Mentorship is a key focus area of efforts to promote a robust biomedical workforce. A growing body of research suggests that mentors’ cultural diversity awareness is an important factor in effective mentoring that can support mentees’ academic and professional outcomes. Less is known about the psychometric properties of scales assessing cultural diversity awareness (CDA). Further, researchers may not have space in surveys to measure all aspects of CDA. We examine the factorial structure and measurement invariance of the CDA Race/Ethnicity (R/E) Behaviors Scale, a subscale from an originally developed CDA Scale, using a large diverse undergraduate sample enrolled in higher education institutions funded by the National Institutes of Health BUilding Infrastructure Leading to Diversity (BUILD) Initiative.

The CDA R/E Behaviors Scale measures mentees’ perceptions of a mentor’s behaviors concerning cultural diversity awareness. Using a diverse national sample of over 4,000 undergraduate mentees, we examined dimensionality, reliability coefficients, and measurement invariance of the scale.

We found high reliability among the items with a Cronbach’s alpha of 0.91 and Omega of 0.91. Items indicated a unidimensional scale and we found differential item-functioning (DIF) with respect to gender, but no DIF across race/ethnicity.

This study lends additional evidence to support construct validity of the CDA R/E Behaviors Scale, a subscale of the originally developed CDA Scale. Our study supports its use in future research for other science and medical training programs, and contributes tools to program evaluation efforts aimed at improving culturally responsive mentorship throughout the scientific career pathway.

Clinical translational research relies on clinical research professional staff, and efforts to stimulate a future workforce has included outreach to high schools.

We assembled a multidisciplinary team from three institutions, leveraging expertise in education, clinical translational science, and team science to design and implement a summer camp for high school students to expose them to career opportunities in clinical research. Using backward design, we developed structured lesson plans and logistical operations. Recruitment targeted rising sophomores and juniors from Columbus, Ohio, metropolitan area high schools, with a blinded review process and no grade point averages requirement to encourage broad participation. Evaluation included pre/post assessments, facilitator feedback, and daily safety checks, with IRB exemption secured for toolkit development and dissemination.

Within one month, we received 100 applications from 14 schools, far exceeding expectations, and selected 34 students from 8 Columbus, Ohio, metropolitan area high schools. Of the 34 accepted, 33 participated in most elements of the program and 29 students completed all four days of camp and post-camp evaluations, with an average self-reported goal achievement score of 8.41 out of 10. Pre- and post-test results showed statistically significant increases in confidence across clinical research topics.

The pilot summer camp for high school students, supported by in-kind contributions, successfully met its goals and led to the creation of a replicable summer camp toolkit. The camp laid a strong foundation for future offerings and collaborations, with ongoing efforts to secure funding and expand access and impact.

Implementation science increasingly uses participatory systems modeling (PSM) approaches to handle the complexity inherent to implementation science issues. To support the process of integrating PSM with implementation science, we aimed to understand and explicate the benefits, facilitators, and future needs of applying PSM to implementation research.

We conducted semi-structured qualitative interviews with 23 researchers (n = 18) and practitioners (n = 5). We purposively sampled participants and identified additional participants through recommendations. Interviews were inductively analyzed. Key concepts were identified via iterative description, comparison, and conceptualization.

Engagement with people in the system was typically focused in earlier stages of PSM approaches, while engagement with decision makers occurred throughout a project. PSM approaches benefited researchers (e.g., improving the relevance of research) and practitioners (e.g., promoting systems thinking). Both benefited from the visual, intuitive nature of PSM and the ability of PSM to reflect partners’ input transparently. Facilitators included trusting relationships and conducting practice-driven research. Participants emphasized the need to improve funding opportunities for engagement and increase training in systems modeling facilitation.

Our findings can help move the field towards fully partnered and impactful implementation research that addresses the systems problems. While PSM approaches are promising, if not done according to best practices of partnered research, they will reproduce existing power imbalances and consultative engagement patterns between community partners and academics.

Vision impairment disproportionately affects individuals from low-income and African American communities, yet underutilization of preventive eye care persists. We conducted a community-based participatory research (CBPR) study to explore perceptions, values, and barriers related to vision care in an underserved urban neighborhood.

