Scientific quality and feasibility are part of ethics review by Institutional Review Boards (IRBs). Scientific Review Committees (SRCs) were proposed to facilitate this assessment by the Clinical and Translational Science Award (CTSA) SRC Consensus Group. This study assessed SRC feasibility and impact at CTSA-affiliated academic health centers (AHCs).

SRC implementation at 10 AHCs was assessed pre/post-intervention using quantitative and qualitative methods. Pre-intervention, four AHCs had no SRC, and six had at least one SRC needing modifications to better align with Consensus Group recommendations.

Facilitators of successful SRC implementation included broad-based communication, an external motivator, senior-level support, and committed SRC reviewers. Barriers included limited resources and staffing, variable local mandates, limited SRC authority, lack of anticipated benefit, and operational challenges. Research protocol quality did not differ significantly between study periods, but respondents suggested positive effects. During intervention, median total review duration did not lengthen for the 40% of protocols approved within 3 weeks. For the 60% under review after 3 weeks, review was lengthened primarily due to longer IRB review for SRC-reviewed protocols. Site interviews recommended designing locally effective SRC processes, building buy-in by communication or by mandate, allowing time for planning and sharing best practices, and connecting SRC and IRB procedures.

The CTSA SRC Consensus Group recommendations appear feasible. Although not conclusive in this relatively short initial implementation, sites perceived positive impact by SRCs on study quality. Optimal benefit will require local or federal mandate for implementation, adapting processes to local contexts, and employing SRC stipulations.

With persistent incidence, incomplete vaccination rates, confounding respiratory illnesses, and few therapeutic interventions available, COVID-19 continues to be a burden on the pediatric population. During a surge, it is difficult for hospitals to direct limited healthcare resources effectively. While the overwhelming majority of pediatric infections are mild, there have been life-threatening exceptions that illuminated the need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases. However, a nationwide capability for developing validated computational tools to identify pediatric patients at risk using real-world data does not exist.

HHS ASPR BARDA sought, through the power of competition in a challenge, to create computational models to address two clinically important questions using the National COVID Cohort Collaborative: (1) Of pediatric patients who test positive for COVID-19 in an outpatient setting, who are at risk for hospitalization? (2) Of pediatric patients who test positive for COVID-19 and are hospitalized, who are at risk for needing mechanical ventilation or cardiovascular interventions?

This challenge was the first, multi-agency, coordinated computational challenge carried out by the federal government as a response to a public health emergency. Fifty-five computational models were evaluated across both tasks and two winners and three honorable mentions were selected.

This challenge serves as a framework for how the government, research communities, and large data repositories can be brought together to source solutions when resources are strapped during a pandemic.

Research staff are critical to productive translational research teams, yet their professional development is rarely formally addressed.

We created Strategic Teamwork for Effective Practice Mentor Development Program (STEP-MDP) to promote skills development and build a community of practice. We ran and evaluated the STEP-MDP for 32 participants, which consisted of workshops focusing on team communication and mentorship/coaching skills.

We found that STEP-MDP had a long-term positive impact on participants and their teams.

This program facilitated the professional development of research staff.

Designing and conducting clinical trials is challenging for some institutions and researchers due to associated time and personnel requirements. We conducted recruitment, screening, informed consent, study product distribution, and data collection remotely. Our objective is to describe how to conduct a randomized clinical trial using remote and automated methods.

A randomized clinical trial in healthcare workers is used as a model. A random group of workers were invited to participate in the study through email. Following an automated process, interested individuals scheduled consent/screening interviews. Enrollees received study product by mail and surveys via email. Adherence to study product and safety were monitored with survey data review and via real-time safety alerts to study staff.

A staff of 10 remotely screened 406 subjects and enrolled 299 over a 3-month period. Adherence to study product was 87%, and survey data completeness was 98.5% over 9 months. Participants and study staff scored the System Usability Scale 93.8% and 90%, respectively. The automated and remote methods allowed the study maintenance period to be managed by a small study team of two members, while safety monitoring was conducted by three to four team members. Conception of the trial to study completion was 21 months.

The remote and automated methods produced efficient subject recruitment with excellent study product adherence and data completeness. These methods can improve efficiency without sacrificing safety or quality. We share our XML file for researchers to use as a template for learning purposes or designing their own clinical trials.

Although organizational climate may affect faculty’s mentoring behaviors, there has not been any way to measure that climate. The purpose of this study was to test the reliability and validity of two novel scales to measure organizational mentoring climate importance and availability at two public research universities.

