Expert Engagement Research Articles

Unveiling the forces driving expert activity: The impact of information environment and peer behavior on expert reviewer engagement behavior

Online platform engagement with customers, especially those with high expertise, is crucial for companies. As these expert customer reviews directly impact a company’s brand image and sales volume, an understanding of expert reviewer engagement behavior (EREB) is critical for companies’ marketplace success. This study explores the factors that influence EREB from two key situational cues: those from the company’s information environment and from peer expert behavior. Data from 144,634 Yelp reviewers and 5,080 restaurants were analyzed. The results reveal that companies with higher aggregate ratings are more likely to encourage EREB. The impact of overall and peer expert rating inconsistency on EREB varies: Overall rating inconsistency has a positive effect, while inconsistency among expert peers has a negative effect. Additionally, EREB exhibits herding and differentiation patterns in response to changes in peer expert engagement density. This results in a U-shaped relationship between EREB and peer expert engagement density, moderated by aggregate rating, overall rating inconsistency, and peer expert rating inconsistency. This study provides practical insights for marketers looking to engage expert customers and expands on the literature on expert customer engagement behavior.

Integrating environmental sustainability within national food-based dietary guidelines: a qualitative analysis from five case study countries

Abstract Background The incorporation of environmental sustainability principles into national food-based dietary guidelines (FBDGs) has gained prominence as a strategy to address both human health and environmental concerns. This study aims to identify and explore the processes and considerations to integrate environmental sustainability in food-based dietary guidelines. Methods Countries that have integrated environmental sustainability in FBDGs since 2016 were identified from the UN FAO website. Background documents and interviews with key stakeholders involved in the development of FBDGs from case study countries (The Netherlands, Denmark, Chile, France and Belgium (Flanders)) were thematically analysed using the Framework Method. A coding framework explored the rationale for integration, data sources and analysis methods, stakeholder and expert engagement, synergies and trade-offs, translation into public messaging, and key learnings. Results Eight background documents and six interviews were analysed. Approaches to integrating environmental sustainability varied, from inclusion in dietary modelling optimisation to changes to consumer messaging. Nearly all countries adjusted guidelines to reduce red and processed meat and emphasise the role of plants in diets. However, issues of cultural significance, affordability, and access limited ambitions to reduce overall meat intake. Novel plant-based alternatives remain contentious due to concerns about their ultra-processed nature and potential nutrient inadequacies as substitution foods. Key barriers to integrating environmental sustainability into FBDGs include consumer acceptance, data quality and availability, stakeholder involvement and conflict, timelines, and resource constraints. Conclusions Several countries have successfully integrated environmental sustainability into their dietary guidelines, but there is still variation in the integration and acceptance of FBDGs including environmental sustainability.

What Makes An Expert? Reviewing How ML Researchers Define "Expert"

Human experts are often engaged in the development of machine learning systems to collect and validate data, consult on algorithm development, and evaluate system performance. At the same time, who counts as an ‘expert’ and what constitutes ‘expertise’ is not always explicitly defined. In this work, we review 112 academic publications that explicitly reference ‘expert’ and ‘expertise’ and that describe the development of machine learning (ML) systems to survey how expertise is characterized and the role experts play. We find that expertise is often undefined and forms of knowledge outside of formal education and professional certification are rarely sought, which has implications for the kinds of knowledge that are recognized and legitimized in ML development. Moreover, we find that expert knowledge tends to be utilized in ways focused on mining textbook knowledge, such as through data annotation. We discuss the ways experts are engaged in ML development in relation to deskilling, the social construction of expertise, and implications for responsible AI development. We point to a need for reflection and specificity in justifications of domain expert engagement, both as a matter of documentation and reproducibility, as well as a matter of broadening the range of recognized expertise.

Expert engagement in setting a climate adaptation research agenda

AbstractIn order to initiate the actionable science needed to support effective conservation under climate change, we engaged researchers and other experts in refining and prioritizing a climate adaptation research agenda that was originally developed via dialogue with natural resource managers. Experts identified topics that were missing or underrepresented in an initial practitioner‐defined list of science topics, and then scored topics according to the state of knowledge, the feasibility of research, and the potential that research might change management. Our process capitalizes on the complementarity between the expertise of practitioners and the expertise of researchers and other non‐practitioners, improves the transparency and legitimacy of the agenda‐setting process, and reveals the challenges public agencies have in focusing on some research topics.

