Browsing by Author "Solonskyi Andrii"

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    PROGNOSTIC ASSESSMENT OF STRESS-RELATED FACTORS IN HEALTHCARE WORKERS DURING THE COVID-19 PANDEMIC
    (Medicinska naklada, 2022) Pinchuk Irina; Pishel Vitaliy; Chumak Stanislav; Ilnytska Tetiana; Stepanova Nataliia; Filimonova Natalia; Kopchak Oksana; Yachnik Yulia; Kolodezhny Oleksiy; Solonskyi Andrii
    Introduction: The COVID-19 pandemic is an extraordinary challenge for all countries and affects the psychological wellbeing of healthcare professionals working with people suffering from COVID-19 and puts them at a high risk of mental health problems. The aim of the study was to identify stress-related factors that affect the mental health of healthcare workers during the COVID-19 pandemic in Ukraine. Subjects and methods: A total of 1098 Ukrainian healthcare workers were surveyed using an online questionnaire consisting of questions relating to a) socio-demographic characteristics; b) perceptions of the COVID-19 related situation; and c) stress and protective factors. Respondents were divided into two groups, depending on whether they provided care to the patients with COVID-19 or not. Results: Of the 1087 healthcare workers, 863 (79.4%) were found to have anxiety / fear caused by the COVID-19. No significant difference was detected between professionals who did and did not provide personal assistance to patients with COVID-19 concerning anxiety / fear related to COVID-19 the most significant predictive factors for anxiety / fear caused by the COVID-19 were factors related to safety and risk perception (the risk of getting infected, dying, infecting loved ones, perception of the threat of the epidemic spread), information factors (constant news about COVID-19), as well as factors related to the organisation of care (lack of staff in health care facilities). Conclusions: Negative risk perception, high consumption of COVID-19 news, and shortage of staff in health care facilities were significant predictors of anxiety / fear caused by the COVID-19.
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    Psychological well-being of Ukrainian students three months after the emerge of full-scale war
    (Polish Psychiatric Association Editorial/Publishing Commitee, 2024) Pinchuk Irina; Solonskyi Andrii; Yachnik Yuliia; Kopchak Oksana; Klasa Katarzyna; Sobański Jerzy; Odintsova Tetiana
    Aim. To depict overall psychological well-being of a large group of students of different universities in Ukraine three months after the emerge of the full-scale war. Material and methods. A total of 1,142 participants were asked to measure their psychological well-being on a 0–10 scale before and after the onset of full-scale war. Mental health symptoms were measured with questionnaires targeting depression (PHQ-9), anxiety (GAD-7), sleep problems (ISI), eating disorders (SCOFF), alcohol abuse (CAGE), and PTSD symptoms (PC-PTSD-5). To evaluate the connection between variables a χ2 was conducted. Phi and Cramer’s V coefficient were stated to demonstrate the power of the relationships. Additionally, machine learning (the XGBoost regression model) was used to build a predictive model for depressive symptoms. Results. Of all respondents, 66% screened positive for PTSD symptoms, 45% – moderate and severe anxiety symptoms, 47% – moderate and severe depressive symptoms. Regarding sleep, alcohol use and eating behavior, 19% of surveyed students had signs of moderate and severe insomnia, 15% reported alcohol abuse and 31% disordered eating. The severity of the aforementioned disorders varied depending on gender, year of study, social status, etc. According to the predictive model, lower initial psychological well-being, female gender, younger age, first years of study and any traumatic experience, including multiple trauma, predicted increases in depression score. Return to home after relocation was a protective factor. Conclusions. The study demonstrated the high prevalence of mental health symptoms among university students in Ukraine during the first months of the full-scale war. The psychological well-being pre-war was the strongest predictor of depressive symptoms in the model.