Enrollment into a prospective cohort study of mother–preterm infant dyads during the COVID-19 pandemic progressed slower than anticipated. Enrollment occurred during the first week after preterm birth, while infants were still hospitalized. We hypothesized that slower enrollment was attributable to mothers testing positive for COVID-19 as hospital policies restricted them from entering the neonatal intensive care unit, thus reducing interactions with research staff. However, only 4.5% of 245 screened mothers tested COVID-19 positive. Only 24.9% of those screened, far fewer than anticipated, were eligible for enrollment. Assumptions about pandemic-related enrollment barriers were not substantiated in this pediatric cohort.

This article presents a landscape assessment of the findings from the 2021 Clinical and Translational Science Award (CTSA) Evaluators Survey. This survey was the most recent iteration of a well established, national, peer-led systematic snapshot of the CTSA evaluators, their skillsets, listed evaluation resources, preferred methods, and identified best practices. Three questions guided our study: who are the CTSA evaluators, what competencies do they share and how is their work used within hubs. We describe our survey process (logistics of development, deployment, and differences in historical context with prior instruments); and present its main findings. We provide specific recommendations for evaluation practice in two main categories (National vs Group-level) including, among others, the need for a national, strategic plan for evaluation as well as enhanced mentoring and training of the next generation of evaluators. Although based on the challenges and opportunities currently within the CTSA Consortium, takeaways from this study constitute important lessons with potential for application in other large evaluation consortia. To our knowledge, this is the first time 2021 survey findings are disseminated widely, to increase transparency of the CTSA evaluators' work and to motivate conversations within hub and beyond, as to how best to leverage existent evaluative capacity.

OBJECTIVES/GOALS: Empowering the Participant Voice (EPV) is a 6-CTSA Rockefeller-led collaboration to developcustom REDCap infrastructure to collect participant feedback using the validated Research Participant Perception Survey (RPPS), demonstrate its value in use cases, and disseminate it for broad adoption. METHODS/STUDY POPULATION: The EPV team developed data and survey implementation standards, and specifications for the dashboard and multi-lingual RPPS/REDCap project XML file. The VUMC built a custom At-a-Glance Dashboard external module that displays Top Box scores (percent best answer), with conditional formatting to aid analysis, and response/completion rates. Results populate site dashboards, and aggregate to a multi-site dashboard for benchmarking. Results can be filtered by participant/study characteristics. Sites developed individual use cases, leveraging local infrastructure, initiatives and stakeholder input. Infrastructure and guides were designed for dissemination through public websites. RESULTS/ANTICIPATED RESULTS: Five sites sent 23,797surveys via email, patient portal or SMS. 4,133 (19%) participants diverse in age, race, and ethnicity, returned responses. Sites analyzed their data and acted on selected findings, improving recruitment, communication and feeling valued. Aggregate scores for feeling listened to and respected were hight (>90%%); scores for feeling prepared by the consent process were lower (57-77%) and require action. Some groups experiences were better than others. Sites differed significantly in some scores. Dissemination of EPV is underway. Infrastructure and guides are downloadable free of charge, with advice from the EPV team. In 2023, a sixth site began piloting a lower literacy survey version and syncing data to the consortium dashboard. DISCUSSION/SIGNIFICANCE: The EPV RPPS/REDCap infrastructure enabled sites to collect participant feedback, identify actionable findings and benchmark with peers. Stakeholders and collaborators designed and tested local initiatives to increase responses and diversity, address disparities, and discover better practices.

