While the privacy implications of social robots have been increasingly discussed and privacy-sensitive robotics is becoming a research field within human-robot interaction, little empirical research has investigated privacy concerns about robots and the effect they have on behavioral intentions. To address this gap, we present the results of an experimental vignette study that includes antecedents from the privacy, robotics, technology adoption, and trust literature. Using linear regression analysis, with the privacy-invasiveness of a fictional but realistic robot as the key manipulation, we show that privacy concerns affect use intention significantly and negatively. Compared with earlier work done through a survey, where we found a robot privacy paradox, the experimental vignette approach allows for a more realistic and tangible assessment of respondents' concerns and behavioral intentions, showing how potential robot users take into account privacy as consideration for future behavior. We contextualize our findings within broader debates on privacy and data protection with smart technologies.
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http://dx.doi.org/10.3389/frobt.2021.627958 | DOI Listing |
BMC Complement Med Ther
January 2025
Clinic of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany.
Background: Patient engagement (PE) in clinical trials has gained importance yet remains uncommon, particularly in patients with mild cognitive impairment (MCI), a critical precursor to Alzheimer's disease (AD). Cannabidiol (CBD) shows potential in slowing MCI progression due to its neuroprotective and anti-inflammatory properties. In CBD research, PE is underutilized too.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Institut Curie, CNRS UMR168, PSL University, Sorbonne University, Paris, 75005, France.
Generating synthetic data from medical records is a complex task intensified by patient privacy concerns. In recent years, multiple approaches have been reported for the generation of synthetic data, however, limited attention was given to jointly evaluate the quality and the privacy of the generated data. The quality and privacy of synthetic data stem from multivariate associations across variables, which cannot be assessed by comparing univariate distributions with the original data.
View Article and Find Full Text PDFInt Dent J
January 2025
Department of Endodontics, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.
Introduction: Artificial intelligence (AI), including its subfields of machine learning and deep learning, is a branch of computer science and engineering focused on creating machines capable of tasks requiring human-like intelligence, such as visual perception, decision-making, and natural language processing. AI applications have become increasingly prevalent in dental medicine, generating high expectations as well as raising ethical and practical concerns.
Methods: This critical review evaluates the current applications of AI in dentistry, identifying key perspectives, challenges, and limitations in ongoing AI research.
J Clin Epidemiol
January 2025
Univ Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Centre d'investigation clinique de Rennes (CIC1414), Rennes, France; Institut Universitaire de France (IUF), Paris, France.
Guided by the FAIR principles (Findable, Accessible, Interoperable, Reusable), responsible data sharing requires well-organized, high-quality datasets. However, researchers often struggle with implementing Data Management and Sharing Plans (DMSPs) due to lack of knowledge on how to do this, time constraints, legal, technical and financial challenges, particularly concerning data ownership and privacy. While patients support data sharing, researchers and funders may hesitate, fearing the loss of intellectual property or competitive advantage.
View Article and Find Full Text PDFChild Abuse Negl
January 2025
School of Public Health, University of the Witwatersrand, Johannesburg, South Africa; Gillings School of Global Public Health, University North Carolina, USA.
Background: In South Africa, one in five adolescents experience pregnancy and face heightened rates of interpersonal violence and mental health challenges. Yet, few interventions are tailored to them.
Methods: 28 pregnant adolescents reporting past year intimate partner violence and/or non-partner rape were purposively recruited in antenatal clinics in Johannesburg to attend a 6-session arts-based intervention, delivered by 4 graduate art therapy students alongside clinical supervision.
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