The purpose of this article is to describe the process of developing and implementing a transdisciplinary community-based research center, the Center for Health Equity Research (CHER) Chicago, to offer a model for designing and implementing research centers that aim to address structural causes of health inequality.

Scholars from diverse backgrounds and disciplines formed a multidisciplinary team for the Center and adopted the structural violence framework as the organizing conceptual model. All Center activities were based on community partnership. The Center activities were organized within three cores: administrative, investigator development, and community engagement and dissemination cores. The key activities during the first year were to develop a pilot grant program for early-stage investigators (ESIs) and to establish community partnership mechanisms.

CHER provided more than 60 consultations for ESIs, which resulted in 31 pilot applications over the three application cycles. Over 200 academic and community partners attended the community symposium and discussed community priority. Some challenges encountered were to improve communication among investigators, to clarify roles and responsibilities of the three cores, and to build consensus on the definition and operationalization of the concept of structural violence.

There is an increasing need for local hubs to facilitate transdisciplinary collaboration and community engagement to effectively address health inequity. Building consensus around a shared vision among partners is a difficult and yet important step toward achieving equity.

Social unrest tied to racism negatively impacted half of NIH-funded extramural researchers underrepresented (UR) in science. UR early-career scientists encounter more challenges in their research careers, but the impact of social unrest due to systemic racism in this group is unclear. We used mixed methods to describe the impact of social unrest due to systemic racism on mentoring relationships, research, and psychological well-being in UR post-doctoral fellows and early-career faculty.

This is a cross-sectional analysis of data collected in September 2021–January 2022 from 144 UR early-career researchers from 25 academic medical centers in the Building Up Trial. The primary outcomes were agreement on five-point Likert scales with social unrest impact statements (e.g., “I experienced psychological distress due to events of social unrest regarding systemic racism”). Thematic analysis was conducted on responses to one open-ended question assessing how social unrest regarding systemic racism affected participants.

Most participants were female (80%), non-Hispanic Black (35%), or Hispanic (40%). Over half of participants (57%) experienced psychological distress as a result of social unrest due to systemic racism. Participants described direct and indirect discrimination and isolation from other persons of color at their institutions. Twice as many participants felt their mentoring relationships were positively (21%) versus negatively (11%) impacted by social unrest due to systemic racism.

Experiences with racial bias and discrimination impact the career and well-being of UR early-career researchers. Mentoring relationships and institutional support play an important role in buffering the negative impact of racial injustice for this population.

The Institute of Medicine recommended the advance of innovation and entrepreneurship training programs within the Clinical & Translational Science Award (CTSA) program; however, there remains a gap in adoption by CTSA institutes. The University of Michigan’s Michigan Institute for Clinical & Health Research and Fast Forward Medical Innovation (FFMI) partnered to develop a pilot program designed to teach CTSA hubs how to implement innovation and entrepreneurship programs at their home institutions.

The program provided a 2-day onsite training experience combined with observation of an ongoing course focused on providing biomedical innovation, commercialization and entrepreneurial training to a medical academician audience (FFMI fastPACE).

All 9 participating CTSA institutes reported a greater connection to biomedical research commercialization resources. Six launched their own version of the FFMI fastPACE course or modified existing programs. Two reported greater collaboration with their technology transfer offices.

The FFMI fastPACE course and training program may be suitable for CTSA hubs looking to enhance innovation and entrepreneurship within their institutions and across their innovation ecosystems.

Effective patient engagement is central to patient-centered outcomes research. A well-designed infrastructure supports and facilitates patient engagement, enabling study development and implementation. We sought to understand infrastructure needs from recipients of Patient-Centered Outcomes Research Institute (PCORI) pilot grant awards.

We surveyed recipients of PCORI pilot project awards on self-perceived strengths in engagement infrastructure through PCORI’s Ways of Engaging-Engagement Activity Tool survey, and interviewed leaders of 8 projects who volunteered as exemplars. Descriptive statistics summarized the survey findings. We conducted a thematic analysis of the interview transcripts.

Of the 50 surveyed pilots, 22 answered the engagement infrastructure questions (44% response rate). Survey and interview findings emphasized the importance of committed institutional leadership, ongoing relationships with stakeholder organizations, and infrastructure funding through Clinical and Translational Science Awards, PCORI, and institutional discretionary funds.

These findings highlight the importance of and how to improve upon existing institutional infrastructure.

This study describes the design, operation, and evaluation of a community-based research (CBR) consult service within the setting of a Clinical and Translational Science Award (CTSA) institution. To our knowledge, there are no published evaluations of a CBR consult service at a CTSA hub.

