Inadequate recruitment and retention impede clinical trial goals. Emerging decentralized clinical trials (DCTs) leveraging digital health technologies (DHTs) for remote recruitment and data collection aim to address barriers to participation in traditional trials. The ACTIV-6 trial is a DCT using DHTs, but participants’ experiences of such trials remain largely unknown. This study explored participants’ perspectives of the ACTIV-6 DCT that tested outpatient COVID-19 therapeutics.

Participants in the ACTIV-6 study were recruited via email to share their day-to-day trial experiences during 1-hour virtual focus groups. Two human factors researchers guided group discussions through a semi-structured script that probed expectations and perceptions of study activities. Qualitative data analysis was conducted using a grounded theory approach with open coding to identify key themes.

Twenty-eight ACTIV-6 study participants aged 30+ years completed a virtual focus group including 1–4 participants each. Analysis yielded three major themes: perceptions of the DCT experience, study activity engagement, and trust. Participants perceived the use of remote DCT procedures supported by DHTs as an acceptable and efficient method of organizing and tracking study activities, communicating with study personnel, and managing study medications at home. Use of social media was effective in supporting geographically dispersed participant recruitment but also raised issues with trust and study legitimacy.

While participants in this qualitative study viewed the DCT-with-DHT approach as reasonably efficient and engaging, they also identified challenges to address. Understanding facilitators and barriers to DCT participation and DHT interaction can help improve future research design.

Many factors can impact survey completion rates, including survey length, sensitivity of the topics addressed, and clarity of wording. This study used cognitive interviews (CIs), a methodological tool that can aid in developing and refining elements for multi-faceted assessments, and previous survey response patterns to refine, streamline, and increase response rates of RADx-UP Common Data Elements (CDEs) for survey/questionnaire use.

Ten previously enrolled CO-CREATE study participants were interviewed between May–June 2023. Interviewees identified CDEs that were “confusing, uncomfortable, and/or not applicable,” along with their reasoning. Interview data were analyzed using a rapid qualitative analytic approach, resulting in a summary matrix categorized by language. For further contextualization, CDE response rates were calculated for the 9147 surveys administered during the CO-CREATE study (May 2021–March 2023) and compared against their survey position.

Of the 94 CDEs evaluated in the CIs, 20 (21.3%) were flagged by one or more interviewees. Nine (9.6%) English while fourteen (14.9%) Spanish CDEs were flagged by interviewees, with some overlap. Also, CDE response rates differed according to position in the survey, with lower response rates for questions positioned later in the survey. Following review by the research team and the RADx-UP program, 10 English and 15 Spanish were revised, and seven were removed in both languages in the final survey.

Our findings underscore the importance of integrating community member perspectives to enhance the relevance and clarity of assessment instruments, optimizing the impact of public health research among underrepresented populations.

Community-engaged partnerships (community/academia/government) can play a role in developing effective protocols that address public health crises. Systemic racism, prioritization of money over humanity, and the repression of the local democratic processes through the State of Michigan Emergency Manager Law (Order of Act 439) all played a role in the Flint Water Crisis. Despite decades of collaboration between Flint-based community organizations and academic institutions, ways to navigate such crises and conduct relevant research were ineffective.

The Michigan Institute for Clinical and Health Research Community Engagement program at the University of Michigan and Flint’s Community Based Organization Partners co-developed the Research Readiness and Partnership Protocol (R2P2) to provide community-engaged recommendations that inform a rapid research response to public health emergencies. The R2P2 Workgroup conducted an extensive literature review and key interviews to inform protocol development.

This manuscript provides an overview of the Workgroup’s methods, key interview findings, and the main principles identified. Detailed recommendations and key elements to address prior to and during a crisis will be presented including methods for: establishing and maintaining trust, ensuring transparency, supporting clear communication, establishing a “front door” to academic institutions including a means to “sound the alarm,” addressing academic incentives, achieving equitable resource sharing, and addressing systemic racism.

This manuscript of community perspectives provides essential elements to develop meaningful community-academic research partnerships to address public health crises impacting communities, particularly communities of color. Furthermore, this work highlights an opportunity for greater acknowledgment and utilization of community-based participatory research (CBPR) by academic institutions.

Lack of reliable, affordable transportation is a common barrier to clinical research participation, potentially contributing to health disparities. Insufficient and/or nonexistent institutional policies on research-related transportation make it challenging for research teams to effectively overcome transportation barriers and promote research participation among people from disadvantaged backgrounds. This study’s goal was to review research-related transportation policies across clinical research-involved institutions and propose recommendations for what such policies should address to help promote research engagement among diverse, representative populations.

We surveyed 28 recruitment sites, members of the National Institutes of Health-funded Healthy Brain and Child Development Consortium, poised to recruit over 7000 families, and completed an online search for each site’s policies relevant to research-related transportation (i.e., transportation of participants or research staff travel to/from research activities). We identified, reviewed, and thematically described content of the relevant policies and developed summary recommendations for institutional guidance components.

