Johnson Shoyama Graduate School of Public Policy, University of Regina, Regina, SK, Canada.
JMIR Mhealth Uhealth. 2019 Aug 30;7(8):e14056. doi: 10.2196/14056.
Citizen science enables citizens to actively contribute to all aspects of the research process, from conceptualization and data collection, to knowledge translation and evaluation. Citizen science is gradually emerging as a pertinent approach in population health research. Given that citizen science has intrinsic links with community-based research, where participatory action drives the research agenda, these two approaches could be integrated to address complex population health issues. Community-based participatory research has a strong record of application across multiple disciplines and sectors to address health inequities. Citizen science can use the structure of community-based participatory research to take local approaches of problem solving to a global scale, because citizen science emerged through individual environmental activism that is not limited by geography. This synergy has significant implications for population health research if combined with systems science, which can offer theoretical and methodological strength to citizen science and community-based participatory research. Systems science applies a holistic perspective to understand the complex mechanisms underlying causal relationships within and between systems, as it goes beyond linear relationships by utilizing big data-driven advanced computational models. However, to truly integrate citizen science, community-based participatory research, and systems science, it is time to realize the power of ubiquitous digital tools, such as smartphones, for connecting us all and providing big data. Smartphones have the potential to not only create equity by providing a voice to disenfranchised citizens but smartphone-based apps also have the reach and power to source big data to inform policies. An imminent challenge in legitimizing citizen science is minimizing bias, which can be achieved by standardizing methods and enhancing data quality-a rigorous process that requires researchers to collaborate with citizen scientists utilizing the principles of community-based participatory research action. This study advances SMART, an evidence-based framework that integrates citizen science, community-based participatory research, and systems science through ubiquitous tools by addressing core challenges such as citizen engagement, data management, and internet inequity to legitimize this integration.
公民科学使公民能够积极参与研究过程的各个方面，从概念化和数据收集，到知识转化和评估。公民科学正在逐渐成为人口健康研究中的一种相关方法。鉴于公民科学与以参与式行动推动研究议程为特点的社区为基础的研究有着内在联系，这两种方法可以整合起来解决复杂的人口健康问题。社区为基础的参与性研究在多个学科和部门得到了广泛应用，以解决健康不平等问题，有着良好的应用记录。公民科学可以利用社区为基础的参与性研究的结构，将地方解决问题的方法推广到全球范围，因为公民科学是通过不受地理限制的个人环境行动主义而产生的。如果将系统科学与之结合，这种协同作用对人口健康研究具有重要意义，因为系统科学可以为公民科学和社区为基础的参与性研究提供理论和方法上的优势。系统科学采用整体观点来理解系统内部和系统之间因果关系的复杂机制，它超越了线性关系，利用大数据驱动的先进计算模型。然而，要真正整合公民科学、社区为基础的参与性研究和系统科学，现在是时候认识到无处不在的数字工具（如智能手机）的力量了，这些工具可以将我们所有人连接起来，并提供大数据。智能手机不仅有潜力通过为被剥夺权利的公民提供发言权来创造公平，而且基于智能手机的应用程序也有能力获取大数据，为政策提供信息。使公民科学合法化的一个紧迫挑战是最大限度地减少偏见，这可以通过标准化方法和提高数据质量来实现，这是一个需要研究人员与公民科学家合作，利用社区为基础的参与性研究行动原则的严格过程。本研究通过解决公民参与、数据管理和互联网不平等核心挑战，提出了 SMART，这是一个基于证据的框架，通过无处不在的工具整合了公民科学、社区为基础的参与性研究和系统科学，从而使这种整合合法化。