Improving Professional Development Based on Evaluation of Nursing Education Programs Through Feedback.

A feedback-driven selection process to evaluate professionals strengthens and improves the quality of future education they provide. A university in Sri Lanka invited teaching specialists by incorporating feedback of those teaching educators. The selection of professionals to teach courses improved the quality of education drastically. The findings show that refining education through feedback enhances future education quality and promotes subject matter expert engagement in improving educational programs. [J Contin Educ Nurs. 2024;55(3):110-112.].

Digital Participatory Model as Part of a Data-Driven Decision Support System for Urban Vibrancy

Digital participation relies on computational systems as the instruments for expert engagement, data-driven insight, and informed decision-making. This study aims to increase expert engagement with the Bayesian-based decision support model in evaluating urban vibrancy decisions. In this study, urban vibrancy parameters are defined using “economic, use, and image value” measures. This article focuses on the visual aspect of vibrancy, defined as the image value of place. The image value is evaluated through likability and likability features. The case study area is the Eminönü Central Business District in the Istanbul Historic Peninsula due to its distinctive urban dynamics derived from the duality of being a cultural and cosmopolitan city center. This research presents a method as a decision support system (DSS) model based on the Bayesian belief network (BBN) and spatial BBN for supporting urban vibrancy decisions. The spatial BBNs monitor spatial outcomes of variables’ dependencies that form through the BBN relationship network. Spatial BBN tools monitors the spatial impact of decisions for informed urban interventions. The results demonstrate that urban greening, pedestrianization, and human-scaled streetscapes should be prioritized to make streets more likable. The most significant intervention areas are Tahtakale for signboard regulation, Sultanahmet and Vefa for cultural landscape improvement, and Vefa and Mahmutpaşa for planning building enclosures. The participation is achieved by evaluating urban vibrancy with what-if scenarios using BBN. The developed DSS model addresses which parameters should be prioritized, and what are their spatial consequences. The use of spatial BBN tools presents certain limitations in terms of interoperability and user interaction. Overall, this research contributes to participatory urban planning by incorporating both conditional and spatial dependencies. This unique approach not only promotes a more holistic understanding of urban vibrancy but also contributes to the advancement of digital participation in urban planning decisions.

Performance Comparison of Indoor Humidity Modeling: A Novel Hybrid Model vs. the Simplified Calculation Model by EN ISO 13788

Ventilation is essential for maintaining indoor air quality and human comfort, particularly in buildings with mechanical ventilation systems. However, modeling the complex relationship between ventilation rate and measurable indoor environmental parameters, such as humidity, poses significant challenges. Current methods for modeling this relationship are often inefficient. For example, data-driven models frequently require sufficient information on ventilation, which may not be available for constant air volume (CAV) systems. On the other hand, simulation programs can be time-consuming and require expert engagement. In this study, we present two simplified calculation models, including a hybrid model developed by the authors, and a well-known model introduced by EN ISO 13788. We compare their performance in evaluating the impact of ventilation rate on indoor humidity. The hybrid model uses a novel analytical method to upgrade a single-input regression model (which considers only outdoor humidity as input) to enable the evaluation of indoor humidity based on both outdoor humidity and ventilation rate. We used the commercial TRNSYS program to model two scenarios: increasing the reference ventilation rate and decreasing the reference ventilation rate. The results showed that both models matched TRNSYS simulations with satisfactory accuracies, but the hybrid model demonstrated superior performance to the EN ISO 13788 model in both scenarios. The hybrid model’s higher accuracy and hybrid features make it better suited for big data analysis and field studies.

An empirical investigation into UK university–industry collaboration: the development of an impact framework

Providing evidence of the impact of university–industry (U–I) partnerships is challenging. This empirical research contributes to this thought-provoking subject area by developing an impact assessment framework to assess the effect of collaboration between university and industry. This is examined through a multiple case study approach: 13 partnership schemes, each of two years duration, in manufacturing and healthcare. This study demonstrates that effective knowledge transfer from universities to enterprises is not only hypothetically feasible, but also realistically tangible and measurable. It explores how Business and Management Schools transfer knowledge and technology through external interventions and formal partnership schemes. Our findings show that impact and knowledge transfer can be evaluated, but requires active facilitation before, during and after the project, plus a level of openness and expert engagement within the partnerships. Additionally, our findings established that healthcare partnerships generated higher perceived levels of impact than manufacturing. This perhaps indicates that further work is necessary to resolve the issues limiting the productivity gains of manufacturing partnerships.