We identified seven policies (from five sites) on research-related transportation; four provided guidance on research-related transportation services; two on reimbursement; and one on when research staff transports participants. The online search identified publicly available business travel policies for 22 sites. No policy addressed research staff travel specifically for “study business” or research personnel transporting children for research purposes.

Few institutions involved in clinical research have policies guiding research-related transportation. Such policies, if adopted, could help support research-related transportation and, thus, participation of individuals from disadvantaged backgrounds, increasing generalizability of research results and contributing toward reducing social and health disparities.

A promising approach to assessing research impact draws on the Translational Science Benefits Model (TSBM), an evaluation model that tracks the applied benefits of research in four domains: Clinical and Medical; Community and Public Health; Economic; and Policy and Legislative. However, standardized methods to verify TSBM benefit data, to aid in aggregating impact data within quantitative summaries, do not currently exist.

A panel of 11 topic experts participated in a modified Delphi process for establishing content and face validity of a set of criteria for verifying qualitative TSBM data. Two survey rounds were completed by panelists, with a moderated discussion in between rounds to discuss criteria not reaching consensus. Criteria with panel consensus at or above 70% in the survey rounds were confirmed as validated.

Criteria fell into 9 categories: Content Relevant, Project Related, Who, Reach, What, How, Novel, Documented Evidence, and When. The Delphi process yielded 197 total criteria across the 30 benefits characterized by the TSBM (range = 5–8 criteria per benefit).

The results of this Delphi process lay the foundation for developing a TSBM coding tool for evaluating and quantifying TSBM data. Standardizing this process will enable data aggregation, group analysis, and the comparison of research impact across contexts.

Clinical and Translational Science trainees are motivated to publish influential research. However, the extent to which this work gains influence with the public is largely unknown.

The authors identified over 30,000 publications that received KL2 or TL1 grant support through a Clinical and Translational Science Awards hub, from 2006 through January 2024. The Altmetric Explorer database was then used, to collect references in sources such as news articles, tweets, and blogs. We investigated bibliometric characteristics and content areas, provide illustrative examples of influence, and determine the characteristics most likely to gain public attention.

Articles were published in 3,923 journals with a mean Journal Impact Factor (JIF) of 5.78, a mean Relative Citation Ratio (RCR) score of 2.02, and were cited an average of 33.7 times, totaling 1,017,291 citations. Over 4,800 were referenced in policy and were mentioned in over 64K news articles, 7K blog posts, and 480K X (Twitter) posts. The mean Altmetric Attention Score was 28.9, with 18.5% having scores of 20 or higher. Nearly 30% were related to COVID-19, indicating close public attention to this important health topic. Regression analyses indicate that higher JIF, being published after 2020, receiving more Mendeley downloads, higher RCR scores, being cited by in policy, and fewer academic citations, were more likely to receive altmetric attention.

By demonstrating how supported research has influence beyond academia to become “Academic Influencers,” this study represents a significant advance in our ability to evaluate translational research impact.

Biostatisticians increasingly use large language models (LLMs) to enhance efficiency, yet practical guidance on responsible integration is limited. This study explores current LLM usage, challenges, and training needs to support biostatisticians.

A cross-sectional survey was conducted across three biostatistics units at two academic medical centers. The survey assessed LLM usage across three key professional activities: communication and leadership, clinical and domain knowledge, and quantitative expertise. Responses were analyzed using descriptive statistics, while free-text responses underwent thematic analysis.

Of 208 eligible biostatisticians (162 staff and 46 faculty), 69 (33.2%) responded. Among them, 44 (63.8%) reported using LLMs; of the 43 who answered the frequency question, 20 (46.5%) used them daily and 16 (37.2%) weekly. LLMs improved productivity in coding, writing, and literature review; however, 29 of 41 respondents (70.7%) reported significant errors, including incorrect code, statistical misinterpretations, and hallucinated functions. Key verification strategies included expertise, external validation, debugging, and manual inspection. Among 58 respondents providing training feedback, 44 (75.9%) requested case studies, 40 (69.0%) sought interactive tutorials, and 37 (63.8%) desired structured training.

LLM usage is notable among respondents at two academic medical centers, though response patterns likely reflect early adopters. While LLMs enhance productivity, challenges like errors and reliability concerns highlight the need for verification strategies and systematic validation. The strong interest in training underscores the need for structured guidance. As an initial step, we propose eight core principles for responsible LLM integration, offering a preliminary framework for structured usage, validation, and ethical considerations.

A critical challenge for biomedical investigators is the delay between research and its adoption, yet there are few tools that use bibliometrics and artificial intelligence to address this translational gap. We built a tool to quantify translation of clinical investigation using novel approaches to identify themes in published clinical trials from PubMed and their appearance in the natural language elements of the electronic health record (EHR).

As a use case, we selected the translation of known health effects of exercise for heart disease, as found in published clinical trials, with the appearance of these themes in the EHR of heart disease patients seen in an emergency department (ED). We present a self-supervised framework that quantifies semantic similarity of themes within the EHR.

