This paper is in the following e-collection/theme issue:

Viewpoints and Perspectives (625) Security in Digital Health (55) Gestational Diabetes (43) Crowdsourcing and Mechanical Turks (103) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1009)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58450, first published .
Identifying Fraudulent Responses in a Study Exploring Delivery Options for Pregnancies Impacted by Gestational Diabetes: Lessons Learned From a Web-Based Survey

Identifying Fraudulent Responses in a Study Exploring Delivery Options for Pregnancies Impacted by Gestational Diabetes: Lessons Learned From a Web-Based Survey

Identifying Fraudulent Responses in a Study Exploring Delivery Options for Pregnancies Impacted by Gestational Diabetes: Lessons Learned From a Web-Based Survey

Authors of this article:

Emma Ruby1, 2 Author Orcid Image ;   Serine Ramlawi2 Author Orcid Image ;   Alexa Clare Bowie2 Author Orcid Image ;   Stephanie Boyd2 Author Orcid Image ;   Alysha Dingwall-Harvey2 Author Orcid Image ;   Ruth Rennicks White2, 3 Author Orcid Image ;   Darine El-Chaâr2, 3, 4, 5 Author Orcid Image ;   Mark Walker2, 3, 4, 5, 6 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Willard-Grace R, Klima T, Dedhia M, Lo E, Nisnevich A, Gray A, Henry H. Selecting fraud detection methods: Using epidemiological test diagnostics to evaluate responses in a cross-sectional questionnaire study (Preprint). Journal of Medical Internet Research 2025 View

Conference Proceedings

  1. Shahania S, Spiliopoulou M, Broneske D. Proceedings of the ACM Collective Intelligence Conference. Gotta Catch 'Em All... Or Not?: How LLMs Bypass Traditional Checks & Mimic Human Response Behavior in Web Surveys View