Assessing the validity and feasibility of using a best-worst scaling measure for values elicitation among older adults with newly-diagnosed blood cancers to inform treatment decisions.

344 Background: Multiple stakeholders have called for the development of tools to facilitate values elicitation with patients to inform treatment decision making about chemotherapy. We previously developed a best-worst scaling (BWS) measure, a theory-driven values elicitation tool, for older adults (≥ 60 years) with blood cancers through patient and expert engagement. We sought to evaluate the feasibility and validity of using this measure to inform initial treatment decisions for newly-diagnosed patients. Methods: We completed a convergent mixed-methods study to test feasibility and validity of remote delivery of the measure following guidance for choice experiments (Janssen, Exp Rev Pharm Out Res, 2017). Convergent validity was evaluated by comparing preferences with a ranking exercise. Content validity was tested in cognitive interviews. Results: 178 patients at a public safety net hospital were eligible, 157 (88%) were approached, and 48 (31%) consented (48% lymphoma, 40% leukemia/myelodysplastic syndrome, 12% other). Mean age was 73.9 years (range 60 - 95). 59% were male; 96% were white; 67% had a bachelor’s degree or higher. 52% completed the BWS measure prior to their initial treatment visit. Patients agreed or strongly agreed that the instrument was understandable (96%), relevant to them (58%), and showed their real preferences (75%). 50% felt it was easy to answer. 54% of responses from the BWS measure were concordant with ranking on their top priority. Cognitive interviews (n=7) demonstrated content validity: all patients were able to accurately describe the instructions of the BWS instrument and correctly define each included outcome; most patients reported that the BWS instrument helped solidify their preferences; all patients reported it was beneficial; no patients reported missing domains of outcomes; all reported that the time and emotional burden to complete the measure were appropriate. Conclusions: Using this BWS measure to inform treatment decisions of older adults with newly-diagnosed blood cancers is feasible and appears valid for clinical use. Future work is needed to optimize implementation and validate the measure in different settings and with a larger and more diverse population.

The role of the dually certified primary care/psychiatric mental health nurse practitioner in treating high-needs/high-cost patients.

National nursing organizations have called for nurses to provide leadership in care solutions for high-cost/high-needs vulnerable populations. In response to this call, important modifications are emerging in Doctor of Nursing Practice programs, including an increasing number of nurses pursuing dual certification in primary care and psychiatric mental health or primary care nurse practitioners returning for a postgraduate certificate in psychiatric mental health. This innovative role warrants examination, particularly because it relates to high-needs/high-cost patients such as those with serious mental illness (SMI). This article highlights two aspects of the role of the primary care/psychiatric mental health nurse practitioner (PC/PMHNP): one, to provide a vision of the PC/PMHNP as a unique solution for optimal care of vulnerable patients and two, to demonstrate potential contributions of the PC/PMHNP to the larger health care system. A case exemplar is used to illustrate role contributions of the PC/PMHNP in a high-needs/high-cost patient with SMI and complex co-occurring physical illness. Three capacities of the PC/PMHNP are discussed: depth and breath, expert engagement and rapport building, and full patient-centered care and flexibility. The case exemplar emphasizes the PC/PMHNP value through transitions across care settings and where gaps in service frequently occur. The impact of the PC/PMHNP on improved patient outcomes, patient and provider satisfaction, and cost savings are explored.

The systematic development of guidance for parents on talking to children of primary school age about weight

