Introduction: The fear of COVID-19 scale (FCV-19S) is used to screen for symptoms of anxiety and depression related to COVID-19 in the general population; it consists of seven questions with Likert-type answers (1-5). Our objective was to validate FCV-19S Spanish version in the Mexican general population.
Material And Methods: Analytical, cross-sectional design. Three-hundred and six subjects from the general population were included during 2020 after having signed informed consent. Barlett and Kaiser-Meyer-Olkin (KMO) sphericity tests were applied. Reliability was calculated with Cronbach's alpha, and external validity, using the Hospital Anxiety and Depression Scale and Pearson's correlation coefficient for retest.
Results: The general population sample included 306 participants; 64.4 % were women (n = 197), mean age was 32 years (18-68). We obtained a KMO = 0.848, internal consistency with Cronbach's alpha = 0.870 (95% CI: 0.848-0.891), a rho coefficient of 0.508 (p < 0.001) and external validity of 0.151 (p = 0.008). Confirmatory analysis showed: χ = 22.802 (df = 13) with CMIN-DF = 1.900 (p ≤ 0.001), GFI = 0.972, CFI = 0.901, RMSEA = 0.062 (90% CI: 0.019-0.100) and TLI = 0.827.
Conclusions: According to our findings, the scale shows adequate psychometric properties: reliability, internal consistency, correlation with subsequent measurements and convergence validity, for initial screening of the Mexican general population.
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http://dx.doi.org/10.24875/GMM.M21000618 | DOI Listing |
J Ovarian Res
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Departments of Endocrinology, Sheri Kashmir Institute of Medical Sciences, Srinagar, J&K, India.
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National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
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Department of Neurology, the First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
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QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany.
Background: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training cohort and perform worse at new hospitals. Thus, external validation is a critical - but frequently overlooked - step to establish the reliability of predicted risk scores to translate them into clinical practice.
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