Prevalence and influencing factors of sleep disturbance among medical students under the COVID-19 pandemic

Wang, Shuo; Luo, Guoshuai; Zhang, Xue; Jing, Yifan; Zaimina,; Yao, Cong; Li, Meijuan; Chen, Hongli; Li, Jie

doi:10.1007/s00406-023-01707-6

Cited by 2 publications

(2 citation statements)

References 61 publications

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“…Also, the authors exploited the central processing unit (CPU) acceleration capabilities of the neural engine to further improve the speed and efficiency of our approach. The effectiveness of using logistic regression for analyzing medical data in combination with other methods has been shown in many works [25]- [31]. Most of the research on medical data analysis is related to the spread of Covid, where logistic regression is used [30] with methods such as: geographically weighted logistic regression (GWLR) [32]; single factor logistic regression analysis and multiple factor logistic regression analysis [33]; Oonishi et al [34] the logistic regression analysis was conducted to examine the correlation between language mismatch between patients and healthcare providers and the requirement for professional medical interpretation.…”

Section: Methods 31 Literature Reviewmentioning

confidence: 99%

Research and implementation of the medical text analysis algorithm for predicting mortality

Rakhmetullina,

Belginova,

Karlygash

et al. 2024

IJEECS

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Mortality prediction has a role to play in the development of a descriptive measure of the quality of care that provides a fair and equitable means of comparing and evaluating hospitals. This article describes a study of a medical text analysis algorithm for mortality prediction that used big data in the form of unstructured medical notes. The article describes the concept of using text mining technology for medical systems, a method for preprocessing medical data to predict patient mortality, an algorithm for predicting patient deaths based on the logistic regression classifier and presents a software module for implementing the proposed algorithm.

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Section: Methods 31 Literature Reviewmentioning

confidence: 99%

Research and implementation of the medical text analysis algorithm for predicting mortality

Rakhmetullina,

Belginova,

Karlygash

et al. 2024

IJEECS

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“…However, previous study has demonstrated that higher level of stress were directly linked to poor sleep quality ( 4 ). Sleep disturbances among medical students are rapidly worsening by the impact of COVID-19 pandemic globally ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

Association of perceived stress and sleep quality among medical students: the mediating role of anxiety and depression symptoms during COVID-19

Huang,

Wen,

et al. 2024

Front. Psychiatry

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COVID-19 has intensified the influence on medical students by changing the lifestyle, online study and clinical practice which bring out series of stress, sleep disturbance and mental health problems. This cross-sectional study aim to explore the association between perceived stress and sleep quality among medical students and investigated whether anxiety and depression mediated this association during pandemic. A total of 1,021 medical students in two universities who were from Guangdong Province, China participated this study and from January to September 2020 through the online question are, with 28.80% reporting sleep disturbances. The medical students were finished various self-reported questionnaires, including the Perceived Stress Scale-10, Pittsburgh Sleep Quality Index, Generalized Anxiety Disorder-7 Scale, and Patient Health Questionnaire-9, the study found positive correlations among sleep quality, perceived stress, anxiety, and depression. The data was analyzed with The Amos 26.0 system. Result demonstrated that perceived stress was associated with poor sleep quality. Anxiety and depression partially mediated the association between perceived stress and poor sleep quality, explaining 73.08% of the association. This study’s structural equation model offers a useful framework for assessing mechanisms mediating associations between perceived stress and sleep quality. The findings emphases the importance of addressing psychological factors in high-pressure situations, which can exacerbate sleep disturbances among medical students. It is important to screen the level of stress, mental health problems and investigate the risk factors of sleep quality among medical students during emergency public health events.

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Prevalence and influencing factors of sleep disturbance among medical students under the COVID-19 pandemic

Cited by 2 publications

References 61 publications

Research and implementation of the medical text analysis algorithm for predicting mortality

Research and implementation of the medical text analysis algorithm for predicting mortality

Association of perceived stress and sleep quality among medical students: the mediating role of anxiety and depression symptoms during COVID-19

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