BackgroundThe prevalence of overweight and obesity in children is increasing, alongside increases in rates of children’s anxiety and worry about their weight. In many countries children’s weight is measured, and parents are made aware if a child has been classified as having overweight or obesity. However, many parents are concerned that raising their child’s awareness of their weight, and talking to children about their weight could harm their wellbeing. The aim of this project was to develop guidance for parents on constructive ways to talk to children of primary school age about weight when they need to.MethodsThe project followed a mixed-methods design: Phase 1 involved the collation of evidence including (a) two previously published systematic literature reviews to identify the associations between parent–child weight talk, and broader health discussions, and children’s wellbeing, (b) interviews with children, and (c) development and refinement of narrative messaging (previously published). In Phase 2 we developed a conceptual model and mapped primary findings to techniques and content within a draft guidance document for parents on talking to children about weight. Phase 3 involved a modified Delphi process with 29 stakeholders to refine and agree a final version.ResultsAn acceptable draft guidance was agreed following two stages of feedback from Delphi participants. Key areas for debate and adaptation included: encouraging discussion about health and growth with lesser focus on weight; finding ways to reduce stigma and perceptions of blame; emphasising a whole-family approach; inclusive representation of diversity among children and families.ConclusionsConsensus on the content of guidance for parents on talking to children about weight was achieved through a process of evidence review and stakeholder and expert engagement. The next steps are to measure the impact of the resource on improving the experience of parents and children in communicating about weight.

Evaluating Allied Health Clinical Placement Performance: Protocol for a Modified Delphi Study.

University-affiliated student-led health care services have emerged in response to the challenges faced by universities in securing quality clinical placements for health care students. Evidence of the health care benefits and challenges of student-led health care services is growing, while evidence of clinical placement performance remains variable and not generalizable. Though there have been previous attempts to develop a framework for evaluation of clinical placement performance, concerns have been raised about the applicability of these frameworks across the various placement settings. Additionally, the perspectives of all key stakeholders on the critical areas of clinical placement performance have yet to be considered. This study's objective is to gather information on areas of measurement related to student learning outcomes, experience of placement, and costs of placement and then develop consensus on which of those areas need to be included in a framework for evaluation of clinical placement performance within the context of student-led health care services. The aim of this paper is to outline a protocol for a modified Delphi study designed to gain consensus on what is important to measure when evaluating an allied health clinical placement. We will recruit up to 30 experts to a heterogeneous expert panel in a modified Delphi study. Experts will consist of those with firsthand experience either coordinating, supervising, or undertaking clinical placement. Purposive sampling will be used to ensure maximum variation in expert panel member characteristics. Experts' opinions will be sought on measuring student learning outcomes, student experience, and cost of clinical placement, and other areas of clinical placement performance that are considered important. Three rounds will be conducted to establish consensus on what is important to measure when evaluating clinical placement. Each round is anticipated to yield both quantitative data (eg, percentage of agreement) and qualitative data (eg, free-text responses). In each round, quantitative data will be analyzed descriptively and used to determine consensus, which will be defined as ≥70% agreement. Qualitative responses will be analyzed thematically and used to inform the subsequent round. Findings of each round will be presented, both consensus data and qualitative responses in each subsequent round, to inform expert panel members and to elicit further rankings on areas of measurement yet to achieve consensus. Data analysis is currently underway, with a planned publication in 2024. The modified Delphi approach, supported by existing research and its ability to gain consensus through multiround expert engagement, provides an appropriate methodology to inform the development of a framework for the evaluation of clinical placement performance in allied health service. DERR1-10.2196/44020.

Developing an indicator-based assessment framework for assessing the sustainability of urban water management

Currently, Metropolitans throughout the globe are confronting crucial obstacles to accomplishing water management sustainability, encompassing climate change, explosive urbanization, and swelling populations. This study proposes a framework to evaluate the sustainability of urban water management (UWM), underscoring the significance of expert engagement in identifying appropriate indicators. The study adopts a novel two-step procedure for screening over 300 candidate indicators. A two-stage procedure was followed initially, underpinned by SMART criteria. In the second stage, 24 fit-to-purpose indicators were selected through the fuzzy Delphi method. The framework is subsequently finalized by categorizing the final indicators into seven categories, namely “Water Security,” “Water Quality,” “Drinking Water,” Sanitation and Reuse, “Financial Management,” “Governance,” and “Risk and Disaster Management,” thus facilitating better understanding and linking of UWM practices among diverse stakeholders. The paper demonstrates its application in Mashhad City as a real case. The rapid adoption of the proposed framework in Iran underscores its efficacy in facilitating stakeholders to cultivate collective commitment. This indicator-based framework can assist in identifying essential sustainability trade-offs. In addition, it can also be a practical approach for recognizing water-related priorities to achieve urban water management sustainability. The suggested framework can inspire further expansion of indicator-based frameworks for UWM.

Development of a novel shared decision making aid for primary immunodeficiency diseases.