Empowering the Participant Voice (EPV) is an NCATS-funded six-CTSA collaboration to develop, demonstrate, and disseminate a low-cost infrastructure for collecting timely feedback from research participants, fostering trust, and providing data for improving clinical translational research. EPV leverages the validated Research Participant Perception Survey (RPPS) and the popular REDCap electronic data-capture platform. This report describes the development of infrastructure designed to overcome identified institutional barriers to routinely collecting participant feedback using RPPS and demonstration use cases. Sites engaged local stakeholders iteratively, incorporating feedback about anticipated value and potential concerns into project design. The team defined common standards and operations, developed software, and produced a detailed planning and implementation Guide. By May 2023, 2,575 participants diverse in age, race, ethnicity, and sex had responded to approximately 13,850 survey invitations (18.6%); 29% of responses included free-text comments. EPV infrastructure enabled sites to routinely access local and multi-site research participant experience data on an interactive analytics dashboard. The EPV learning collaborative continues to test initiatives to improve survey reach and optimize infrastructure and process. Broad uptake of EPV will expand the evidence base, enable hypothesis generation, and drive research-on-research locally and nationally to enhance the clinical research enterprise.

OBJECTIVES/GOALS: Empowering the Participant Voice (EPV) is a Rockefeller-led 6-CTSA consortium that aims to collect research participant feedback through new Research Participant Perception Survey (RPPS)/REDCap infrastructure and data aggregation to a national database. Here we describe diverse Use Cases and launch dissemination to other hubs. METHODS/STUDY POPULATION: The EPV team refined the RPPS-S and developed fielding and data standards, a multi-lingual RPPS/REDCap project XML, At-a-Glance Dashboard, EPV Consortium Database, and Use Cases to align with local initiatives and stakeholder input. Sites ran full thread tests of the infrastructure before launch. To demonstrate RPPS/REDCap, 5 sites implemented Use Cases, surveyed diverse populations via email, patient portal or SMS, and analyzed results using the At-a-Glance Dashboard External module (which provides visual analytics and enables filtering by participant/study characteristics). Sites continue to collect, synthesize and respond to actionable data. To disseminate infrastructure, we will invite early adopters to implement the RPPS/REDCap infrastructure locally, joining the EPV learning collective. RESULTS/ANTICIPATED RESULTS: To date, 5 sites surveyed 10,199 research participants, at post-consent or end of study. 2833 (26%) research participants responded, from diverse demographic groups. More than 90% gave the Top Box score response regarding courtesy, respect for cultural background, privacy, and lack of pressure to join a study. Disparities were apparent in the informed consent experience, with a Top Box score range of 38-78% in different demographics. Dissatisfaction with out-of-pocket research costs was a recurring theme. Top Box scores varied for feeling like a valued partner in research (69-93%), would recommend research participation to friends or family (56%-81%), and Overall Experience (64%-90%) questions. Sites identified actionable findings in areas of consent, communication, partnership, and study conduct. DISCUSSION/SIGNIFICANCE: The EPV RPPS/REDCap infrastructure enabled sites to broadly collect participant feedback, identify actionable findings and make inter-institutional comparisons. Collaborators are designing local initiatives to increase response rate and diversity, address disparities in research participation experiences, and discover better practices.

Diversification of the Translational Science workforce is a strategic goal for the National Center for the Advancement of Translational Science (NCATS) program. NCATS has identified the development of translational science education, training, and support for a diverse translational science workforce as key to advancing the growing field of translational science. An annual mixed-methods assessment has been conducted on Common Metrics data submitted by over 60 Clinical & Translational Science Awards (CTSA) programs nationwide and includes metrics addressing recruitment and retention of scientists with particular attention to underrepresented persons and women. This article describes a methodology for the development of From Insights to Action, a resource for guiding program implementation and strategic planning to develop a diverse clinical and translational science workforce. This was informed by the Common Metrics Initiative process and constituted of findings from qualitative interviews of a subset of CTSAs that participated. The dissemination of this guide had several impacts, including providing structural foci for the CTSA Fall 2020 program meeting centered on Diversity, Equity, and Inclusion in translational science; addressing NCATS’ goal of workforce diversity; and understanding the number of diverse graduates still engaged in research.

