Participant characteristics

Thirty-five (n = 35) team science and evaluation professionals completed the survey with 91.4% affiliated with CTSA institutions, 5.7% (n = 2) with Clinical and Translational Research institutions, and 2.9% (n = 1) with government organizations. The majority (88.6%, n = 31) belonged to ACTS SIGs. The Evaluation SIG was most popular (60.0%, n = 21), followed by the TSP SIG (45.7%, n = 16). In terms of team science experience, 11.4% (n = 4) had <1 year of experience, 47.5% (n = 17) had 1–5 years of experience, and 42.8% (n = 15) participants had 6 or more years of experience. TSP SIG members tended to be more experienced, with 31.3% (n = 5) having >10 years compared to 25.7% overall. Professional roles encompassed five primary categories: evaluation-focused (e.g., program evaluators, directors of evaluation), team science-focused (e.g., team science leads, core scientists), leadership roles (e.g., program directors, associate directors), workforce development (e.g., education directors, training coordinators), and administrative roles (e.g., program administrators, senior staff).

Current evaluation practices

Among survey respondents’ organizations, 68.6% (n = 24) have implemented team science evaluations, while 20.0% (n = 7) have not conducted evaluations and 11.4% (n = 4) were uncertain about evaluation status. Among the CTSA institutions represented, the evaluation adoption rate was 75.0%.

The 24 respondents reporting conducting team science evaluations employed varied approaches, with mixed-methods strategies predominating. Quantitative measurement was most common (83.3%, n = 20), focusing on publications, grants, and citations. Qualitative approaches followed closely (70.8%, n = 17), incorporating interviews and case studies. Process measurement (e.g., meeting frequency, role clarity) was used by 54.2% (n = 13), team dynamics assessment (e.g., trust, communication) by 50.0% (n = 12), and translational outcomes (e.g., clinical/community impact) evaluation by 58.3% (n = 14). Organizations tended to use 3 evaluation methods on average. The use of multiple complementary methods reflects a growing sophistication in evaluation approach, moving beyond single-method limitations to leverage the strengths of both quantitative and qualitative data (see Figure 1).

Figure 1.

Team science evaluation approaches across clinical and translational science programs. This sample includes the 24 respondents who indicated that team science evaluation was conducted at their institution.

Evaluations of team science were conducted at varying times and frequencies, reflecting an integration of assessment into routine organizational processes: 54.2% (n = 13) conducted annual assessments, 54.2% (n = 13) evaluated at project milestones, and 37.5% (n = 9) performed pre/post funding evaluations. This variation in evaluation timing reflects organizational differences in how teams are organized and how evaluation can be most meaningfully integrated into research processes.

Application of evaluation findings

Evaluation findings served multiple strategic purposes. Nearly universal application occurred for informing programing (95.8%, n = 23), demonstrating practical utility for continuous improvement. Other applications included improving team functioning (58.3%, n = 14), securing funding (50.0%, n = 12), and fostering organizational change (45.8%, n = 11). Respondents reported applying evaluation findings for approximately 3 purposes, indicating broad utilization of evaluation results (see Figure 2).

Figure 2.

Application of evaluation findings across clinical and translational science programs. This sample includes the 24 respondents who indicated that team science evaluation was conducted at their institution.

Evaluation challenges

Survey participants identified challenges that can be organized into 4 key challenge domains: methodological, implementation, data and analysis, and organizational.

Methodological challenges arise from difficulties defining, measuring, and comparing key concepts across contexts. A central issue is operationalization. As one respondent emphasized, “One of the biggest challenges is in operationalizing our constructs. What is team science? How do we define impact?” Without shared definitions, evaluations vary widely. These challenges are compounded by a lack of validated instruments. Standardization is also difficult, since methods that work in one organizational setting often do not translate to others, limiting cross-team or cross-institutional comparisons. Attributing outcomes to team science initiatives is hard in complex systems, where isolating effects of a single intervention is rarely possible. These issues complicate rigorous evaluation, limit generalizability, and hinder building an evidence base about what works, for whom, and under what conditions.

Implementation challenges reflect the realities of conducting team science evaluations in real-world settings and can significantly affect data quality and interpretability. Resistance to evaluation efforts may limit participation and buy-in, while low survey response rates threaten representativeness. Time constraints faced by both evaluators and participants delay data collection and limit engagement, and team member turnover complicates longitudinal evaluation. As one participant explained, “Team science PIs/research members/survey responders often face time constraints, which slow down their response time for evaluation survey or email inquiry and prevents them from completing survey. This can result in low response rate in data collection and missing data.” These challenges constrain feasibility and rigor of evaluations, introduce bias, and make it harder to draw reliable conclusions about team functioning and outcomes over time.

