce, we designed, developed, and implemented an online career navigation system, eMPACTTM (eMpowering Purposeful Advancement of Careers and Training).

A design-based research method was employed as an overarching approach that frames iterative design, development, and implementation of educational interventions. The five major phases of this project – conceptualization, task analysis for measurement development, algorithms development, algorithms validation, and system evaluation – presented specific goals and relevant methods.

The results reported how the eMPACTTM system was conceptualized, developed, and validated. The system allowed CRCs to navigate tailored training and job opportunities by completing their task competencies and career goals. The data sets could, in turn, support employees’ and training coordinators’ informed decisions about organizational training needs and recruitment. The early dissemination results showed steady growth in registered CRCs and diversity in users’ ethnicity and job levels.

The eMPACTTM service showed the possibility of supporting CRCs’ individual career advancement and organizational workforce enhancement and diversity. Long-term research is needed to evaluate its impact on CRC workforce development, explore key factors influencing workforce sustainability, and expand eMPACTTM service to other CRP sectors.

Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher’s ability to gain confidence in interpreting and implementing Bayesian analyses.

We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research.

The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems.

Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

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.

This study aimed to map the maturity of precision oncology as an example of a Learning Health System by understanding the current state of practice, tools and informatics, and barriers and facilitators of maturity.

We conducted semi-structured interviews with 34 professionals (e.g., clinicians, pathologists, and program managers) involved in Molecular Tumor Boards (MTBs). Interviewees were recruited through outreach at 3 large academic medical centers (AMCs) (n = 16) and a Next Generation Sequencing (NGS) company (n = 18). Interviewees were asked about their roles and relationships with MTBs, processes and tools used, and institutional practices. The interviews were then coded and analyzed to understand the variation in maturity across the evolving field of precision oncology.

The findings provide insight into the present level of maturity in the precision oncology field, including the state of tooling and informatics within the same domain, the effects of the critical environment on overall maturity, and prospective approaches to enhance maturity of the field. We found that maturity is relatively low, but continuing to evolve, across these dimensions due to the resource-intensive and complex sociotechnical infrastructure required to advance maturity of the field and to fully close learning loops.

Our findings advance the field by defining and contextualizing the current state of maturity and potential future strategies for advancing precision oncology, providing a framework to examine how learning health systems mature, and furthering the development of maturity models with new evidence.

Despite the central importance of cross-disciplinary collaboration in the Clinical and Translational Science Award (CTSA) network and the implementation of various programs designed to enhance collaboration, rigorous evidence for the efficacy of these approaches is lacking. We conducted a novel randomized controlled trial (RCT; ClinicalTrials.gov identifier: NCT05395286) of a promising approach to enhance collaboration readiness and behavior among 95 early career scholars from throughout the CTSA network.

Participants were randomly assigned (within two cohorts) to participate in an Innovation Lab, a week-long immersive collaboration experience, or to a treatment-as-usual control group. Primary outcomes were change in metrics of self-reported collaboration readiness (through 12-month follow-up) and objective collaboration network size from bibliometrics (through 21 months); secondary outcomes included self-reported number of grants submitted and, among Innovation Lab participants only, reactions to the Lab experience (through 12 months).

Short-term reactions from Innovation Lab participants were quite positive, and controlled evidence for a beneficial impact of Innovation Labs over the control condition was observed in the self-reported number of grant proposals in the intent-to-treat sample. Primary measures of collaboration readiness were near ceiling in both groups, limiting the ability to detect enhancement. Collaboration network size increased over time to a comparable degree in both groups.

The findings highlight the need for systematic intervention development research to identify efficacious strategies that can be implemented throughout the CTSA network to better support the goal of enhanced cross-disciplinary collaboration.

The slow adoption of evidence-based interventions reflects gaps in effective dissemination of research evidence. Existing studies examining designing for dissemination (D4D), a process that ensures interventions and implementation strategies consider adopters’ contexts, have focused primarily on researchers, with limited perspectives of practitioners. To address these gaps, this study examined D4D practice among public health and clinical practitioners in the USA.

We conducted a cross-sectional study among public health and primary care practitioners in April to June 2022 (analyzed in July 2022 to December 2022). Both groups were recruited through national-level rosters. The survey was informed by previous D4D studies and pretested using cognitive interviewing.