Identifying and disseminating actionable intelligence is a challenging task that requires thoughtful planning. The National Center for Advancing Translational Sciences instituted the Common Metrics Initiative with the goal of evaluating the Clinical and Translational Science Awards (CTSA) Programs using a standard set of metrics. Initially managed by Tufts University, the Center for Leading Innovation and Collaboration (CLIC) at the University of Rochester began leading this initiative in 2017. In directing this work, CLIC created a framework for communicating and disseminating data insights. Insights to Inspire emerged from the need to share strategies and lessons learned to improve metric performance at the local level to a network of 60+ academic research institutions. Insights to Inspire employs a mixed methods approach for translating data into actionable intelligence. A series of blogs, webinars, and webcasts were designed to communicate metric-specific strategies used by individual sites to the broader CTSA consortium. A dissemination plan to expand the reach beyond metric stakeholders utilized focused communications including social media channels, network newsletters, and presentations at national meetings. This framework serves as a blueprint for other national evaluation programs interested in a systematic approach to using data insights for continuous improvement.

OBJECTIVES/GOALS: Six CTSA sites formed a collaboration to DEVELOP, DEMONSTRATE, AND DISSEMINATE new infrastructure to streamline collection and analysis of research participant feedback, using the Research Participant Perception Survey (RPPS), common standards, and customized REDCap-based tools, to improve the clinical research enterprise. METHODS/STUDY POPULATION: DEVELOP charter, consensus approach, core survey, deployment standards, data-use agreements; define meta-data, system requirements for the infrastructure, use-cases. Engage stakeholders for broad institutional and community input. Build RPPS/REDCap project, visual analytics Dashboard External Module, and Program Dashboard module for evaluation. Configure to use with Multilingual Module. DEMONSTRATE by implementing site-based use cases that reflect local priorities and span diverse populations, testing different methods of survey deployment (REDCap, patient portal, SMS) to showcase utility and flexibility. Generate data for local and inter-institutional benchmarking. Refine, then DISSEMINATE new infrastructure across the Consortium and REDCap community for broader testing and uptake. RESULTS/ANTICIPATED RESULTS: The project team refined the RPPS survey for inclusivity and mode of informed consent; defined standards for survey timing, sampling, and study metadata; configured the data dictionary in English and Spanishfor use with the multi-lingual module ; developed tools for project evaluation. Stakeholder engagement identified themes of anticipated value and fears about feedback. We designed an At-a-Glance Dashboard to display survey results with detailed analytics and filters. A REDCap application programming interface will send de-identified site data to the EPV Consortium Database to support benchmarking. Full implementation began November 2021 and will scale in 2022. Dissemination to Consortium and REDCap users is ongoing through presentations and a project website (www.Rockefeller.edu/research/epv). DISCUSSION/SIGNIFICANCE: Direct feedback from representative populations about their experiences in research is essential to understand and resolve barriers to broad participation in research. Streamlined RPPS/REDCap infrastructure provides a platform for local and national benchmarking, and collection of actionable data to improve clinical research.

ABSTRACT IMPACT: This analysis helps disentangle various paths to translational collaboration, with implications for departmental capacity building and support. OBJECTIVES/GOALS: Studies that bridge research collaboration networks are cross-disciplinary and translational. We explored the characteristics of researchers and their collaboration patterns in bridging research grants at University of Rochester School of Medicine and Dentistry. METHODS/STUDY POPULATION: the database of sponsored research grants from 2011 to 2018, obtained from an internal University database was transformed into a two-mode network of grant-to-investigator. Grants at 90th percentile and above of normalized two-mode betweenness centrality were defined as ‘bridging grants’. For each grant we extracted the gender, academic rank, academic degree, affiliating department, and centrality-status (being at 75th percentile of degree centrality in one-mode collaboration network) of the Principal Investigator (PI), as well as the number of co-investigators (CI) and the existence of central actor(s) in the research team. RESULTS/ANTICIPATED RESULTS: Out of 2491 sponsored grants, 250 were ‘bridging grants’. The significant predictors of bridging were centrality of PI, existence of central CI(s), PI holding PhD, and larger number of CIs. The PI’s academic rank (being full professor) and gender were not significant predictors. Among bridging grants 79 included both central PI and CIs (central actors group) and 60 included no central actor on the team. In the latter group, more PIs were clinical faculty and fewer were full professors. Network analysis of affiliating departments showed that Medicine was the prominent actor in the central actors group, while the network of no-central actor group was more fragmented with Neurology as central. DISCUSSION/SIGNIFICANCE OF FINDINGS: Widely recognized researchers are more likely to collaborate with each other in bridging studies possibly marginalizing less experienced peers. Bridging grants led by less central researchers, often clinician-scientists, may thrive where supportive culture and departmental facilities exist.

