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Violence against women in Covid-19 pandemic
Asszonyi, Anna ; Mareš, Jiří (advisor) ; Havigerová, Jana Marie (referee)
Name and surname of the author: Anna Asszonyi Institution: Charles University Faculty of Medicine in Hradec Králové Department of non-medical studies Title: Violence against women in Covid-19 pandemic Supervisor: prof. PhDr.Jiří Mareš, CSc. Number of pages: 87 Year od defense: 2023 The bachelor thesis, "Domestic violence against women in the time of the Covid-19 pandemic", deals in the theoretical part with the issue of domestic violence within families and the possibilities of helping women, its victims. It provides basic information on domestic violence, its forms and causes, dynamics and consequences, not only on women but also on children. A significant part of the bachelor thesis focuses on the necessity of professional approach and procedure of medical and non-medical personnel in the process of interdisciplinary cooperation towards victims of domestic violence. In the research part, the bachelor's thesis tries to delve into the issue of domestic violence from the perspective of five women of different ages who have experienced domestic violence, including during quarantine. Using narrative interviews, it records their life stories and maps the ways in which they themselves try to resolve their situation. Keywords: violence against women, Covid - 19, syndrom, quarantine, domestic abuse,...
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Exploring the Contribution of Isochrony-based Features to Computerized Assessment of Handwriting Disabilities
Gavenčiak, M. ; Zvončák, V. ; Mekyska, J. ; Šafárová, Katarína ; Čunek, Lukáš ; Urbánek, Tomáš ; Havigerová, Jana Marie ; Bednářová, Jiřina ; Galáž, Z. ; Mucha, J.
Approximately 30–60 % of the time children spend in school is associated with handwriting. However, up to 30 % of them experience handwriting disabilities (HD), which lead to a decrease in their academic performance. Current HD assessment methods are not unified and show signs of subjectivity which can lead to misdiagnosis. The aim of this paper is to propose a new approach to objective HD assessment based on the principle of movement isochrony. For this purpose, we used a database of 137 children attending a primary school, who performed a transcription and dictation task, and who were associated with a BHK (Concise Evaluation Scale for Children's Handwriting) score. Employing a machine learning model, we were able to estimate this score with 18 % error. An interpretation of the model suggests that the isochrony-based features could bring new benefits to the objective assessment of HD.
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