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Updated: Sep 11, 2025

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机器人和假参与者:使用在线参与者招聘方法确保有效和可靠的数据收集
Roseline Jean Louis1, Lisa M Thompson1
1Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
International journal of social research methodology
|August 11, 2025
概括
在线招聘健康研究面临欺诈风险. 这项研究提出了一个分层的瑞士奶酪模型,以防止参与者欺诈,确保数字时代研究的可靠数据.
科学领域:
- 健康科学 卫生科学 卫生科学
- 临床研究 临床研究
- 数字健康数字健康
背景情况:
- 在线招聘为健康科学研究提供了广泛的覆盖范围,但引入了机器人干扰和欺诈参与等风险.
- 在使用在线招聘方法时,维护数据完整性对于研究结果的有效性和可靠性至关重要.
研究的目的:
- 为应对在线参与者招聘在健康科学研究中的挑战.
- 提出一个新的框架,瑞士奶酪模型研究参与者欺诈预防,以减轻在线招聘中的欺诈行为.
主要方法:
- 一个用美国各地的在线招聘方法进行的病例控制研究.
- 调整理性的瑞士奶酪模型,以制定预防欺诈的战略.
- 在线招聘十项具体预防和验证措施的说明.
主要成果:
- 在线招聘扩大了参与者访问,但需要强大的欺诈预防策略.
- 拟议的瑞士奶酪模型提供了一个结构化,分层的方法,以尽量减少参与者欺诈.
- 实施设计的招聘媒体,薪酬,审查和数据验证是关键.
结论:
- 多层次的方法对于确保在线健康研究招聘数据完整性至关重要.
- 研究参与者欺诈预防的瑞士奶酪模型为研究人员提供了一个实用的框架.
- 通过这些措施,在数字时代提高了研究结果的有效性和可靠性.
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