We found that 12.7% of the clinical trial abstracts dataset recommended aerobic exercise or strength training. Of the ED treatment plans, 19.2% related to heart disease. Of these, the treatment plans that included heart disease identified aerobic exercise or strength training only 0.34% of the time. Treatment plans from the overall ED dataset mentioned aerobic exercise or strength training less than 5% of the time.

Having access to publicly available clinical research and associated EHR data, including clinician notes and after-visit summaries, provided a unique opportunity to assess the adoption of clinical research in medical practice. This approach can be used for a variety of clinical conditions, and if assessed over time could measure implementation effectiveness of quality improvement strategies and clinical guidelines.

High workforce turbulence has plagued clinical research, becoming intensified during the COVID-19 pandemic, especially for patient-facing workers. In a time of great uncertainty and risk among healthcare workers, researchers included, the pandemic also brought increased demand for research studies in volume, speed, and complexity, triggering elevated staff turnover. This has posed significant hurdles for employers, especially research sites, where retaining skilled patient-facing clinical research professionals (CRPs) is pivotal for sustaining medical innovation. Lack of job standardization and advancement pathways has been noted to play an important role both in turnover and contributes to the inability to accurately measure workforce trends. To address these factors, Duke University adopted a competency-based job classification system for CRPs in 2016.

Since that adoption of competency-based jobs, employee-level staffing data for all CRPs have been tracked monthly, creating a master data file from September 2016 through June 2024. This study updates previous analyses, evaluating turnover and turbulence rates, and demographic changes in the CRP workforce over this period.

Over the last six years, the Duke CRP workforce remained relatively stable. Voluntary turnover rates fluctuated, peaking at 19.1% in FY 2021 during the COVID-19 pandemic, and have steadily declined each year since then.

Despite national workforce challenges exacerbated by the pandemic, our data indicate that proactive measures to standardize clinical research jobs and assess the resultant well-defined site-based employee data may have mitigated extremes in workforce turnover at Duke University. Turbulence rates, while stabilizing, signal areas for further study.

While organizations leading community initiatives play a crucial role in tackling public health challenges, their difficulties in designing rigorous evaluations often undermine the strength of their proposals and diminish their chances of securing funding. We developed a matching service funded by the Robert Wood Johnson Foundation’s Evidence for Action program to bridge these gaps. This service identified matched applicants involved in community-engaged research with evaluation experts to provide complementary expertise, strengthen evaluation capacity, and enhance participants’ ability to secure funding.

We conducted a mixed-methods evaluation of the pilot phase of the Accelerating Collaborations for Evaluation Matching Service from August 2018 to February 2021. Data sources included program records, participant surveys administered at 3-, 6-, and 12-months post-match, and semi-structured interviews conducted at 12–18 months post-match. We assessed outcomes such as match success, resubmissions, funding rates, and participant satisfaction.

Over the 2.5-year pilot period, the matching service successfully matched 20 of 24 referred applicants. Among these, 50% submitted revised proposals, and a third of secured funding. Survey results indicated widespread satisfaction with the partnerships. One-year interviews highlighted complementary expertise, bidirectional learning, and capacity-building as key benefits of these partnerships.

This pilot demonstrated the feasibility, acceptability, and impact of the matching service in creating rewarding collaborations for community-engaged researchers. Beyond funding outcomes, participants uniformly valued the partnerships and described them as mutually satisfying. This model offers a scalable approach to creating research partnerships to build capacity for the evaluation of community initiatives.

The purpose of this study was to document the development of a Community Advisory Board (CAB) to enhance equitable dissemination of research findings within an implementation mapping study to enhance equitable impact of Universal School Meals (USM) through the Designing for Dissemination and Sustainability (D4DS) process.

The D4DS process comprises 7 key elements to facilitate meaningful dissemination. To accomplish Step 1: Identify Partners, the research team conducted snowball recruitment methods within the local Philadelphia community and with existing connections. To Empathize and Outline the Problem (Step 2) and Understand the Context (Step 3), an interest meeting was held followed by monthly meetings. Our team Confirmed and Co-designed the Product (Step 4) and Developed the Dissemination Plan (Step 5) through collaborative brainstorming sessions. Finally, we started the Iterative Evaluation (Step 6) and Plan for Sustainability (Step 7) by administering a baseline and follow-up survey measuring CAB members’ perceived utility, effectiveness, and sustainability of the board.

The final CAB included 8 members. The co-created dissemination products and plan comprised a 2-page infographic, social media toolkits, and a webinar slide deck, which were disseminated locally by the research team via presentations, websites, and email communication, in spring 2024. Initial findings from baseline and follow-up surveys indicated that CAB members benefited from skill development, compensation, writing credit, and autonomy in dissemination designing.

Sharing power and decision-making enhanced the capacity for local-level dissemination, which is much needed to advance the science of community partnerships.