Clinical/translational science (CTS) is team-based, requiring effective collaboration and communication across many disciplines involving a variety of stakeholders. We implemented a pre-doctoral team-based training model with didactic and experiential curricular interventions to support the development of CTS research skills in a cross-disciplinary team environment. We assessed the potential impact of this new training model as a team science intervention that can catalyze new cross-disciplinary collaborations across the institution.

Between 2016 and 2020, 32 pre-doctoral students and 26 co-mentors participated in the assessment of the CTS Team program over a two-year period of TL1 training grant support. Data collection and analyses followed a program logic model and used a variety of metrics for clinical and translational scientist career success.

CTS training in the context of CTS Teams supported improved self-efficacy for clinical research skills and resulted in a significant increase in the frequency of participation in cross-disciplinary collaborative activities by both trainees and mentors. Most CTS Team co-mentor pairs had not previously collaborated. Two-thirds of the co-mentors plan to continue collaborating, and most (85%) currently use or plan to use collaboration tools, for example, written collaboration plans, authorship agreements.

The CTS Team training model provides a unique clinical and translational science team training experience that embeds authentic cross-disciplinary research collaboration into PhD research projects. It establishes trainee cohorts that are diverse in terms of scientific disciplines and translational research phases, and creates a new cross-disciplinary community of practice across faculty members and research groups in multiple colleges.

Unmet social needs contribute to growing health disparities and rising health care costs. Strategies to collect and integrate information on social needs into patients’ electronic health records (EHRs) show promise for connecting patients with community resources. However, gaps remain in understanding the contextual factors that impact implementing these interventions in clinical settings.

We conducted qualitative interviews with patients and focus groups with providers (January−September 2020) in two primary care clinics to inform the implementation of a module that collects and integrates patient-reported social needs information into the EHR. Questions addressed constructs within the Theoretical Framework for Acceptability and the Consolidated Framework for Implementation Research. Data were coded deductively using team-based framework analysis, followed by inductive coding and matrix analyses.

Forty patients participated in interviews, with 20 recruited at the clinics and 20 from home. Two focus groups were conducted with a total of 12 providers. Factors salient to acceptability and feasibility included patients’ discomfort answering sensitive questions, concerns about privacy, difficulty reading/understanding module content, and technological literacy. Rapport with providers was a facilitator for patients to discuss social needs. Providers stressed that limited time with patients would be a barrier, and expressed concerns about the lack of available community resources.

Findings highlight the need for flexible approaches to assessing and discussing social needs with patients. Feasibility of the intervention is contingent upon support from the health system to facilitate social needs assessment and discussion. Further study of availability of community resources is needed to ensure intervention effectiveness.

The Best Practices in Social and Behavioral Research Course was developed to provide instruction on good clinical practice for social and behavioral trials. This study evaluated the new course.

Participants across 4 universities took the course (n=294) and were sent surveys following course completion and 2 months later. Outcomes included relevance, how engaging the course was, and working differently because of the course. Open-ended questions were posed to understand how work was impacted.

Participants rated the course as relevant and engaging (6.4 and 5.8/7 points) and reported working differently (4.7/7 points). Participants with less experience in social and behavioral trials were most likely to report working differently 2 months later.

The course was perceived as relevant and engaging. Participants described actions taken to improve rigor in implementing trials. Future studies with a larger sample and additional participating sites are recommended.

Electronic data capture is essential to advancing family-centered coordinated care in early intervention (EI). The purpose of this paper is to report on EI service coordinator response to piloting an electronic parent-reported outcome (e-PRO) assessment as part of their routine workflow, including lessons learned that may inform future phases of e-PRO implementation.

This second pilot study involved families enrolled in a large EI program (n=1040 families) in concert with their implementation of a statewide quality improvement initiative for care plan development and outcomes reporting. A total of 22 EI service coordinators and supervisors were engaged in 3 phases: initial e-PRO intervention, peer-mentor enhancement, and standard recruitment protocol.