In partnership with University Settlement, a trusted community center in Cleveland, Ohio, and a study-specific Community Advisory Board (CAB), we conducted semi-structured interviews with 60 adult residents of the Broadway-Slavic Village neighborhood. Thematic analysis identified key facilitators and barriers to accessing vision care.

Participants across racial groups described vision as highly important and often endorsed more frequent preventive eye care than they reported receiving. Barriers to eye care utilization included cost, inadequate insurance coverage, transportation challenges, fear of diagnosis, and limited awareness of preventive benefits. Distinct differences emerged in insurance type and visit frequency between African American and White participants despite comparable socioeconomic status. Participants identified community-specific solutions such as free or low-cost exams, mobile clinics, transportation support, and increased education.

Our findings demonstrate that underutilization of eye care reflects structural and systemic barriers rather than lack of motivation. System-level interventions that integrate screenings into clinical and community settings, streamline referral pathways, and reduce logistical burdens are needed to facilitate equitable access to vision care. Embedding data collection in a trusted community setting surfaced perspectives from individuals who may be absent from clinic-based or online research and provides a model for translational research addressing health disparities.

Primary care providers understand the importance of collecting social needs screener (SNS) information from patients. Gathering this information permits more comprehensive, patient-centered care plans and allows primary care settings to serve as a hub where patients can receive resources and referrals to address their social needs. Medical–legal partnerships (MLPs) are a legal intervention that can also connect patients to social services. However, barriers exist that prevent full integration of SNS and MLPs. This study aims to understand how best to integrate SNS and MLPs into primary care.

Informed by the Consolidated Framework for Implementation Research (CFIR), an idealized process map and semi-structured interview guide were developed to facilitate interviews with primary care providers. The process map included four “swim lanes,” including planning, patient-provider interaction, referrals, and MLPs. Eleven primary care providers from three different health systems were interviewed in January–March 2025. Interviews were recorded, transcribed verbatim, and data were coded inductively.

Eleven themes were identified from the interviews, including review and adaptation of SNS should be regular, involvement of providers and staff is necessary for SNS integration, cross-disciplinary training, physical space, and information technology is needed to promote MLP, and identification of local resources for social need referrals is critical for providers’ buy-in. These themes were matched to constructs from CFIR.

SNS and MLPs can be integrated into the clinical workflow, but external factors may negate the impacts of SNS success and MLP referrals. By addressing the barriers identified using implementation strategies, integration of SNS and MLPs into primary care can be more feasible.

The growing utility and availability of genome-scale sequencing has led to increasingly broad incorporation across specialty disciplines of clinical research. However, classification of clinical relevance of genetic variation is an inherently clinical task, and the expertise to perform the necessary analysis, confirmation and reporting of these variants is not available in all research teams; consequently, disclosure of genomic variants to research participants remains challenging for many researchers. Advancing genomic medicine as a standard of care first requires institutional commitment and partnerships in supporting genomics in varied research studies that are inclusive of return of results to participants.

The University of North Carolina at Chapel Hill has vast experience with genetics in both clinical and research realms. By utilizing historical experience and input from key players, the Clinical GENomic analYSIS (GENYSIS) core facility was created as a case study and aims to provide a roadmap for research organizations to implement their own genomic sequencing core facilities.

The core has established a molecular sign-out conference, partnered with other core facilities on campus, and provides five main services: bioinformatics, variant analysis, clinical reporting, post-test services, and consultation with project advising. This paper presents case examples with discussion of continuous methodology improvements and embedded educational activities.

This novel shared research resource enables clinical researchers with limited staff and genomics expertise to provide clinically relevant results to their study participants, expanding the reach of genomics research.

Clinical and Translational Science Awards (CTSAs) are positioned to enhance the integration of rigorous implementation research methods into projects across their networks, but lack a systematic, standardized process to do so. This study introduces the Dissemination and Implementation Research Capability Self Survey (DIRC-SS), a pragmatic instrument to evaluate and integrate implementation science methods in traditional research activities.

We developed the 15-item DIRC-SS to assess researchers’ use of implementation research methods across five key constructs. Its reliability (inter-rater agreement and internal consistency) and sensitivity (change over time) were examined in 10 NIH-funded research projects via ratings assigned by the research teams and by implementation science experts at baseline and one year later.

