Background: Recruiting older adults (OA) into research is challenging.
Objective: To assess the feasibility of using two crowdsourcing platforms, Amazon's Mechanical Turk (MTurk) and Prolific Academic (ProA), as efficient and low-cost venues for recruiting survey participants aged 65 and older.
Methods: We developed an online survey to investigate and compare the demographics, technology use, and motivations for research participation of OA on MTurk and ProA. Qualitative responses, response time, word count, and recruitment costs were analyzed.
Results: We recruited 97 OA survey participants on both MTurk and ProA. Participants were similar in terms ofdemographics, technology usage, and motivations for participation (topic interest and payment).
Conclusion: Both crowdsourcing platforms are useful for rapid and low-cost recruitment of OA. The OA recruitment process was more efficient with ProA. Crowdsourcing platforms are potential sources of OA research participants; however, the pool is limited to generally healthy, technologically active, and well-educated older adults.
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Individuals who struggle to regulate their gaming involvement, particularly those with gaming disorder, often report strong subjective urges to play games. Desire thinking has been proposed to be an active driver of urge, and therefore disrupting desire thinking processes may reduce urges to play. Detached mindfulness, a meta-cognitive therapy technique, is a candidate option for reducing desire thinking, but the available research in relation to gaming is limited.
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Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, 1150 NW 14th Street, Miami, FL 33136, USA.
Background: Reproductive health technology has evolved significantly since the introduction of in vitro fertilization in 1978, enhancing the possibility of conceiving children at later stages in life. Despite these advancements, there remains a critical gap in fertility knowledge among young adults, as demonstrated by recent studies. This gap is compounded by the growing influence of social media on health information, where misinformation can distort public understanding of fertility-related issues.
View Article and Find Full Text PDFInt J Med Inform
December 2024
Biomedical Informatics Center, Department of Public Health Sciences, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA. Electronic address:
Objectives: This scoping review aims to clarify the definition and trajectory of citizen-led scientific research (so-called citizen science) within the healthcare domain, examine the degree of integration of machine learning (ML) and the participation levels of citizen scientists in health-related projects.
Materials And Methods: In January and September 2024 we conducted a comprehensive search in PubMed, Scopus, Web of Science, and EBSCOhost platform for peer-reviewed publications that combine citizen science and machine learning (ML) in healthcare. Articles were excluded if citizens were merely passive data providers or if only professional scientists were involved.
Successful identification of domestic minor sex trafficking (DMST) remains challenging. Laypersons could play a significant role in identifying victims, but only if laypersons recognize trafficking situations as such and do not incorrectly attribute responsibility to victims. In the current study, we examined laypersons' perceptions of situations highly suggestive of DMST.
View Article and Find Full Text PDFPLoS One
December 2024
Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, United States of America.
Crowdsource platforms have been used to study a range of perceptual stimuli such as the graphical perception of scatterplots and various aspects of human color perception. Given the lack of control over a crowdsourced participant's experimental setup, there are valid concerns on the use of crowdsourcing for color studies as the perception of the stimuli is highly dependent on the stimulus presentation. Here, we propose that the error due to a crowdsourced experimental design can be effectively averaged out because the crowdsourced experiment can be accommodated by the Thurstonian model as the convolution of two normal distributions, one that is perceptual in nature and one that captures the error due to variability in stimulus presentation.
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