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Mindfulness Intervention for Health Information Avoidance in Older Adults: Mixed Methods Study.
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School of Arts and Media, Wuhan College, Wuhan, China.
Background: The global aging population and rapid development of digital technology have made health management among older adults an urgent public health issue. The complexity of online health information often leads to psychological challenges, such as cyberchondria, exacerbating health information avoidance behaviors. These behaviors hinder effective health management; yet, little research examines their mechanisms or intervention strategies.
View Article and Find Full Text PDFFeasibility of a Mobile App-Based Cognitive-Behavioral Perinatal Skills Program: Protocol for Nonrandomized Pilot Trial.
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Psychiatry Department, Weill Cornell Medicine, New York, NY, United States.
Background: Mental illness is one of the top causes of preventable pregnancy-related deaths in the United States. There are many barriers that interfere with the ability of perinatal individuals to access traditional mental health care. Digital health interventions, including app-based programs, have the potential to increase access to useful tools for these individuals.
View Article and Find Full Text PDFHealth and Experiences During the COVID-19 Pandemic Among Children and Young People: Analysis of Free-Text Responses From the Children and Young People With Long COVID Study.
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Division of Surgery & Interventional Science, Faculty of Medical Sciences, University College London, London, United Kingdom.
Background: The literature is equivocal as to whether the predicted negative mental health impact of the COVID-19 pandemic came to fruition. Some quantitative studies report increased emotional problems and depression; others report improved mental health and well-being. Qualitative explorations reveal heterogeneity, with themes ranging from feelings of loss to growth and development.
View Article and Find Full Text PDFValue Propositions for Digital Shared Medication Plans to Boost Patient-Health Care Professional Partnerships: Co-Design Study.
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Department of Ambulatory Care, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
Background: Health authorities worldwide have invested in digital technologies to establish robust information exchange systems for improving the safety and efficiency of medication management. Nevertheless, inaccurate medication lists and information gaps are common, particularly during care transitions, leading to avoidable harm, inefficiencies, and increased costs. Besides fragmented health care processes, the inconsistent incorporation of patient-driven changes contributes to these problems.
View Article and Find Full Text PDFThe Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets.
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Institute of History and Ethics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Background: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients' gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.
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