The Center for the Advancement of Team Science, Analytics, and Systems Thinking (CATALYST), College of Medicine, The Ohio State University, 700 Ackerman Road, Suite 4000, Columbus, OH, 43202, USA.
College of Medicine, HEALing Communities Study, The Ohio State University, Columbus, OH, USA.
J Community Health. 2024 Dec;49(6):1062-1072. doi: 10.1007/s10900-024-01377-y. Epub 2024 Jul 3.
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.
数据驱动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会遇到挑战。社区参与式数据科学（CEDS）模型为社区提供了一个目标导向、迭代的指南，通过数据大使与研究数据科学家合作。本研究介绍了 CEDS 在俄亥俄州 18 个县的阿片类药物流行研究中的应用案例，该研究是 HEALing 社区研究（HCS）的一部分。数据大使在授权社区联盟方面发挥了关键作用，通过 CEDS 的关键步骤将数据转化为行动，这些步骤包括：数据景观，确定社区中可用的数据；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；以及数据系统，包括门户和仪表板。在整个 CEDS 过程中，数据大使强调可持续的数据工作流程，支持在 HCS 之外继续进行数据参与。俄亥俄州的 CEDS 实施强调了建立关系、实施时机、了解社区数据偏好以及与社区合作时的灵活性的重要性。研究人员应考虑在基于社区的研究中实施 CEDS 并整合数据大使，以增强社区数据参与度，并推动数据驱动的干预措施，以改善公共卫生成果。