A CBR consult service was created to support faculty, health-care providers/research coordinators, trainees, community-based organizations, and community members. A framework was developed to assess the stages of client engagement and to foster clear articulation of client needs and challenges. A developmental evaluation system was integrated with the framework to track progress, store documents, continuously improve the consult service, and assess research outcomes.

This framework provides information on client numbers, types, services used, and successful outreach methods. Tracking progress reveals reasons that prevent clients from completing projects and facilitates learning outcomes relevant to clients and funding agencies. Clients benefit from the expert knowledge, community connections, and project guidance provided by the consult service team, increasing the likelihood of study completion and achieving research outcomes.

This study offers a framework by which CTSA institutions can expand their capacity to conduct and evaluate CBR while addressing challenges that inhibit community engagement.

This study examined the perceived competence of Clinical Research Coordinators (CRCs) using several conceptual frameworks. Accurate self-assessment of one’s professional competence is a critical component in the career navigation process and contributes to (a) identifying and securing professional development (training), (b) leveraging professional strengths, and (c) integrating self-knowledge into a comprehensive career plan.

A survey design gathered responses from a sample of 119 CRCs in a southeastern region of the USA Two conceptual frameworks were used to represent aspects of CRC professional competence: the eight Joint Task Force (JTF) competence domains, and perceptions of strengths and training needs from a list of 12 task categories.

The JTF domain with the lowest competence level was Development and Regulations, while the highest was Communication. Perceived competence increased incrementally with years of experience. Top strengths involved direct patient interaction and data management. Tasks in need of training included project management and reporting issues. Variations in responses were based on years of experience as a CRC.

Our results demonstrate an association between the self-reported strengths and training needs of CRCs and experience. This information can contribute to the self-directed career navigation of CRCs.

While previous studies have described career outcomes of physician-scientist trainees after graduation, trainee perceptions of research-intensive career pathways remain unclear. This study sought to identify the perceived interests, factors, and challenges associated with academic and research careers among predoctoral MD trainees, MD trainees with research-intense (>50%) career intentions (MD-RI), and MD-PhD trainees.

A 70-question survey was administered to 16,418 trainees at 32 academic medical centers from September 2012 to December 2014. MD vs. MD-RI (>50% research intentions) vs. MD-PhD trainee responses were compared by chi-square tests. Multivariate logistic regression analyses were performed to identify variables associated with academic and research career intentions.

There were 4433 respondents (27% response rate), including 2625 MD (64%), 653 MD-RI (15%), and 856 MD-PhD (21%) trainees. MD-PhDs were most interested in pursuing academia (85.8%), followed by MD-RIs (57.3%) and MDs (31.2%). Translational research was the primary career intention for MD-PhD trainees (42.9%). Clinical duties were the primary career intention for MD-RIs (51.9%) and MDs (84.2%). While 39.8% of MD-PhD respondents identified opportunities for research as the most important career selection factor, only 12.9% of MD-RI and 0.5% of MD respondents shared this perspective. Interest in basic research, translational research, clinical research, education, and the ability to identify a mentor were each independently associated with academic career intentions by multivariate regression.

Predoctoral MD, MD-RI, and MD-PhD trainees are unique cohorts with different perceptions and interests toward academic and research careers. Understanding these differences may help to guide efforts to mentor the next generation of physician-scientists.

Community-based organizations (CBOs) are well-positioned to incorporate research evidence, local expertise, and contextual factors to address health inequities. However, insufficient capacity limits use of evidence-based interventions (EBIs) in these settings. Capacity-building implementation strategies are popular, but a lack of standard models and validated measures hinders progress in the field. To advance the literature, we conducted a comprehensive scoping review.

With a reference librarian, we executed a comprehensive search strategy of PubMed/Medline, Web of Science Core Collection, and EBSCO Global Health. We included articles that addressed implementation science, capacity-building, and CBOs. Of 5527 articles, 99 met our inclusion criteria, and we extracted data using a double-coding process

Of the 99 articles, 47% defined capacity explicitly, 31% defined it indirectly, and 21% did not define it. Common concepts in definitions were skills, knowledge/expertise, and resources. Of the 57 articles with quantitative analysis, 48 (82%) measured capacity, and 11 (23%) offered psychometric data for the capacity measures. Of the 99 studies, 40% focused exclusively on populations experiencing inequities and 22% included those populations to some extent. The bulk of the studies came from high-income countries.

Implementation scientists should 1) be explicit about models and definitions of capacity and strategies for building capacity, 2) specify expected multi-level implementation outcomes, 3) develop and use validated measures for quantitative work, and 4) integrate equity considerations into the conceptualization and measurement of capacity-building efforts. With these refinements, we can ensure that the necessary supports reach CBO practitioners and critical partners for addressing health inequities.

