ABSTRACT
BACKGROUND: COVID-19 pandemic has had a global major healthcare, social and economic impact. In present study we aim to adapt the Fear of COVID-19 Scale to Hungarian. MATERIALS AND METHODS: Forward-backward translation method was used to translate the English version of the scale to Hungarian. Participants were a convenience sample of 2175 university students and employees. The study was conducted between January 18th and February 12th 2021. The test battery included Hungarian versions of Fear of COVID-19 scale, short Beck Depression Inventory (BDI-H) and State-Trait Anxiety Inventory (STAI). RESULTS: The scale showed one-factor structure, the loadings on the factor were significant and strong (from .47 to .84). Internal consistency was very good (α = .84). Construct validity for the Fear of COVID-19 Scale was supported by significant and positive correlations with STAI (r = 0.402; p < 0.001) and BDI-H (r = 0.270; p < 0.001). CONCLUSION: The Hungarian version of Fear of COVID-19 Scale is a reliable and valid tool in assessing fear of coronavirus.
Subject(s)
COVID-19/epidemiology , COVID-19/psychology , Fear/psychology , Pandemics , Psychiatric Status Rating Scales/standards , Psychometrics/methods , SARS-CoV-2 , Adult , Aged , Aged, 80 and over , Anxiety/epidemiology , Anxiety/psychology , COVID-19/virology , Faculty/psychology , Female , Humans , Hungary/epidemiology , Male , Middle Aged , Reproducibility of Results , Students/psychology , Surveys and Questionnaires , Young AdultABSTRACT
Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.