Among 577 respondents, 45% were public health and 55% primary care practitioners, with an overall survey response rate of 5.5%. The most commonly ranked sources of research evidence were email announcements for public health practitioners (43.7%) and reading academic journals for clinical practitioners (37.9%). Practitioners used research findings to promote health equity (67%) and evaluate programs/services (66%). A higher proportion of clinical compared to public health practitioners strongly agreed/agreed that within their work setting they had adequate financial resources (36% vs. 23%, p < 0.001) and adequate staffing (36% vs. 24%, p = 0.001) to implement research findings. Only 20% of all practitioners reported having a designated individual or team responsible for finding and disseminating research evidence.

Addressing both individual and modifiable barriers, including organizational capacity to access and use research evidence, may better align the efforts of researchers with priorities and resources of practitioners.

Social Network Analysis is a method of analyzing coauthorship networks or relationships through graph theory. Institutional Development Award (IDeA) Networks for Clinical and Translational Research (IDeA-CTR) was designed to expand the capability for clinical and translational research to enhance National Institutes of Health funding.

All publications from a cohort of clinical and translational scientists in Oklahoma were collected through a PubMed search for 2014 through 2021 in October 2022. For this study’s bibliometric portion, we pulled the citations from iCite in November of 2022.

There were 2,391 articles published in 1,019 journals. The number of papers published by year increased from 56 in 2014 to 448 in 2021. The network had an average of 6.4 authors per paper, with this increasing by year from 5.3 in 2014 to 6.9 in 2021. The average journal impact factor for the overall network was 7.19, with a range from 0.08 to 202.73. The Oklahoma Shared Clinical and Translational Resources (OSCTR) network is a small world network with relatively weak ties.

This study provides an overview of coauthorship in an IDeA-CTR collaboration. We show the growth and structure of coauthorship in OSCTR, highlighting the importance of understanding and fostering collaboration within research networks.

The clinical and translational research workforce involved in social and behavioral research (SBR) needs to keep pace with clinical research guidance and regulations. Updated information and a new module on community and stakeholder engagement were added to an existing SBR training course. This article presents evaluation findings of the updated course for the Social and Behavioral Workforce.

Participants working across one university were recruited. Course completers were sent an online survey to evaluate the training. Some participants were invited to join in a focus group to discuss the application of the training to their work. We performed descriptive statistics and conducted a qualitative analysis on focus group data.

There were 99 participants from diverse backgrounds who completed the survey. Most reported the training was relevant to their work or that of the study teams they worked with. Almost half (46%) indicated they would work differently after participating. Respondents with community or stakeholder engaged research experience vs. those without were more likely to report that the new module was relevant to study teams they worked with (t = 5.61, p = 0.001), and that they would work differently following the training (t = 2.63, p = 0.01). Open-ended survey responses (n = 99) and focus group (n = 12) data showed how participants felt their work would be affected by the training.

The updated course was rated highly, particularly by those whose work was related to the new course content. This course provides an up-to-date resource for the training and development for the Social and Behavioral Workforce.

To compare how clinical researchers generate data-driven hypotheses with a visual interactive analytic tool (VIADS, a visual interactive analysis tool for filtering and summarizing large datasets coded with hierarchical terminologies) or other tools.

We recruited clinical researchers and separated them into “experienced” and “inexperienced” groups. Participants were randomly assigned to a VIADS or control group within the groups. Each participant conducted a remote 2-hour study session for hypothesis generation with the same study facilitator on the same datasets by following a think-aloud protocol. Screen activities and audio were recorded, transcribed, coded, and analyzed. Hypotheses were evaluated by seven experts on their validity, significance, and feasibility. We conducted multilevel random effect modeling for statistical tests.

Eighteen participants generated 227 hypotheses, of which 147 (65%) were valid. The VIADS and control groups generated a similar number of hypotheses. The VIADS group took a significantly shorter time to generate one hypothesis (e.g., among inexperienced clinical researchers, 258 s versus 379 s, p = 0.046, power = 0.437, ICC = 0.15). The VIADS group received significantly lower ratings than the control group on feasibility and the combination rating of validity, significance, and feasibility.

The role of VIADS in hypothesis generation seems inconclusive. The VIADS group took a significantly shorter time to generate each hypothesis. However, the combined validity, significance, and feasibility ratings of their hypotheses were significantly lower. Further characterization of hypotheses, including specifics on how they might be improved, could guide future tool development.

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.