Aim: To describe development of a shared decision making (SDM) aid in treating primary immunodeficiency diseases (PID) with immunoglobulin replacement therapy (IGRT). Materials & methods: Expert engagement and qualitative formative research informed development. IGRT administration features were prioritized using object-case best-worst scaling (BWS) methodology. The aid was assessed by US adults self-reporting PID and revised following interviews/mock treatment-choice discussions with immunologists. Results: Patients participating in interviews (n=19) and mock treatment-choice discussions (n=5) deemed the aid useful/accessible and supported the utility of BWS, with content and BWS exercises refined following participant feedback. Conclusion: Formative research led to an improved SDM aid/BWS exercise, and illustrated how the aid may improve treatment decision making. The aid may help less-experienced patients and facilitate efficient SDM.

Institutional arrangements for water reuse: assessing challenges for the transition to water circularity

Abstract Water scarcity and security drive attention to water reuse in policy and business. However, water reuse may generate new water loops and challenge water governance with new and different types of water, risks, involved actors, and responsibilities. These challenges demand robust institutional arrangements related to water governance. This article assesses the institutional arrangements associated with four case studies in Spain, Italy, Croatia, and Israel. The findings reveal that the more diverse the water uses and users, the more challenges and risks, particularly those associated with institutional arrangements such as quality standards, sanctions, and conflict prevention, are likely to emerge. The weaknesses of governance models and regulations to deal with changes, uncertainties, and public resistance call for special attention to the design of the institutional arrangements before the adoption. Independent of the type of technology adopted, governance may be improved by ensuring internal and external water monitoring; integrating water management with spatial concerns; improving training, expert engagement, and civil society awareness; and reducing water reuse costs. In addition, alternative models that guarantee the efficiency of governance in attaining objectives and assuring the participation of new water users in the management of water reuse loops may also improve governance.

Best practices for exposure model peer review – A SciPinion advisory panel report

The extent and rigor of peer review that a model undergoes during and after development influences the confidence of users and managers in model predictions. A process for determining the breadth and depth of peer review of exposure models was developed with input from a panel of exposure-modeling experts. This included consideration of the tiers and types of models (e.g., screening, deterministic, probabilistic, etc.). The experts recommended specific criteria be considered when evaluating the degree to which a model has been peer reviewed, including quality of documentation and the model peer review process (e.g., internal review with a regulatory agency by subject matter experts, expert review reports, formal Scientific Advisory Panels, and journal peer review). In addition, because the determination of the confidence level for an exposure model's predictions is related to the degree of evaluation the model has undergone, irrespective of peer review, the experts recommended the approach include judging the degree of model rigor using a set of specific criteria: (1) nature and quality of input data, (2) model verification, (3) model corroboration, and (4) model evaluation. Other key areas considered by the experts included recommendations for addressing model uncertainty and sensitivity, defining the model domain of applicability, and flags for when a model is used outside its domain of applicability. The findings of this expert engagement will help developers as well as users of exposure models have greater confidence in their application and yield greater transparency in the evaluation and peer review of exposure models.

Standardizing the Clinical Definition of Opioid Withdrawal in the Neonate

To standardize the clinical definition of opioid withdrawal in neonates to address challenges in clinical care, quality improvement, research, and public policy for this patient population. Between October and December 2020, we conducted 2 modified-Delphi panels using ExpertLens, a virtual platform for performing iterative expert engagement panels. Twenty clinical experts specializing in care for the substance-exposed mother-neonate dyad explored the necessity of key evidence-based clinical elements in defining opioid withdrawal in the neonate leading to a diagnosis of neonatal abstinence syndrome (NAS)/neonatal opioid withdrawal syndrome (NOWS). Expert consensus was assessed using descriptive statistics, the RAND/UCLA Appropriateness Method, and thematic analysis of participants' comments. Expert panels concluded the following were required for diagnosis: in utero exposure (known by history, not necessarily by toxicology testing) to opioids with or without the presence of other psychotropic substances, and the presence of at least two of the most common clinical signs characteristic of withdrawal (excessive crying, fragmented sleep, tremors, increased muscle tone, gastrointestinal dysfunction). Results indicate that both a known history of in utero opioid exposure and a distinct set of withdrawal signs are necessary to standardize a definition of neonatal withdrawal. Implementation of a standardized definition requires both patient engagement and a mother-neonate dyadic approach mindful of program and policy implications.

