Clinical and Translational Science Award Program (CTSA)-funded institutions were charged with developing clinical and translational science programs and transforming clinical research at their institutions. Community engagement (CE) was recognized as a key component and catalyst of that transformation. CE hub capacities for working with communities and translating knowledge into practice have been illustrated through their COVID-19 responses.

CE hub leaders met and discussed their CTSA’s early responses regarding the COVID-19 pandemic. The 2-hour discussion was distilled into themes which were sent to the CE hub leaders with a request for written accounts describing actions taken to engage local partners, communities, and institutions. The written reports form the basis for this compendium.

Eighteen institutions submitted written reports describing activities in relation to six themes: (1) listen to the community and respond to concerns, (2) collect data to understand the impact of COVID-19 on distinct communities and groups, (3) communicate science and address misinformation, (4) collaborate with health departments, (5) engage hubs and underrepresented populations in COVID-19 research, and (6) support our own well-being and that of others.

Bidirectional interactions comprise the foundation of CE, which requires trusted partnerships that sustain communication through a series of activities and goals. The nimble responses to the pandemic substantiate the need for CE programs to maintain the infrastructure necessary to achieve the primary CTSA goals of improving health within and across communities and localities as well as expanding research participation of community members.

There is a clear need to educate and train the clinical research workforce to conduct scientifically sound clinical research. Meeting this need requires the creation of tools to assess both an individual’s preparedness to function efficiently in the clinical research enterprise and tools to evaluate the quality and effectiveness of programs that are designed to educate and train clinical research professionals. Here we report the development and validation of a competency self-assessment entitled the Competency Index for Clinical Research Professionals, version II (CICRP-II).

CICRP-II was developed using data collected from clinical research coordinators (CRCs) participating in the “Development, Implementation and Assessment of Novel Training In Domain-Based Competencies” (DIAMOND) project at four clinical and translational science award (CTSA) hubs and partnering institutions.

An exploratory factor analysis (EFA) identified a two-factor structure: the first factor measures self-reported competence to perform Routine clinical research functions (e.g., good clinical practice regulations (GCPs)), while the second factor measures competence to perform Advanced clinical functions (e.g., global regulatory affairs). We demonstrate the between groups validity by comparing CRCs working in different research settings.

The excellent psychometric properties of CICRP-II and its ability to distinguish between experienced CRCs at research-intensive CTSA hubs and CRCs working in less-intensive community-based sites coupled with the simplicity of alternative methods for scoring respondents make it a valuable tool for gauging an individual’s perceived preparedness to function in the role of CRC as well as an equally valuable tool to evaluate the value and effectiveness of clinical research education and training programs.

The application of digital monitoring biomarkers in health, wellness and disease management is reviewed. Harnessing the near limitless capacity of these approaches in the managed healthcare continuum will benefit from a systems-based architecture which presents data quality, quantity, and ease of capture within a decision-making dashboard.

A framework was developed which stratifies key components and advances the concept of contextualized biomarkers. The framework codifies how direct, indirect, composite, and contextualized composite data can drive innovation for the application of digital biomarkers in healthcare.

The de novo framework implies consideration of physiological, behavioral, and environmental factors in the context of biomarker capture and analysis. Application in disease and wellness is highlighted, and incorporation in clinical feedback loops and closed-loop systems is illustrated.

The study of contextualized biomarkers has the potential to offer rich and insightful data for clinical decision making. Moreover, advancement of the field will benefit from innovation at the intersection of medicine, engineering, and science. Technological developments in this dynamic field will thus fuel its logical evolution guided by inputs from patients, physicians, healthcare providers, end-payors, actuarists, medical device manufacturers, and drug companies.

Defining key barriers to the development of a well-trained clinical research professional (CRP) workforce is an essential first step in identifying solutions for successful CRP onboarding, training, and competency development, which will enhance quality across the clinical and translational research enterprise. This study aimed to summarize barriers and best practices at academic medical centers related to effective CRP onboarding, training, professional development, identify challenges with the assessment of and mentoring for CRP competency growth, and describe opportunities to improve training and professionalization for the CRP career pathway.