Data and analysis challenges highlight structural limitations in collecting, managing, and interpreting evaluation data, weakening conclusions about team science initiatives. Long-term follow‑up is often difficult, limiting insight into sustained outcomes. These challenges are compounded by insufficient data to support comparative analyses and small sample sizes that reduce statistical power and restrict subgroup analyses. Inconsistent documentation across teams and programs further undermines data integration and comparability. As one respondent explained, “One of the primary challenges is the lack of sufficient data to conduct comparative or group-based analyses, which limits our ability to assess the true impact of leadership-focused team science training programs. Another challenge involves the longitudinal tracking of program participants, which is essential for understanding how team science training influences their long-term career development.” These challenges limit evaluators’ ability to demonstrate impact, detect differential effects, and build a robust evidence base for understanding how and when team interventions produce meaningful outcomes.

Organizational challenges reflect structural, cultural, and resource-related barriers within institutions or networks that limit effective implementation and evaluation of team science. Participants described varying levels of engagement across networks, with some teams or leaders less willing to participate in evaluation. Complex organizational structures complicate coordination and data flow, making it difficult to track teams, align documentation practices, or aggregate information. Competing institutional priorities constrain the time, funding, and attention devoted to evaluation, while inadequate numbers of dedicated evaluation personnel limit the capacity to manage and analyze data at scale. As one participant noted, there is sometimes “resistance to submitting and participating in evaluation efforts,” while another highlighted “…inconsistency in the way programs document team members, inconsistent responses from team members, and underutilization of services to support team science.” These challenges weaken evaluation infrastructure, reduce data quality, and impede the ability to systematically assess the effectiveness and value of team science initiatives.

Evaluation facilitators

Participants were asked to identify specific factors that enhanced their program’s ability to evaluate team science. The four domains that emerged included methodological enhancements, organizational support, collaborative approaches, and infrastructure elements.

Methodological enhancements involve improvements to evaluation design and practice that strengthen the rigor, relevance, and usefulness of team science assessment. Participants emphasized integrating qualitative methods alongside quantitative measures, noting that interviews, observations, and open‑ended feedback provide richer insight into team dynamics, collaborative processes, and contextual factors. As one respondent stated, “Bringing qualitative methods into the evaluation folder has helped tremendously.” Setting clear evaluation expectations early was also identified as critical for aligning around shared goals, roles, and data collection requirements. Centralized tracking systems were described as improving consistency, data management, and longitudinal follow‑up. Participants highlighted meaningful, measurable, and actionable metrics that accurately reflect team science outcomes and support decision‑making. These enhancements address methodological limitations by increasing measurement validity, strengthening comparability, and enabling more rigorous evaluations.

Organizational support factors reflect the structural and cultural conditions that enable effective, integrated, and sustainable evaluation. Participants emphasized that strong leadership commitment and dedicated time allocation signals the importance of evaluation and legitimizes the effort. Broader institutional backing strengthens evaluation capacity, and PIs are more likely to support evaluation when they perceive clear benefits (e.g., improved productivity or competitiveness for funding). When these supports are in place, evaluation is more easily embedded into routine organizational processes. As one participant noted, “Setting evaluation expectations at the beginning of a project, getting buy-in from teams” enhanced their program’s ability to evaluate team science within their CTSA. These support factors promote consistent participation, improve data quality, and help make team science evaluation a standard and enduring component of institutional practice.

Collaborative approaches focus on strengthening interpersonal and interprofessional relationships to enhance evaluation capacity. Participants emphasized strong partnerships between evaluators and team scientists, noted that close collaboration ensured evaluations are relevant, methodologically sound, and aligned with scientific goals. Working across disciplines and roles allows teams to leverage unique perspectives enriching evaluation design and supporting nuanced interpretations. Knowledge sharing around team science and evaluation theories and practices builds collective expertise, improving both rigor and applicability. Respondents highlighted the importance of cultivating specialized expertise, with individuals and teams developing advanced skills at the intersection of evaluation and team science, enabling more sophisticated and context-sensitive assessments. As one participant explained, “We have a fabulous evaluation team! We enjoy strong collaborations between the eval team and the team science core. Thus, we can bring their knowledge of metrics and their infrastructure for regular assessment and combine it with our knowledge about how teams work and the factors that impact their work.” These approaches promote shared ownership, strengthen methodological quality, and enhance the usefulness of findings for team science.

Infrastructure elements include the technical and logistical systems that enable efficient, high-quality evaluation of team science initiatives. Participants valued streamlined data management platforms like REDCap, which support secure, consistent metric tracking. Dedicated personnel who assist with data collection and management were essential for reducing burden and improving data accuracy. Centralized activity monitoring was highlighted, as consolidating information allows organizations to identify patterns, detect gaps, and report outcomes more efficiently. Effective communication systems were critical for showing the value of evaluation and fostering stakeholder trust. As one participant noted, “Team science project managers work closely [with their teams] to increase awareness of various evaluation needs and deadlines,” enhancing evaluation efforts within their CTSA. These elements strengthen evaluation capacity by improving coordination, data quality, and transparency.