Electronic health record (EHR) data have many quality problems that may affect the outcome of research results and decision support systems. Many methods have been used to evaluate EHR data quality. However, there has yet to be a consensus on the best practice. We used a rule-based approach to assess the variability of EHR data quality across multiple healthcare systems.

To quantify data quality concerns across healthcare systems in a PCORnet Clinical Research Network, we used a previously tested rule-based framework tailored to the PCORnet Common Data Model to perform data quality assessment at 13 clinical sites across eight states. Results were compared with the current PCORnet data curation process to explore the differences between both methods. Additional analyses of testosterone therapy prescribing were used to explore clinical care variability and quality.

The framework detected discrepancies across sites, revealing evident data quality variability between sites. The detailed requirements encoded the rules captured additional data errors with a specificity that aids in remediation of technical errors compared to the current PCORnet data curation process. Other rules designed to detect logical and clinical inconsistencies may also support clinical care variability and quality programs.

Rule-based EHR data quality methods quantify significant discrepancies across all sites. Medication and laboratory sources are causes of data errors.

Clinical research involving humans is critically important, but it is a lengthy and expensive process. Most studies require institutional review board (IRB) approval. Our objective is to identify predictors of delays or accelerations in the IRB review process and apply this knowledge to inform process change in an effort to improve IRB efficiency, transparency, consistency and communication.

We analyzed timelines of protocol submissions to determine protocol or IRB characteristics associated with different processing times. Our evaluation included single variable analysis to identify significant predictors of IRB processing time and machine learning methods to predict processing times through the IRB review system. Based on initial identified predictors, changes to IRB workflow and staffing procedures were instituted and we repeated our analysis.

Our analysis identified several predictors of delays in the IRB review process including type of IRB review to be conducted, whether a protocol falls under Veteran’s Administration purview and specific staff in charge of a protocol's review.

We have identified several predictors of delays in IRB protocol review processing times using statistical and machine learning methods. Application of this knowledge to process improvement efforts in two IRBs has led to increased efficiency in protocol review. The workflow and system enhancements that are being made support our four-part goal of improving IRB efficiency, consistency, transparency, and communication.

Federal grant funding to support infrastructure development of translational biomedical research centers is a form of public health intervention. Establishing rigorous methods for measuring center success and outcomes is essential to justify continued funding.

Bibliometric data compiled from a 5-year funding cycle of neurodegeneration and translational neuroscience research center were analyzed using the package bibliometrix for open-source software R and the NIH-developed research tool iCite.

The research team and their collaborators (n = 485) produced 157 grant-citing publications from 2015–2020. The science was produced by small research teams clustered around three main communities of topics: Alzheimer’s Disease, brain imaging, and neuropsychological testing in the elderly. Using the relative citation ratio, the publications produced by the research team were found to be influential when compared to other R01-funded publications.

Recent developments in bibliometric analysis expand beyond traditional measurement capabilities to better understand the characteristics, outcomes, and influences of research teams. These findings can be used to inform researchers and institutions about research team composition, productivity, and success. Measures of research influence may be used to justify return on investment to funders.

In order to tackle the challenge of efficiently meeting clinical research accrual goals, many Clinical and Translational Science Award (CTSA) recipients have developed recruitment support mechanisms and resources to help investigators successfully recruit study participants. Disseminating recruitment best practices and developing collaborations between institutions can help strengthen recruitment capabilities and methodologies currently utilized by researchers.

To discover what recruitment resources and mechanisms CTSAs are using, the CTSA Recruitment and Retention working group developed an electronic survey, which was distributed to CTSAs between May and July 2019. The survey contained over 50 multiple choice and short answer questions, with 40 of the 64 CTSA institutions completing the survey. Institutions reported on registries, feasibility assessment tools, clinical trial listings, experience recruiting special populations, program operations and evaluation, workforce education, social media use, and other recruitment resources.

All respondents currently utilize some form of a volunteer registry; over 80% of the CTSAs provide investigators with recruitment consultations, feasibility assessments, study listings, and electronic health record (EHR) utilization; 73% assist with study materials; 47% offer social media assistance. Many institutions reported success in recruiting patients and healthy volunteers, but difficulty in recruiting special populations such as non-English-speaking persons and rural populations. Additional recruitment tools included use of the EHR to facilitate recruitment, use of registries, and use of social media to engage participants.

Areas of opportunity or growth include the development of innovative solutions in the areas of social media advertising, identification of participants from special populations, and research volunteer engagement.