OBJECTIVES/GOALS: Collaborations are at the core of translational science and team science. Differences by gender have been identified in various research contexts from recruitment to retention to promotion. This study assesses the relational associations of translational collaborations, and what role of gender. METHODS/STUDY POPULATION: In 2011 and 2013, clinical and basic sciences investigators at University of Rochester School of Medicine and Dentistry responded to an online survey nominating their research collaborators. Two study years were merged, and name lists were transformed into a collaboration network. Departments were classified into basic sciences (e.g. biochemistry) and clinical (e.g. urology). If respondent and partner were affiliated to different department classes, the collaboration was defined as translational. Multi-level GLM models were developed to assess the associates of the likelihood of translational vs. within discipline collaborations. Partner nominations were nested in respondents. RESULTS/ANTICIPATED RESULTS: 202 respondents were included in the multi-level GLM models. A collaboration was more likely to be translational if the respondent shared more collaborators with the partner (OR:1.13), and respondent was a central actor in collaboration network (OR: 1.2). Translational collaborations were less likely to be reported by clinicians (OR: 0.25). In the model to assess gender match, a collaboration was more likely to be translational if the respondent was male, and nominated a male partner. For both genders, collaboration with a partner of the opposite gender was more likely to be translational if respondent had more shared collaborators with the partner. DISCUSSION/SIGNIFICANCE OF IMPACT: Translational collaborations happen in teams. Gender homophily exits in translational collaborations, and is reduced by shared collaborators; implying the effect of personal connections and community membership. Community-building interventions may increase diversity in translational collaborations.

Life course research embraces the complexity of health and disease development, tackling the extensive interactions between genetics and environment. This interdisciplinary blueprint, or theoretical framework, offers a structure for research ideas and specifies relationships between related factors. Traditionally, methodological approaches attempt to reduce the complexity of these dynamic interactions and decompose health into component parts, ignoring the complex reciprocal interaction of factors that shape health over time. New methods that match the epistemological foundation of the life course framework are needed to fully explore adaptive, multilevel, and reciprocal interactions between individuals and their environment. The focus of this article is to (1) delineate the differences between lifespan and life course research, (2) articulate the importance of complex systems science as a methodological framework in the life course research toolbox to guide our research questions, (3) raise key questions that can be asked within the clinical and translational science domain utilizing this framework, and (4) provide recommendations for life course research implementation, charting the way forward. Recent advances in computational analytics, computer science, and data collection could be used to approximate, measure, and analyze the intertwining and dynamic nature of genetic and environmental factors involved in health development.