The DIRC-SS total score demonstrated good internal consistency and inter-rater reliability increased over one year. Although the research team ratings did not change significantly over time, the expert ratings significantly increased, and effect sizes across research teams and expert raters were large in this small sample study.

The DIRC-SS demonstrated good internal consistency reliability and moderate inter-rater reliability. It effectively distinguished between different levels of implementation research methods integration. Unlike tools focused on grant proposals or final reports, the DIRC-SS can be used at any point in the research process by a research team as a self-survey, by implementation science experts in a consultation process, or across a CTSA program to characterize the implementation science methods employed across projects and highlight targeted areas for researcher education and training.

Carotid atherosclerosis is associated with increased coronary heart disease (CHD) risk, yet current risk models lack specificity and interpretability for this population. This study aimed to develop explainable machine learning (ML) models to predict CHD in these patients.

We retrospectively analyzed 487 patients with carotid atherosclerosis (191 CHD, 296 non-CHD) from January 2022 to July 2025. Thirty-eight variables were collected, including demographic, clinical, and biochemical indicators. LASSO regression identified six key predictors. Seven ML models were trained and evaluated using area under receiver operating characteristic curve (AUC), PRC-AUC, calibration curves, and decision curve analysis (DCA). SHAP was applied to interpret the best-performing model.

Logistic regression model achieved the highest test-set performance (AUC = 0.827; PRC-AUC = 0.752), with strong generalizability and calibration. SHAP analysis identified age and diastolic blood pressure as the most influential features, aligning with model coefficients. DCA demonstrated superior clinical net benefit of the logistic regression model across probability thresholds.

A six-variable logistic model provides accurate and interpretable CHD risk prediction in patients with carotid atherosclerosis. Its transparency and clinical utility support its integration into personalized risk management.

Effective communication of risks and benefits is essential to ethical clinical trial recruitment, yet clinical research coordinators (CRCs) often lack formal training in how to navigate participant uncertainty. This study examined whether targeted communication training could enhance CRCs’ ability to disclose risks and benefits clearly and ethically.

A mixed-methods, pre–post design compared CRCs who received communication training with those in a control group.

Standardized patient (SP) interactions simulating clinical trial recruitment conversations.

Twenty-four CRCs participated across intervention and control groups.

A communication training program grounded, focusing on transparent disclosure, benefit framing, and participant engagement strategies.

Qualitative content analysis of SP interaction transcripts guided by uncertainty management theory examined risk disclosure, mitigation strategies, benefit framing, and engagement. Frequency analysis tracked changes in the number and specificity of risk/benefit mentions over time.

CRCs in the intervention group demonstrated more structured and transparent risk disclosure, greater use of participant-centered benefit framing, and improved interpersonal engagement. Frequency analysis showed significant increases in both the number and specificity of risk and benefit mentions post-training. Control group CRCs showed minimal change.

Targeted communication training enhances CRCs’ ability to manage participant uncertainty, improving both ethical standards and informed consent quality in clinical trial recruitment.

National clinical studies improve generalizability when they enroll participants from geographically underserved areas. We assessed representativeness of dentists and patients in the National Dental Practice-Based Research Network (“Network”) by comparing them to national benchmarks for rural/urban and underserved classifications.

Analyses compared rural status using Rural-Urban Continuum Codes and Health Provider Shortage Areas (HPSA), practitioner data from the Network’s Enrollment Questionnaire and the American Community Survey (ACS), and patient data from Network clinical studies and the Behavioral Risk Factor Surveillance System (BRFSS).

The network has similar proportions of dentists in rural areas compared to national estimates from the ACS (8.5% in Network vs. 8.2% nationally) and comparable proportions in HPSA (76.8% in Network vs. 84.0% nationally). The Network’s proportion of patients living in rural areas (33.7%) is much higher than that of Network practitioners (8.5%), and higher than that of the US “dental” population overall from the BRFSS (20.5%).

The Network not only is effective at engaging a broad dentist base but also engages a comparably higher proportion of rural patients, a group that is often underrepresented in clinical research. BRFSS respondents are a more-selective subset of the population because they report a recent dental visit, yet the Network exceeded even this benchmark in rural representation.