A clinical study is often necessary for exploring important research questions; however, this approach is sometimes time and money consuming. Another extreme approach, which is to collect and aggregate opinions from crowds, provides a result drawn from the crowds' past experiences and knowledge. To explore a solution that takes advantage of both the rigid clinical approach and the crowds' opinion-based approach, we design a framework that exploits crowdsourcing as a part of the research process, whereby crowd workers serve as if they were a scientist conducting a "pseudo" prospective study. This study evaluates the feasibility of the proposed framework to generate hypotheses on a specified topic and verify them in the real world by employing many crowd workers. The framework comprises two phases of crowd-based workflow. In Phase 1 - the hypothesis generation and ranking phase - our system asks workers two types of questions to collect a number of hypotheses and rank them. In Phase 2 - the hypothesis verification phase - the system asks workers to verify the top-ranked hypotheses from Phase 1 by implementing one of them in real life. Through experiments, we explore the potential and limitations of the framework to generate and evaluate hypotheses about the factors that result in a good night's sleep. Our results on significant sleep quality improvement show the basic feasibility of our framework, suggesting that crowd-based research is compatible with experts' knowledge in a certain domain.
翻译:临床研究往往对于探索重要的研究问题十分必要;然而,这一方法有时需要时间和金钱的消耗。另一种极端的方法是收集和综合人群的意见,从人群过去的经验和知识中得出结果。为了探索一种既利用僵硬的临床方法又利用人群意见的方法的解决办法,我们设计了一个框架,利用众包作为研究过程的一部分,使人群工人成为进行“假想”未来研究的科学家。本研究评估了拟议框架的可行性,以生成关于特定主题的假设并通过雇用许多人群工人在现实世界中核实这些假设。框架包括基于人群的工作流程的两个阶段。在第一阶段——假设的生成和排名阶段——我们的制度要求工人收集若干假想和排序。在第二阶段——假设核查阶段——系统要求工人通过在现实生活中实施其中的假设来核实第一阶段的顶级假设。通过实验,我们探索了框架的潜力和局限性,以生成和评估基于人群工作流程的两个阶段。 在第一阶段——假设的生成和排位阶段——我们系统要求工人收集一些假想和排档。在梦中,我们的基本研究结果显示我们睡眠质量的可靠结果,在晚上显示一个好的实验室中,一个好的实验显示我们的基本结果。