OBJECTIVES/SPECIFIC AIMS: To develop a social network model of collaborations within and external to the University of Rochester Medical Center (URMC) CTSI using data from the annual Research Performance Progress Report (RPPR) as well as other sources, to provide longitudinal evaluation of the CTSI’s engagement with key stakeholder groups. METHODS/STUDY POPULATION: The annually submitted RPPR follows a specific format with well-defined sections. The Highlights, Milestones and Challenges Report includes areas in which CTSI function leaders provide details about program integration and innovation, including collaborations with other functions or external groups. The Highlights, Milestones and Challenges Report was qualitatively coded to identify function-collaborator dyads. Each entity in the dyad became a node in the network. Nodes were connected by edges named by the dyads. The network included two types of nodes. The first were CTSI internal functions/programs, i.e. the entities that submitted RPPR sections and formed an interconnected sub-network. The second type of nodes were entities external to the CTSI (collaborators, internal or external to the CTSI site). These entities were named by functions submitting RPPR narratives. External nodes with similar meanings were consolidated. Duplicate edges were removed. CTSI-external nodes were grouped into five stakeholder categories: URMC, University of Rochester (UR), community, other CTSA institutions, CTSA consortium. Thus, these nodes were connected to the CTSI internal nodes, but not to each other. A second source of collaboration data was function-reported internal metrics. As part of the internal metric data collection, functions list partners who play a role in improving metric data or who are responsible for providing data. Partners identified in the internal metrics data, but not specified in the RPPR, were added to the network. RESULTS/ANTICIPATED RESULTS: Twenty-three internal CTSI functions submitted an RPPR and represent the CTSI internal nodes. Internal CTSI functions identified 235 collaborations (edges): 125 collaborations with other CTSI internal functions, 57 collaborations with URMC entities, 14 with UR entities, 15 with the external community, 15 with other institutions (CTSA hubs and other universities), and 9 with CTSA consortium entities. Thirty-eight of the collaborations were identified in the internal metrics partners section. In total, the network comprised 104 nodes. Graph density was.022 for full network and.21 for the CTSI internal sub-network. The global clustering coefficient, a measure of connectivity, for the CTSI internal sub-network was.252. DISCUSSION/SIGNIFICANCE OF IMPACT: The RPPR provides an underutilized source of data for annually repeated analyses of internal and external CTSI collaborations and is a way to enhance use of this routinely collected information. Analyses of the network yield metrics for measuring CTSI reach and impact on stakeholder groups over time. For example, measures such as number of nodes representing entities external to CTSI and average vertex degree of the CTSI Internal nodes track aspects of CTSI collaborations. Visualizations using different layouts or highlighting different sub-networks provide a representation of CTSI engagement with the communities of stakeholders as well as insights to relationships between functions, regions of collaboration, and areas of gaps. These data also provide an important new mechanism to engage the CTSI leadership and function leads in understanding how their work contributes to the overall network and synergies they have with each other.

OBJECTIVES/SPECIFIC AIMS: To longitudinally track emerging research collaborations and assess their development and productivity. METHODS/STUDY POPULATION: In four administrations (2011, 2013, 2015, 2017), all full- and part-time University of Rochester Medical Center faculty received an email invitation to complete a research collaborators survey. Respondents indicated whether they were involved in research, and if involved in research, identified collaborators from a drop-down list of investigators in the institution. Space was provided for write-ins. Full- and part-time status, faculty rank, and departmental affiliation was associated with each investigator. Grant data were obtained from a grant management database maintained by the institution’s Office of Research and Project Administration. Grant data included all submissions (funded and not funded), award number, award effective data, award final expiration date, funding amounts, principal investigator and co-investigators. Using Mathematica SNA software, for each year we identified collaborator dyads (including their characteristics such as inter/intradepartmental; investigator characteristics) and networks (e.g. size, density). RESULTS/ANTICIPATED RESULTS: On average, 1800 (range 1730-2034) full- and part-time faculty received email invitations to complete the survey. An average of 403 respondents (range 385-441) completed the survey each administration. While the response rate seems low, the survey was distributed to every faculty member regardless of their primary appointment. Thus it included a large number of individuals whose role is exclusively clinical. Grant data included 4429 awards received between 2011 and 2018, involving 1395 investigators as principal or co-investigators. Survey respondents naming collaborators ranged from 233 to 280 (average 257) with 1594 to 2265 (average 1988) collaborations named each year. Overall density increased from.0204 in 2011 to.0342 in 2017. Density within the group of female investigators increased from.0219 in 2011 to.0412 in 2017. Within the group of male investigators, density increase from.0226 to.0333 in the same time span. Analysis by rank, changes over time and those with grant funding is underway. DISCUSSION/SIGNIFICANCE OF IMPACT: This methodology captured a consistent number of collaborations over an 8 year period. Analyses reveal network growth over time and of increasing heterogeneity (by gender). Analyzing research networks overtime provides an important metric to assess how research networks evolve and devolve and the characteristics of those that grow or stagnate. Further these analyses can demonstrate the impact of support provided to networks or teams by the CTSI, department or other institutional mechanism.

