Vaccination hesitancy (VH) is a phenomenon which increases the occurrence of vaccine-preventable diseases. The study tests the validity of the Multidimensional Vaccine Hesitancy Scale (MVHS) in the case of a sample of Romanian adults (n = 528; Meanage = 30.57). The latter filled in an online cross-sectional survey. The construct validity of MVHS was assessed by using confirmatory factor analysis (CFA), the reliability was calculated by using the internal consistency, and the convergent and discriminant validity was assessed by using the composite reliability (CR), and average variance extracted (AVE). The obtained model was invariant across gender. The structural equation model was designed for predictive validity by using the partial least square method (PLS-SEM) which analyses the relation between the MVHS dimensions and the vaccination willingness. The results show support for the 8-factor structure of the scale (χ2/df = 2.48; CFI = 0.95; RMSEA = 0.053). The Cronbach’s coefficients α > 0.70; McDonald’s ω > 0.70 and CR > 0.80 have very good values. The structural equation model shows that there are more dimensions of the scale which predict vaccination hesitancy in various types of vaccines—the main predictors remain the dimensions of health risk and healthy condition. The study’s conclusion led to the idea that the MVHS is suitable for medical practice and for research on the analysis of vaccination behaviours and intentions.
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http://dx.doi.org/10.3390/vaccines10101755 | DOI Listing |
Vaccine
January 2025
The Department of Nursing, The Jerusalem College of Technology, Israel.
Objectives: This study aims to investigate the factors contributing to the underutilization of childhood and school-age immunizations among parents within the Jewish Ultra-Orthodox community in Israel. It identifies socio-demographic, attitudinal, and belief-related risk factors that affect vaccination decisions.
Study Design: A cross-sectional study was conducted involving 369 Jewish Orthodox parents in Israel, using structured questionnaires distributed through various community channels.
Mol Biomed
January 2025
Department of Artificial Intelligence and Machine Learning, Faculty of Engineering and Technology, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, Maharashtra, 442001, India.
Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates the multidimensional role of AI in the pandemic, which arises as a global health crisis, and its role in preparedness and responses, ranging from enhanced epidemiological modelling to the acceleration of vaccine development. The confluence of AI technologies has guided us in a new era of data-driven decision-making, revolutionizing our ability to anticipate, mitigate, and treat infectious illnesses.
View Article and Find Full Text PDFBMC Glob Public Health
April 2024
College of Health Sciences, University of Liberia, Monrovia, Liberia.
Background: The burden of the COVID-19 pandemic in terms of morbidity and mortality differentially affected populations. Between and within populations, behavior change was likewise heterogeneous. Factors influencing precautionary behavior adoption during COVID-19 have been associated with multidimensional aspects of risk perception; however, the influence of lived experiences during other recent outbreaks on behavior change during COVID-19 has been less studied.
View Article and Find Full Text PDFiScience
December 2024
Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
Recent studies have demonstrated the significance of hyperbolic geometry in uncovering low-dimensional structure within complex hierarchical systems. We developed a Bayesian formulation of multi-dimensional scaling (MDS) for embedding data in hyperbolic spaces that allows for a principled determination of manifold parameters such as curvature and dimension. We show that only a small amount of data are needed to constrain the manifold, the optimization is robust against false minima, and the model is able to correctly discern between Hyperbolic and Euclidean data.
View Article and Find Full Text PDFVirus Evol
November 2024
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States.
Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge.
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