Implementation of the e-PRO intervention and peer-mentoring enhancement yielded low enrollment rates over the first 6 months (n=17). A standard recruitment protocol has resulted in enrollment growth (n=83) towards the targeted enrollment rate (n=832).

This study reports on early insights for building and sustaining a productive academic-community partnership for e-PRO implementation to support family-centered coordinated care. Lessons learned from this academic-community partnership with respect to strategies for enhancing community significance, collaboration, return, and control are discussed as they inform further development of this intervention before scale-up.

This paper is the fourth in a 5-part series that focuses on educating and training the clinical and translational science workforce. The goal of this paper is to delineate components of effective career development programs that go beyond didactic training. All academic health centers with a Clinical and Translational Science Award have a KL2 career development award for junior faculty, and many also have a TL1 training program for predoctoral and postdoctoral fellows. The training across these programs varies, however junior investigators across the United States experience similar challenges. Junior investigators can get overwhelmed with the demands of building their own research program, particularly in academia. 1Often, they are sidetracked by competing demands that can derail their progress. In these situations, junior investigators experience frustration and may search for alternative career paths. By providing them with additional professional skills in the 5 domains of: (1) self-awareness; (2) selecting the right topic and securing funding; (3) getting adequate support; (4) working with others; and (5) managing yourself, your career, and your demands. We will give junior investigators additional tools to manage these demands and facilitate their own career success.

To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes.

A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and within-patient heterogeneity to predict glycemic levels and real-time risk of hypo-/hyperglycemic by creating specific target functions for these excursions.

The majority of the variation (73%) in glucose trajectories was explained by the first two FPCs. Higher order variation in the CGM profiles occurred during weeknights, although variation was higher on weekends. The model has low prediction errors and yields accurate predictions for both glucose levels and real-time risk of glycemic excursions.

By identifying these distinct longitudinal patterns as phenotypes, interventions can be targeted to optimize type 1 diabetes management for subgroups at the highest risk for compromised long-term outcomes such as cardiac disease or stroke. Further, the estimated change/variability in an individual’s glucose trajectory can be used to establish clinically meaningful and patient-specific thresholds that, when coupled with probabilistic predictive inference, provide a useful medical-monitoring tool.

Lack of trust toward medical research is a major barrier to research participation, particularly among some population groups. Valid measures of trust are needed to develop appropriate interventions. The study purpose was to compare two previously validated scales that measure trust in biomedical research – one developed by Hall et al. (H-TBR; 2006) and the other by Mainous et al. (M-TBR; 2006) – in relation to socio-demographic variables and attitudes toward research. Differences between Black and White respondents were explored.

Two nearly identical surveys – one with H-TBR and the other with M-TBR – were systematically administered to a convenience sample. Internal consistency reliability of each scale was assessed. Associations were computed between scores on each scale with attitudes toward biomedical research and demographic variables (i.e., gender, age, race, and socioeconomic status). The difference between White and Black respondents on each TBR score while controlling for age, education, and race was also investigated.

A total of 2020 participants completed the H-TBR survey; 1957 completed the M-TBR survey. Mean item scores for M-TBR were higher (F = 56.05, p < 0.001) among Whites than Blacks. Whites also had higher mean item scores than Blacks on H-TBR (F = 7.09, p < 0.001). Both scales showed a strong association with participants’ perceived barriers to research (ps < 0.001) and significant, positive correlations with interest in research participation (ps < 0.001). Age and household income were positive predictors of TBR scores, but the effects of education differed.

Both scales are internally consistent and show associations with attitudes toward research. Whites score higher than Blacks on both TBR scales, even while controlling for age and socioeconomic status.

Midcareer research faculty are a vital part of the advancement of science in U.S. medical schools, but there are troubling trends in recruitment, retention, and burnout rates.