We developed 36 content-valid mentoring climate items in four dimensions: Structure, Programs/Activities, Policies/Guidelines, and Values. In total, 355 faculty completed an anonymous, structured, online survey asking about the importance (very important to very unimportant) and availability (no, don’t know, yes) of each of the items. We conducted reliability analyses and construct validity testing using exploratory common factor analysis, principal axis factoring, and oblique rotation.

The majority of the predominantly female, White non-Hispanic, senior, tenure-track faculty were not currently mentoring another faculty or being mentored. Analyses demonstrated a 15-item solution for both the Organizational Mentoring Climate Importance (OMCI) and the Availability (OMCA) Scales, with three factors each: Organizational Expectations, Mentor–Mentee Relationships, and Resources. Standardized Cronbach alphas ranged from 0.74 to 0.90 for the subscales, and 0.94 (OMCI) and 0.87 (OMCA) for the full scales. Faculty rated all items as somewhat to very important; however, perceived availability was very low ranging from mentor training programs (40%) to guidelines for evaluating mentoring success or managing conflict (2.5%).

The scales will allow studying of how organizational climate may affect mentoring behavior and whether climate can be changed to improve faculty mentoring outcomes. We provide recommendations for furthering the science of organizational mentoring climate and culture.

In light of calls to engage community health workers (CHWs) in the delivery of cervical cancer screening innovations, this study explores CHW perspectives on i) barriers to cervical cancer screening in a predominantly Hispanic community in Lake County, Indiana, the county with the highest cervical mortality in the state; and ii) the acceptability and feasibility of CHW-facilitated human papillomavirus (HPV) self-sampling as a means of reducing screening disparities.

In 2021, in-depth interviews were conducted with 15 CHWs employed by Lake County community-based organizations including clinics, schools, and faith-based organizations.

Harnessing CHWs’ voices as insiders with knowledge of their communities’ health landscape, our analysis identified multilevel barriers to screening that spanned individual, interpersonal, and community levels of the socio-ecological model. CHW-facilitated HPV self-sampling shows promise of mitigating several barriers to cervical cancer screening. Privacy, time saved, and comfort were perceived to be facilitators for acceptability, with concerns about the novelty of this approach and trust in provider (as opposed to CHW) expertise emerging as key barriers. In terms of feasibility, synergies with existing CHW work, and some community members' prior experience with self-sampling were found to be facilitators, while CHW’s time limitations and self-efficacy in providing adequate medical support were areas of concern. Considerations for adoption included CHW training, gender concordance, safety, and respect, among others.

This study provides critical insights from CHWs as key stakeholders on a screening model that directly engages them, which can inform implementation to increase screening in medically-underserved communities in the US.

In clinical and translational research, data science is often and fortuitously integrated with data collection. This contrasts to the typical position of data scientists in other settings, where they are isolated from data collectors. Because of this, effective use of data science techniques to resolve translational questions requires innovation in the organization and management of these data.

We propose an operational framework that respects this important difference in how research teams are organized. To maximize the accuracy and speed of the clinical and translational data science enterprise under this framework, we define a set of eight best practices for data management.

In our own work at the University of Rochester, we have strived to utilize these practices in a customized version of the open source LabKey platform for integrated data management and collaboration. We have applied this platform to cohorts that longitudinally track multidomain data from over 3000 subjects.

We argue that this has made analytical datasets more readily available and lowered the bar to interdisciplinary collaboration, enabling a team-based data science that is unique to the clinical and translational setting.

Participants and research professionals often overestimate how well participants understand and appreciate consent information for clinical trials, and experts often vary in their determinations of participant’s capacity to consent to research. Past research has developed and validated instruments designed to assess participant understanding and appreciation, but the frequency with which they are utilized is unknown.

We administered a survey to clinical researchers working with older adults or those at risk of cognitive impairment (N = 1284), supplemented by qualitative interviews (N = 60).

We found that using a validated assessment of consent is relatively uncommon, being used by only 44% of researchers who had an opportunity. Factors that predicted adoption of validated assessments included not seeing the study sponsor as a barrier, positive attitudes toward assessments, and being confident that they had the resources needed to implement an assessment. The perceived barriers to adopting validated assessments of consent included lack of awareness, lack of knowledge, being unsure of how to administer such an assessment, and the burden associated with implementing this practice.

Increasing the use of validated assessments of consent will require educating researchers on the practice and emphasizing very practical assessments, and may require Institutional Review Boards (IRBs) or study sponsors to champion the use of assessments.