Specifying and optimizing robotic motion for visual quality inspection

Installation or even just modification of robot-supported production and quality inspection is a tedious process that usually requires full-time human expert engagement. The resulting parameters, e.g. robot velocities specified by an expert, are often subjective and produce suboptimal results. In this paper, we propose a new approach for specifying visual inspection trajectories based on CAD models of workpieces to be inspected. The expert involvement is required only to select – in a CAD system – the desired points on the inspection path along which the robot should move the camera. The rest of the approach is fully automatic. From the selected path data, the system computes temporal parametrization of the path, which ensures smoothness of the resulting robot trajectory for visual inspection. We then apply a new learning method for the optimization of robot speed along the specified path. The proposed approach combines iterative learning control and reinforcement learning. It takes a numerical estimate of image quality as input and produces the fastest possible motion that does not result in the degradation of image quality as output. In our experiments, the algorithm achieved up to 53% cycle time reduction from an initial, manually specified motion, without degrading the image quality. We show experimentally that the proposed algorithm achieves better results compared to some other policy learning approaches. The described approach is general and can be used with different types of learning and feedback signals. • Robot-supported visual quality inspection requires accurate position and velocity tuning. • Human-determined velocities are suboptimal and might reduce the quality of the captured images. • A new approach for specifying spatial and temporal course of visual inspection trajectories based on CAD models of workpieces to be inspected. • A new learning method for the optimization of robot speed along the specified path. • Real-world results show a significant improvement of inspection times with no image quality degradation.

Assessing Patient Preferences in Rare Diseases: Direct Preference Elicitation in the Rare Chronic Kidney Disease, Immunoglobulin A Nephropathy.

BackgroundPatient preference information is increasingly being used to inform decision making; however, further work is required to support the collection of preference information in rare diseases. This study illustrates the use of direct preference elicitation methods to collect preference data from small samples in the context of early decision making to inform the development of a product for the treatment of immunoglobulin A nephropathy.MethodAn interview-based swing weighting approach was used to elicit preferences from 40 patients in the US and China. Attributes were identified through a background review, expert engagement and patient focus groups. Participants completed a series of tasks that involved ranking, rating and scoring improvements in the attributes to obtain attribute swing weights and partial value functions. The preference results were then incorporated into a benefit-risk assessment simulation tool.ResultsParticipants placed the greatest value on avoiding end-stage renal/kidney disease. Similar weight was given to short-term quality-of-life improvements and avoiding infections. Treatment burden (number of vaccinations) received the least weight. Heterogeneity in preferences was also observed. Consistency tests did not identify statistically significant variation in preferences, and qualitative data suggested that the elicitation exercise was sensitive to participants’ interpretation of attributes and that participants were able to express their preferences.ConclusionDirect preference elicitation methods can be used to collect preference data from small samples. Further work should continue to test the validity of the estimate generated by such methods.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40271-021-00521-3.

Using a Collective Impact Framework to Implement Evidence-Based Strategies for Improving Maternal and Child Health Outcomes.

In 2016, the North Carolina Division of Public Health launched the Improving Community Outcomes for Maternal and Child Health program to invest in evidence-based programs to address three aims: improve birth outcomes, reduce infant mortality, and improve health outcomes for children 0 to 5 years old. Five grantees representing 14 counties were awarded 2 years of funding to implement one evidence-based strategy per aim using a collective impact framework, the principles of implementation science, and a health equity approach. Local health departments served as the backbone organization and provided ongoing support to grantees and helped them form community action teams (CATs) comprising implementation team members, community experts, and relevant stakeholders who met regularly. Focus groups with each grantee’s CAT were held during 2017 and 2019 to explore how CATs used a collective impact framework to implement their chosen evidence-based strategies. Results show that grantees made the most progress engaging diverse sectors in implementing a common agenda, continuous communication, and mutually reinforcing activities. Overall, grantees struggled with a shared measurement system but found that a formal tool to assess equity helped use data to drive decision making and program adaptations. Grantees faced logistical challenges holding regular CAT meetings and sustaining community expert engagement. Overtime, CATs cultivated community partnerships and multicounty collaboratives viewed cross-county knowledge sharing as an asset. Future collective impact initiatives should allow grantees more time upfront to form their CAT to plan for sustained community engagement before implementing programs and to incorporate a tool to center equity in their work.