OBJECTIVES/SPECIFIC AIMS: To share lessons learned from implementing a health survey to a global sample of mTWs. METHODS/STUDY POPULATION: mTWs were paid $0.50 for taking a 15 minute survey to ascertain attitudes and intentions toward participating in genetic research. Two phases included: pilot survey targeting 7 global regions and a large-scale implementation in English in United States, India, and other countries and in Spanish in Spanish speaking countries. Administrative and descriptive information were collected and analyzed by region/country including: completions by location, demographics, time to complete, and survey satisfaction. RESULTS/ANTICIPATED RESULTS: There are 4 key lessons: (1) MTurk is fast. The US sample (n=505) accrual took <2 days and the Indian sample (n=505) took 11 days, while the response from other countries (n=118) generally exceeded 30 days. (2) Using Amazon country specification was the best way to ensure responses from specific countries and regions. (3) Demographic differences exist in mTWs between countries. For example, US mTWs were significantly more likely female (60.1%) compared with India (30.2%) and other countries (34.2%). (4) mTWs found the survey understandable/acceptable. mTWs reported high understandability and acceptability of the survey, which did not vary significantly across countries or by language. DISCUSSION/SIGNIFICANCE OF IMPACT: mTurk provides an efficient platform for survey research from diverse US and Indian samples. In other countries and in Spanish, the mTurk mechanism yielded a smaller sample more slowly but was still effective.

This study links two models, one that simulates changes in snow microstructure and one that recovers microstructure properties from measurements of snow reflectance. An energy and mass transfer model, SNTHERM.89, was used to calculate snow grain growth. Grain-sizes from the model and measurements of grain bond areas provided estimates of the surface-to-volume ratio of the bulk snow, which were transformed to geometrically-equivalent sphere sizes. An inversion technique based on a discrete-ordinate model of the directional reflectance recovered optically-equivalent sphere sizes from reflectance measurements at 1.075 μm. The predictions of equivalent sphere sizes from the snow model and the recovered optical sphere sizes from the inversion method were compared with stereological measurements from snow sections. The geometrically-equivalent and optically-equivalent grain-sizes showed good agreement with each other and with stereological measurements from snow a few days old. The predictions of the reflectance inversion method also compared favorably with geometrically-equivalent grain-sizes measured from a melt-freeze surface crust. This investigation showed the potential for fully coupling snow property simulations with models to predict the spectral reflectance of snow.

Remote sensing has provided a means of obtaining estimates of snow-covered area, yet traditional methods have had difficulty mapping snow in shaded and vegetated areas. Spectral mixture analysis is a linear mixture modeling technique that shows promise for mapping land surface covers, particularly when imaging spectrometer data are used. Applying this technique to AVIRIS data collected over the Sierra Nevada, California, we have estimated the fraction of snow cover in each pixel, even in areas that are shaded or forested. This modeling technique enables us to map snow cover at the sub-pixel level and provides a means of estimating the errors associated with the calculation.

Engaging partners for tobacco control within low and middle income countries (LMICs) at early stages of tobacco control presents both challenges and opportunities in the global effort to avert the one billion premature tobacco caused deaths projected for this century. The Dominican Republic (DR) is one such early stage country. The current paper reports on lessons learned from 12 years of partnered United States (US)-DR tobacco cessation research conducted through two NIH trials (Proyecto Doble T, PDT1 and 2). The projects began with a grassroots approach of working with interested communities to develop and test interventions for cessation and secondhand smoke reduction that could benefit the communities, while concurrently building local capacity and providing resources, data, and models of implementation that could be used to ripple upward to expand partnerships and tobacco intervention efforts nationally. Lessons learned are discussed in four key areas: partnering for research, logistical issues in setting up the research project, disseminating and national networking, and mentoring. Effectively addressing the global tobacco epidemic will require sustained focus on supporting LMIC infrastructures for tobacco control, drawing on lessons learned across partnered trials such as those reported here, to provide feasible and innovative approaches for addressing this modifiable public health crisis.