Online participant recruitment ("crowdsourcing") platforms are increasingly being used for research studies. While such platforms can rapidly provide access to large samples, there are concomitant concerns around data quality. Researchers have studied and demonstrated means to reduce the prevalence of low-quality data from crowdsourcing platforms, but approaches to doing so often involve rejecting work and/or denying payment to participants, which can pose ethical dilemmas. We write this essay as an associate professor and two institutional review board (IRB) directors to provide a perspective on the competing interests of participants/workers and researchers and to propose a checklist of steps that we believe may support workers' agency on the platform and lessen instances of unfair consequences to them while enabling researchers to definitively reject lower-quality work that might otherwise reduce the likelihood of their studies producing true results. We encourage further, explicit discussion of these issues among academics and among IRBs.
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http://dx.doi.org/10.1002/eahr.500217 | DOI Listing |
J Autism Dev Disord
January 2025
Institutes for Behavior Resources, Inc, 2104 Maryland Ave., Baltimore, MD, 21218, USA.
We aimed to compare sleep problems in autistic and non-autistic adults with co-occurring depression and anxiety. The primary research question was whether autism status influences sleep quality, after accounting for the effects of depression and anxiety. We hypothesized that autistic adults would report higher levels of depression, anxiety, and sleep problems compared to non-autistic adults, after controlling for these covariates.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Psychology, Crean College of Health and Behavioral Sciences, Chapman University, Orange, California, United States of America.
Accumulating evidence indicates that unpredictable signals in early life represent a unique form of adverse childhood experiences (ACEs) associated with disrupted neurodevelopmental trajectories in children and adolescents. The Questionnaire of Unpredictability in Childhood (QUIC) was developed to assess early life unpredictability [1], encompassing social, emotional, and physical unpredictability in a child's environment, and has been validated in three independent cohorts. However, the importance of identifying ACEs in diverse populations, including non-English speaking groups, necessitates translation of the QUIC.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Biomedical Informatics & Data Science Section, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Background: Mobile devices offer an emerging opportunity for research participants to contribute person-generated health data (PGHD). There is little guidance, however, on how to best report findings from studies leveraging those data. Thus, there is a need to characterize current reporting practices so as to better understand the potential implications for producing reproducible results.
View Article and Find Full Text PDFJ Speech Lang Hear Res
January 2025
Department of Otolaryngology-Head and Neck Surgery, New York University Grossman School of Medicine, NY.
Purpose: Most auditory-perceptual voice research utilizes the judgments of trained listeners rather than everyday listeners with no previous training in speech pathology. Online crowdsourcing of behavioral data from untrained participants is rapidly increasing in popularity but has yet to be a common procedure for auditory-perceptual studies of the voice. The objective of this pilot study was to assess the functionality of this model for judgments of voice by using an online experiment platform to replicate a lab-based, voice-specific age estimation study.
View Article and Find Full Text PDFLearn Health Syst
January 2025
Bioethics Research Center, Division of General Medical Sciences, Department of Medicine Washington University School of Medicine St. Louis Missouri USA.
Objectives: Patient engagement is critical for the effective development and use of artificial intelligence (AI)-enabled tools in learning health systems (LHSs). We adapted a previously validated measure from pediatrics to assess adults' openness and concerns about the use of AI in their healthcare.
Study Design: Cross-sectional survey.
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