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The Associations between Knowledge and Behaviours Related to Touch Screens and Microbiological Threats among IT Students’

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  • Dorota Oszutowska-Mazurek

    (Department of Histology and Embryology, Pomeranian Medical University, Powstancow Wielkopolskich 72 Ave., 70111 Szczecin, Poland
    These authors contributed equally to this work.)

  • Jaroslaw Fastowicz

    (Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology Szczecin, 26. Kwietnia 10 St., 71126 Szczecin, Poland
    These authors contributed equally to this work.)

  • Przemyslaw Mazurek

    (Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology Szczecin, 26. Kwietnia 10 St., 71126 Szczecin, Poland
    These authors contributed equally to this work.)

Abstract

Current issue like the COVID–19 pandemic show how elementary knowledge and hygiene behaviours are important for ordinary people. Microbiological hazards, not just viruses, can be transmitted in various ways through touch screens. For ordinary users, there is a wide range of behaviours that affect the ability to transfer microbial hazards (viruses, bacteria and fungi). The purpose of the paper is to analyse the association between knowledge and behaviour of touch screen users based on surveys. This paper presents selected results of a survey conducted at the end of 2019 (pre–COVID–19 survey). The survey was conducted on a group of 172 IT school students. The relationship between responses using a 2D linear model regression and clustering is used. Most respondents believe that bacteria were more common than viruses on touch screens. The respondents declare altruism in terms of a greater willingness to lend their smartphone, rather than to use someone else’s. An interesting result is that respondents often lend their smartphone to others, while being aware that viruses or bacteria are present on the touch screens. The results can be used in terms of changes in the education process of smartphone users in relation to microbiological hazards.

Suggested Citation

  • Dorota Oszutowska-Mazurek & Jaroslaw Fastowicz & Przemyslaw Mazurek, 2021. "The Associations between Knowledge and Behaviours Related to Touch Screens and Microbiological Threats among IT Students’," IJERPH, MDPI, vol. 18(17), pages 1-17, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9269-:d:627816
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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
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