The primary sampling frame for this online survey was recipients of a single R01 or equivalent and/or K-award from 2013 to 2019. Inclusion criteria were 3–14 years at a U.S. medical school and rank of associate professor or two or more years as assistant professor. Forty physician investigators and Ph.D. scientists volunteered for a faculty development program, and 106 were propensity-matched controls. Survey items covered self-efficacy in career, research, work-life; vitality/burnout; relationships, inclusion, trust; diversity; and intention to leave academic medicine.

The majority (52%) reported receiving poor mentoring; 40% experienced high burnout and 41% low vitality, which, in turn, predicted leaving intention (P < 0.0005). Women were more likely to report high burnout (P = 0.01) and low self-efficacy managing work and personal life (P = 0.01) and to be seriously considering leaving academic medicine than men (P = 0.003). Mentoring quality (P < 0.0005) and poor relationships, inclusion, and trust (P < 0.0005) predicted leaving intention. Non-underrepresented men were very likely to report low identity self-awareness (65%) and valuing differences (24%) versus underrepresented men (25% and 0%; P < 0.0005). Ph.D.s had lower career advancement self-efficacy than M.D.s (P < .0005).

Midcareer Ph.D. and physician investigators faced significant career challenges. Experiences diverged by underrepresentation, gender, and degree. Poor quality mentoring was an issue for most. Effective mentoring could address the concerns of this vital component of the biomedical workforce.

In order to conduct translational science, scientists must combine domain-specific expertise with knowledge on how to identify and cross translational hurdles, and insights on positioning discoveries for the next translational stage. Expert educators from the Clinical and Translational Science Awards (CTSA) Consortium identified 97 knowledge, skills, and abilities (KSAs) important to include in training programs for translational scientists. To assist educators and trainees to use these KSAs, a conceptual model called “Personalized Pathways” was developed that prioritizes KSAs based on trainee background, research area, or phenotype, and expertise on the research team.

To understand how CTSA educators prioritize specific KSAs when developing personalized training plans for different translational phenotypes and to identify areas of similarity and difference across phenotypes.

A web-based, cross-sectional survey of CTSA educators was done. For a selected phenotype, respondents recommended one of four levels of mastery for each of the 97 KSAs. Results were tabulated by frequency, weighted by importance, and divided into tertiles representing high, middle, and lower priority KSAs. Agreement across phenotypes was compared using Krippendorff’s alpha.

Ten KSAs were high training priority for Preclinical, Clinical, and Community-Engaged phenotypes. These address research methods, responsible conduct of research, team building, and communicating research results. Nine KSAs were in the next tertile for priority reflecting KSAs in biostatistics, bioinformatics, regulatory precepts, and translating implications of research findings.

A smaller set of KSAs can be prioritized for training Preclinical-, Clinical-, and Community-Engaged researchers. Future work should explore this approach for other phenotypes.

This study identified underlying career orientation types of clinical research coordinators (CRCs) using cluster analysis. Select career (satisfaction, engagement, and planning) and competency-related (perceived competence) information was used to identify four distinct career orientation types.

A web-based survey was administered to CRCs employed in one of four research institutions affiliated with a National Institutes of Health-funded Clinical and Translational Research Award (CTSA) in the southeastern USA. Each respondent completed a survey containing questions about personal background, individual attributes, perceived professional competence, and career orientation.

The first CRC type (35.2%) possessed a positive, knowledge-seeking orientation, characterized by high career-related scores but a conservative assessment of perceived competence. The second CRC type (18.6%) represented an optimistic and confident career orientation reflected in moderate to high scores on each of the four identifying factors. The third CRC type (27.6%) reflected an inconsistent career orientation highlighted by lowered perceived competence. The final CRC type (18.6%) reflected a disengaged orientation characterized by negative responses to all career and competence factors.

Understanding the career orientation of CRCs can be helpful to institutional administrators and clinical investigators as they seek to support the professional development of CRCs through tailored training efforts or work-related supports. Knowledge of career orientation may also inform individual CRCs as they manage their personal career paths by assessing current levels of functioning, career-related strengths or weaknesses, and training needs.