Qualitative data from a series of Un-Meeting breakout sessions and open-text survey questions were analyzed to explore the complex issues involved when developing high-quality onboarding and continuing education opportunities for CRPs at academic medical centers.

Results suggest there are several barriers to training the CRP workforce, including balancing foundational onboarding with role-based training, managing logistical challenges and institutional contexts, identifying/enlisting institutional champions, assessing competency, and providing high-quality mentorship. Several of these themes are interrelated. Two universal threads present throughout all themes are the need for effective communication and the need to improve professionalization of the CRP career pathway.

Few institutions have solved all the issues related to training a competent and adaptable CRP workforce, although some have addressed one or more. We applied a socio-technical lens to illustrate our findings and the need for NCATS-funded academic medical centers to work collaboratively within and across institutions to overcome training barriers and support a vital, well-qualified workforce and present several exemplars from the field to help attain this goal.

There is growing evidence for both the need to manage work–life conflict and the opportunity for mentors to advise their mentees on how to do this in an academic research environment.

A multiphase approach was used to develop and implement an evidence-informed training module to help mentors guide their mentees in issues of work–life conflict. Analysis of existing data from a randomized controlled trial (RCT) of a mentor training curriculum (n = 283 mentor/mentee dyads) informed the development of a work–life mentoring module which was incorporated into an established research mentor training curriculum and evaluated by faculty at a single academic medical center.

Only 39% of mentors and 36% of mentees in the RCT indicated high satisfaction with the balance between their personal and professional lives. The majority (75%) of mentors and mentees were sharing personal information as part of the mentoring relationship which was significantly associated with mentees’ ratings of the balance between their personal and professional lives. The effectiveness of the work–life module was assessed by 60 faculty mentors participating in a mentor training program at an academic medical center from 2013 to 2017. Among the respondents to the post-training survey, 82.5% indicated they were very/somewhat comfortable addressing work–life issues with their mentees as a result of the training, with significant improvements (p = 0.001) in self-assessments of mentoring skill in this domain.

Our findings indicate that a structured training approach can significantly improve mentors’ self-reported skills in addressing work–life issues with their mentees.

Lack of participation in clinical trials (CTs) is a major barrier for the evaluation of new pharmaceuticals and devices. Here we report the results of the analysis of a dataset from ResearchMatch, an online clinical registry, using supervised machine learning approaches and a deep learning approach to discover characteristics of individuals more likely to show an interest in participating in CTs.

We trained six supervised machine learning classifiers (Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor Classifier (KNC), Adaboost Classifier (ABC) and a Random Forest Classifier (RFC)), as well as a deep learning method, Convolutional Neural Network (CNN), using a dataset of 841,377 instances and 20 features, including demographic data, geographic constraints, medical conditions and ResearchMatch visit history. Our outcome variable consisted of responses showing specific participant interest when presented with specific clinical trial opportunity invitations (‘yes’ or ‘no’). Furthermore, we created four subsets from this dataset based on top self-reported medical conditions and gender, which were separately analysed.

The deep learning model outperformed the machine learning classifiers, achieving an area under the curve (AUC) of 0.8105.

The results show sufficient evidence that there are meaningful correlations amongst predictor variables and outcome variable in the datasets analysed using the supervised machine learning classifiers. These approaches show promise in identifying individuals who may be more likely to participate when offered an opportunity for a clinical trial.

A key barrier to translation of biomedical research discoveries is a lack of understanding among scientists regarding the complexity and process of implementation. To address this challenge, the National Science Foundation’s Innovation Corps™ (I-Corps™) program trains researchers in entrepreneurship. We report results from the implementation of an I-Corps™ training program aimed at biomedical scientists from institutions funded by the National Center for Advancing Translational Sciences (NCATS).

