Goddard Kelsey S, Hall Jean P, Kurth Noelle K
University of Kansas, Institute for Health and Disability Policy Studies (KU-IHDPS), 1000 Sunnyside Ave., Room 1052, Lawrence, KS, 66045, United States.
Disabil Health J. 2025 Apr 29:101843. doi: 10.1016/j.dhjo.2025.101843.
Disability-focused research is vital for informing policies and services that address the unique needs of people with disabilities. However, survey fraud poses a growing threat to the integrity of such research. Fraudulent responses, often facilitated by bots or scammers, disproportionately impact studies with small sample sizes, where even minimal distortion can significantly skew findings and misinform policy decisions. Compounding the issue, traditional fraud detection mechanisms, such as CAPTCHA tasks and automated response-time analysis, often exclude legitimate participants, particularly those who rely on assistive technologies or face accessibility barriers. This commentary examines the recruitment paradox inherent in disability research, where inclusive participation incentives inadvertently invite fraud while restrictive measures risk excluding genuine respondents. To address these dual challenges, we propose adaptive fraud detection tools, participatory design approaches, and equitable incentive structures that balance inclusivity with data integrity. These strategies advance robust, representative findings to support effective and equitable policy development.
以残疾为重点的研究对于为满足残疾人独特需求的政策和服务提供信息至关重要。然而，调查欺诈对这类研究的完整性构成了日益严重的威胁。欺诈性回答通常由机器人或诈骗者促成，对小样本量的研究影响尤其大，在这类研究中，即使是最小程度的失真也可能严重扭曲研究结果并误导政策决策。使问题更加复杂的是，传统的欺诈检测机制，如验证码任务和自动响应时间分析，往往会排除合法参与者，特别是那些依赖辅助技术或面临无障碍障碍的参与者。本评论探讨了残疾研究中固有的招募悖论，即包容性的参与激励措施无意中引发了欺诈行为，而限制性措施则有可能排除真正的受访者。为应对这双重挑战，我们提出了适应性欺诈检测工具、参与式设计方法和公平的激励结构，以在包容性与数据完整性之间取得平衡。这些策略推进了有力、有代表性的研究结果，以支持有效和公平的政策制定。
