Information Security Education and Self-perception of Privacy Protection Risk in Mobile Web in Obstetrics Students from Peru

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Abstract

The objective of the study was to determine the information security education topics developed in the training of obstetrics students and their relationship with the self-perception of privacy protection risk in mobile web during the COVID-19 pandemic at the Santiago Antúnez of Mayolo National University (UNASAM) (Huaraz-Peru). A correlational cross-sectional investigation was developed, with 164 obstetric students. The information was collected through a questionnaire applied online between November and December 2020, having determined its validity and reliability. The Chi squared statistical test (p < 0.05) was used, while the information processing was carried out using the SPSS program. It was determined that 61.6% of obstetric students perceived a high risk in the protection of their privacy in mobile web during the development of their activities in the academic cycle 2020-I. Likewise, it was evidenced that the majority of students stated that they had not developed the topics consulted with regard to information security education during their virtual studies in obstetric, especially with regard to the recommendations for the use of passwords (83.5%), privacy protection strategies (81.1%) and data management through the creation of backups (79.9%), showing a statistically significant relationship with the self-perception of privacy protection risk in mobile web (p < 0.05). It was concluded that the low development of information security education topics in the training of obstetric students is related to the self-perception of high risk in the privacy protection in mobile web during the COVID-19 pandemic.

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APA

Olaza-Maguiña, A. F., & De La Cruz-Ramirez, Y. M. (2021). Information Security Education and Self-perception of Privacy Protection Risk in Mobile Web in Obstetrics Students from Peru. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12814 LNCS, pp. 32–43). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-83164-6_3

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