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Introduction: The COVID-19 (COronaVIrus Disease-2019) pandemic highlighted the importance of assessing the rationales behind vaccine hesitancy for the containment of pandemics. In this nationwide study, representative of the Luxembourgish population, we identified hesitant groups from adolescence to late adulthood and explored motivations both for and against vaccination.

Methods: We combined data collected via online surveys for the CON-VINCE (COvid-19 National survey for assessing VIral spread by Non-affected CarriErs) study, 1865 respondents aged 18-84, and for the YAC (Young people And Covid-19) study, 3740 respondents aged 12-29.

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Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to predict future depression.

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Previous studies have shown an increase in blood pressure during the coronavirus disease 2019 (COVID-19) pandemic even among patients receiving antihypertensive treatment. This study aims to evaluate the association between equivalized annual household income and refraining from regular medical visits for hypertensive patients since the COVID-19 outbreak. We analyzed data from the Japan COVID-19 and Society Internet Survey (JACSIS), including 2832 hypertensive patients aged 20-79 years from the 2020 survey and at least one survey between 2021 and 2023.

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The need to understand subjective health has increased during the COVID-19 pandemic, given its substantial impact on lifestyle habits and perceptions. Thus, this study aimed to investigate the trends and association of subjective health with demographic and behavioral factors, primarily focusing on the change when the COVID-19 pandemic emerged. This study used data from the Korea Youth Risk Behavior Web-based Survey, comprising 1,190,468 adolescents aged 12-18 years (female, 48.

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Background: Artificial intelligence-driven clinical decision support systems (AI-CDSSs) are pivotal tools for doctors to improve diagnostic and treatment processes, as well as improve the efficiency and quality of health care services. However, not all doctors trust artificial intelligence (AI) technology, and many remain skeptical and unwilling to adopt these systems.

Objective: This study aimed to explore in depth the factors influencing doctors' willingness to adopt AI-CDSSs and assess the causal relationships among these factors to gain a better understanding for promoting the clinical application and widespread implementation of these systems.

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