Measures of success

Participants identified four outcome domains, each with metrics they viewed as most important for evaluating team science performance and growth.

Process measures assess the quality and dynamics of team interactions and how collaborative work is carried out. Participants described both broad assessments of the collaborative experience, often framed as “teaming,” and more nuanced measures of team functioning. These include measures of satisfaction, trust, and leadership skill development, which capture how effectively teams communicate, build relationships, and coordinate their work. Together, process measures provide insight into the social and relational mechanisms that underlie successful team science.

Productivity measures capture quantifiable outputs that reflect the effectiveness and accomplishments of collaborative research. Survey responses emphasized traditional metrics, publications, grant funding, and project completion rates, alongside measures of cross-institutional collaboration that reflect the breadth and depth of partnerships. Participants also described the value of documenting collaborative track records that synthesize quantitative outputs with qualitative information about individual contributions, team processes, and broader scholarly influence. These measures demonstrate the tangible results of team-based research while contextualizing how those results are achieved.

Impact measures assess the broader effects and long-term value beyond immediate research outputs. Participants highlighted outcomes related to career trajectories, including advancement, leadership roles, and expanded research opportunities, as well as community influence and engagement. Measures of translation to practice and real-world implementation were also emphasized, aligning with the Translational Science Benefits Model (TSBM). These measures capture health, economic, policy, and social benefits, offering a more comprehensive view of the sustained and societal value of team science initiatives.

Learning measures focus on growth in knowledge, skills, and capacity among individuals and teams engaged in collaborative research. Participants described assessing changes in individual awareness, knowledge, skills, and behaviors, as well as shifts in team-level practices such as communication and conflict resolution. These measures show how engagement in team science fosters learning at multiple levels, supporting continuous improvement, and strengthening teams’ ability to collaborate effectively over time.

Future directions for evaluating team science

When asked for recommendations on evaluating team science within translational science, participants identified several future directions across four key domains.

Methodological advancements reflect forward-looking improvements in the execution of team science evaluation that enhance rigor, comparability, and scalability. Respondents highlighted standardized approaches that enable cross‑institutional comparison while remaining adaptable to local contexts. Validated tools for longitudinal tracking help capture team development and sustained outcomes, while rigorous designs such as randomized trials or multiphase optimization strategies can strengthen causal inference. Participants stressed the value of large‑scale, coordinated data collection to support generalizable evidence bases. These advancements signal a shift toward evaluation frameworks that, “move beyond outputs to include process and real‑world impact, by fostering continuous learning, system‑wide improvement, and better health outcomes.”

Conceptual development advances strengthen the theoretical foundations guiding team science evaluation. Participants valued flexible models that account for stages of team development, since evaluation priorities and metrics shift across the team lifecycle. Competency‑based assessment frameworks help to identify core capacities, and respondents called for dynamic measures that move beyond binary classifications to capture changes over time. A deeper understanding of how teams evolve across project lifecycles was seen as essential. As one participant noted, “We need to develop flexible models based on team development stages and different translational starting points as well as flexibility in outcome criteria (since different teams have different anticipated outcomes).” These advances support nuanced, context‑sensitive evaluations that reflect the complexity and developmental nature of team science.

Measurement innovation focuses on developing refined metrics that enable timely, meaningful evaluation of team science, particularly in complex and rapidly evolving environments. Participants emphasized the need for interim measures of team function that capture team development without multi‑year follow‑up and for approaches that demonstrate return on investment (ROI) for team initiatives. Interdisciplinary evaluation strategies were seen as essential for honoring varied research paradigms, while participatory evaluation methods served as a way to improve relevance, interpretation, and uptake of findings. As one respondent articulated, “Moving beyond achievements and binary competencies (met/unmet) to something more dynamic and locally‑relevant to that particular context… teams are hard to shoehorn into bounded sets with predictable and observable characteristics.” These innovations reflect a shift toward evaluation approaches that balance standardization with contextual sensitivity, capturing team development in rigorous and meaningful ways.

Impact focus ensures that team science is productive and aligned with translational goals that improve health and well‑being. Respondents highlighted measuring cost‑effectiveness and resource efficiency alongside indicators of process quality, community benefit, and continuous learning outcomes. Rather than viewing impact as static, participants emphasized understanding how teams adapt as projects evolve or change in response to new evidence or context. As one respondent explained, “The future directions for evaluating team science, in my opinion, is understanding how teams change team members as their projects move along the translational spectrum or change direction completely. I think understanding how quickly or what barriers exist for teams as they need to adapt to change may be a marker for understanding success.” This impact-focused lens reframes evaluation as a tool for capturing adaptability and real‑world value, key indicators of meaningful and sustainable team science outcomes. Table 1 summarizes the key conceptual categories and examples derived from the qualitative analysis.

Table 1.

Summary of the insights from the multi-institutional survey on team science evaluation challenges, facilitators, measures, and future directions