Abstract：
Data-informed decision making is a critical goal for many community-based public health research initiatives. However, community partners often encounter challenges when interacting with data. The Community-Engaged Data Science (CEDS) model offers a goal-oriented, iterative guide for communities to collaborate with research data scientists through data ambassadors. This study presents a case study of CEDS applied to research on the opioid epidemic in 18 counties in Ohio as part of the HEALing Communities Study (HCS). Data ambassadors provided a pivotal role in empowering community coalitions to translate data into action using key steps of CEDS which included: data landscapes identifying available data in the community; data action plans from logic models based on community data needs and gaps of data; data collection/sharing agreements; and data systems including portals and dashboards. Throughout the CEDS process, data ambassadors emphasized sustainable data workflows, supporting continued data engagement beyond the HCS. The implementation of CEDS in Ohio underscored the importance of relationship building, timing of implementation, understanding communities\' data preferences, and flexibility when working with communities. Researchers should consider implementing CEDS and integrating a data ambassador in community-based research to enhance community data engagement and drive data-informed interventions to improve public health outcomes.
摘要：
数据知情决策是许多基于社区的公共卫生研究计划的关键目标。然而,社区合作伙伴在与数据交互时经常遇到挑战。社区参与数据科学(CEDS)模型提供了一个以目标为导向的、社区通过数据大使与研究数据科学家合作的迭代指南。作为健康社区研究（HCS）的一部分，本研究提供了CEDS应用于俄亥俄州18个县阿片类药物流行研究的案例研究。数据大使在授权社区联盟使用CEDS的关键步骤将数据转化为行动方面发挥了关键作用，这些步骤包括：数据景观识别社区中的可用数据；基于社区数据需求和数据差距的逻辑模型的数据行动计划；数据收集/共享协议；以及包括门户和仪表板在内的数据系统。在整个CEDS过程中，数据大使强调可持续的数据工作流程，支持HCS以外的持续数据参与。CEDS在俄亥俄州的实施强调了建立关系的重要性，实施时机，了解社区的数据偏好，与社区合作时的灵活性。研究人员应考虑实施CEDS，并在基于社区的研究中整合数据大使，以增强社区数据参与并推动数据知情干预措施，以改善公共卫生结果。
