research-article
Authors: Menglu Xu and Hongjuan Wen
BIC '24: Proceedings of the 2024 4th International Conference on Bioinformatics and Intelligent Computing
January 2024
Pages 32 - 36
Published: 28 June 2024 Publication History
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Abstract
The COVID-19 outbreak, declared a public health emergency by the WHO, has intensified stress levels for medical students who were already under significant pressure. Medical students has been an increase in mental health issues and changes in health behaviors. This study utilized a Logistic regression algorithm to determine factors that cause stress among medical students following public health emergencies in China. Data on demographic characteristics, mental health conditions, and health behaviors post-emergency were collected from 1927 medical students. Chi-square tests and logistic regression were used to analyze the factors that influenced stress levels among Chinese medical students following public health emergencies. Results showed that after public health emergencies, medical students experienced higher stress levels (44.84%). Psychological factors such as anxiety and loneliness, and health behaviors like sleep patterns, social isolation, and changes in daily routines were identified as primary contributors to increased stress levels (p<0.05). In conclusion, psychological factors and health behaviors are primary contributors to increased stress levels, the study highlights the importance of addressing various factors that contribute to stress among medical students, including enhancing stress management abilities and encouraging healthy behaviors to promote the physical and mental well-being of Chinese medical students during public health emergencies.
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Index Terms
Modelling the impact of medical student stress after a public health emergency based on Logistic regression algorithm A model of factors influencing medical student stress Using logistic regression algorithms to predict factors influencing medical student stress after a public health emergency
Computer systems organization
Real-time systems
Computing methodologies
Machine learning
Machine learning approaches
Logical and relational learning
Inductive logic learning
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Published In
BIC '24: Proceedings of the 2024 4th International Conference on Bioinformatics and Intelligent Computing
January 2024
504 pages
ISBN:9798400716645
DOI:10.1145/3665689
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [emailprotected].
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 June 2024
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BIC 2024
BIC 2024: 2024 4th International Conference on Bioinformatics and Intelligent Computing
January 26 - 28, 2024
Beijing, China
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