Consumers' prescription drug decisions are affected by a number of structural, psychological, and health communication source variables. To provide a theoretically sound and comprehensive prescription medication decision engagement framework, this study integrated Andersen's Health Service Use Model to address contextual and structural factors, the Health Belief Model (HBM) to examine psychological factors, and extant research on the influence of various health communication sources to explain the prescription drug decision engagement mechanisms of health information-seeking intention, prescription drug-seeking intent, and prescription-seeking behavior. Employing survey methodology, the framework was tested using a sample of U.S. adult consumers ( = 370). Results demonstrated the utility of the integrated model for explaining consumers' participation in their prescription decisions. Specifically, consumers' assessment of target health behaviors and the use of various health communication sources significantly improved the explanatory power of the decision engagement model beyond structural factors. The results impart valuable theoretical contributions and have the potential to guide public health interventions related to consumers' prescription drug decisions.
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http://dx.doi.org/10.1080/10410236.2018.1545336 | DOI Listing |
JMIR Form Res
January 2025
Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI, United States.
Background: Telehealth approaches can address health care access barriers and improve care delivery in resource-limited settings around the globe. Yet, telehealth adoption in Africa has been limited, due in part to an insufficient understanding of effective strategies for implementation.
Objective: This study aimed to conduct a multi-level formative evaluation identifying barriers and facilitators for implementing telehealth among health service providers and patients in Central Uganda.
Int J Audiol
January 2025
Department of Otorhinolaryngology, Radboud University Medical Center, Nijmegen, The Netherlands.
Objective: To assess the impact of cochlear implantation (CI) and speech perception outcomes on the quality of life (QoL) of adult CI users and their communication partners (CP) one-year post-implantation.
Design: This research is part of a prospective multicenter study in The Netherlands, called SMILE (Societal Merit of Intervention for hearing Loss Evaluation).
Study Sample: Eighty adult CI users completed speech perception testing and the Nijmegen Cochear Implant Questionnaire (NCIQ).
Scand J Trauma Resusc Emerg Med
January 2025
Department of Emergency Medicine and Pre-Hospital Services, St. Olav's University Hospital, Trondheim, Norway.
Background: First responders exist in several countries and have been a prehospital emergency medical resource in Norwegian municipalities since 2010. However, the Norwegian system has not yet been studied. The aim of this study was to describe the first responder system in Central Norway and how it is used as a supplement to emergency medical services (EMS).
View Article and Find Full Text PDFBMC Med Ethics
January 2025
Ethics and Work Research Unit, Institute of Advanced Studies (EPHE), Paris, France.
Aim: To carry out a detailed study of existing positions in the French public of the acceptability of refusing treatment because of alleged futility, and to try to link these to people's age, gender, and religious practice.
Method: 248 lay participants living in southern France were presented with 16 brief vignettes depicting a cancer patient at the end of life who asks his doctor to administer a new cancer treatment he has heard about. Considering that this treatment is futile in the patient's case, the doctor refuses to prescribe it.
Sci Rep
January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
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