National/regional instructors delivered 5-week I-Corps@NCATS short courses to 62 teams (150 individuals) across six institutions. Content included customer discovery, value proposition, and validating needs. Teams interviewed real-life customers and presented the value of innovations for specific end-users weekly, culminating in a “Finale” featuring their refined business thesis and business model canvas. Methodology was developed to evaluate the newly adapted program. National mixed-methods evaluation assessed program implementation, reach, effectiveness using observations of training delivery and surveys at Finale (n = 55 teams), and 3–12 months post-training (n = 34 teams).

Innovations related to medical devices (33%), drugs/biologics (20%), software applications (16%), and diagnostics (8%). An average of 24 interviews was conducted. Teams reported increased readiness for commercialization over time (83%, 9 months; 14%, 3 months). Thirty-nine percent met with institutional technology transfer to pursue licensing/patents and 24% pursued venture capital/investor funding following the short courses.

I-Corps@NCATS training provided the NCATS teams a rigorous and repeatable process to aid development of a business model based on customer needs. Outcomes of this pilot program support the expansion of I-Corps™ training to biomedical scientists for accelerating research translation.

There is discontent and turnover among faculty at US academic health centers because of the challenges in balancing clinical, research, teaching, and work–life responsibilities in the current healthcare environment. One potential strategy to improve faculty satisfaction and limit turnover is through faculty mentoring programs.

A Mentor Leadership Council was formed to design and implement an institution-wide faculty mentoring program across all colleges at an academic health center. The authors conducted an experimental study of the impact of the mentoring program using pre-intervention (2011) and 6-year (2017) post-intervention faculty surveys that measured the long-term effectiveness of the program.

The percent of faculty who responded to the surveys was 45.9% (656/1428) in 2011 and 40.2% (706/1756) in 2017. For faculty below the rank of full professor, percent of faculty with a mentor (45.3% vs. 67.1%, P < 0.001), familiarity with promotion criteria (81.7% vs. 90.0%, P = 0.001), and satisfaction with department’s support of career (75.6% vs. 84.7%, P = 0.002) improved. The percent of full professors serving as mentors also increased from 50.3% in 2011 to 68.0% in 2017 (P = 0.002). However, the percent of non-retiring faculty considering leaving the institution over the next 2 years increased from 18.8% in 2011 to 24.3% in 2017 (P = 0.02).

Implementation of an institution-wide faculty mentoring program significantly improved metrics of career development and faculty satisfaction but was not associated with a reduction in the percent of faculty considering leaving the institution. This suggests the need for additional efforts to identify and limit factors driving faculty turnover.

News media play an important role in raising public awareness, framing public opinions, affecting policy formulation, and acknowledgment of public health issues. Traditional qualitative content analysis for news sentiments and focuses are time-consuming and may not efficiently convey sentiments nor the focuses of news media.

We used descriptive statistics and state-of-art text mining to conduct sentiment analysis and topic modeling, to efficiently analyze over 3 million Reuters news articles during 2007–2017 for identifying their coverage, sentiments, and focuses for public health issues. Based on the top keywords from public health scientific journals, we identified 10 major public health issues (i.e., “air pollution,” “alcohol drinking,” “asthma,” “depression,” “diet,” “exercise,” “obesity,” “pregnancy,” “sexual behavior,” and “smoking”).

The news coverage for seven public health issues, “Smoking,” “Exercise,” “Alcohol drinking,” “Diet,” “Obesity,” “Depression,” and “Asthma” decreased over time. The news coverage for “Sexual behavior,” “Pregnancy,” and “Air pollution” fluctuated during 2007–2017. The sentiments of the news articles for three of the public health issues, “exercise,” “alcohol drinking,” and “diet” were predominately positive and associated such as “energy.” Sentiments for the remaining seven public health issues were mainly negative, linked to negative terms, e.g., diseases. The results of topic modeling reflected the media’s focus on public health issues.

Text mining methods may address the limitations of traditional qualitative approaches. Using big data to understand public health needs is a novel approach that could help clinical and translational science awards programs focus on community-engaged research efforts to address community priorities.