Ranard Benjamin L, Ha Yoonhee P, Meisel Zachary F, Asch David A, Hill Shawndra S, Becker Lance B, Seymour Anne K, Merchant Raina M
Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Blockley Hall, Philadelphia, PA, 19104, USA.
J Gen Intern Med. 2014 Jan;29(1):187-203. doi: 10.1007/s11606-013-2536-8. Epub 2013 Jul 11.
Crowdsourcing research allows investigators to engage thousands of people to provide either data or data analysis. However, prior work has not documented the use of crowdsourcing in health and medical research. We sought to systematically review the literature to describe the scope of crowdsourcing in health research and to create a taxonomy to characterize past uses of this methodology for health and medical research.
PubMed, Embase, and CINAHL through March 2013.
Primary peer-reviewed literature that used crowdsourcing for health research.
Two authors independently screened studies and abstracted data, including demographics of the crowd engaged and approaches to crowdsourcing.
Twenty-one health-related studies utilizing crowdsourcing met eligibility criteria. Four distinct types of crowdsourcing tasks were identified: problem solving, data processing, surveillance/monitoring, and surveying. These studies collectively engaged a crowd of >136,395 people, yet few studies reported demographics of the crowd. Only one (5 %) reported age, sex, and race statistics, and seven (33 %) reported at least one of these descriptors. Most reports included data on crowdsourcing logistics such as the length of crowdsourcing (n = 18, 86 %) and time to complete crowdsourcing task (n = 15, 71 %). All articles (n = 21, 100 %) reported employing some method for validating or improving the quality of data reported from the crowd.
Gray literature not searched and only a sample of online survey articles included.
Utilizing crowdsourcing can improve the quality, cost, and speed of a research project while engaging large segments of the public and creating novel science. Standardized guidelines are needed on crowdsourcing metrics that should be collected and reported to provide clarity and comparability in methods.
众包研究使研究人员能够吸引数千人提供数据或进行数据分析。然而,此前的研究尚未记录众包在健康和医学研究中的应用情况。我们试图系统地回顾文献,以描述众包在健康研究中的应用范围,并创建一个分类法来描述该方法在健康和医学研究中的过往应用。
截至2013年3月的PubMed、Embase和CINAHL数据库。
使用众包进行健康研究的同行评审的原始文献。
两位作者独立筛选研究并提取数据,包括参与人群的人口统计学信息和众包方法。
21项利用众包的健康相关研究符合入选标准。确定了四种不同类型的众包任务:问题解决、数据处理、监测/监控和调查。这些研究共吸引了超过136,395人参与,但很少有研究报告参与人群的人口统计学信息。只有一项研究(5%)报告了年龄、性别和种族统计数据,七项研究(33%)报告了至少其中一项描述信息。大多数报告包含众包后勤方面的数据,如众包时长(n = 18,86%)和完成众包任务的时间(n = 15,71%)。所有文章(n = 21,100%)都报告采用了某种方法来验证或提高从人群中报告的数据质量。
未检索灰色文献,仅纳入了在线调查文章样本。
利用众包可以提高研究项目的质量、成本和速度,同时吸引大量公众参与并创造新的科学成果。需要制定关于应收集和报告的众包指标的标准化指南,以确保方法的清晰度和可比性。