Observational Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Psychiatry. Aug 19, 2023; 13(8): 573-582
Published online Aug 19, 2023. doi: 10.5498/wjp.v13.i8.573
Investigation of contemporary college students’ mental health status and construction of a risk prediction model
Xiao-Li Mao, Hong-Mei Chen
Xiao-Li Mao, Hong-Mei Chen, School of Health and Nursing, Wuchang University of Technology, Wuhan 430223, Hubei Province, China
Author contributions: Mao XL designed and performed the research and wrote the paper; Chen HM designed the research and contributed to the analysis.
Supported by Hubei Province Education Science Planning Project, No. 2020GB132.
Institutional review board statement: The study procedures were approved by the Ethics Committee of the School of Health and Nursing, Wuchang University of Technology (No. 20234002).
Informed consent statement: All participants signed an informed consent form.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data used in this study can be obtained from the corresponding author upon request.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xiao-Li Mao, MBBS, Associate Professor, School of Health and Nursing, Wuchang University of Technology, No. 16 Jiangxia Avenue, Wuhan 430223, Hubei Province, China. maoxiaoli1973@163.com
Received: June 14, 2023
Peer-review started: June 14, 2023
First decision: July 3, 2023
Revised: July 6, 2023
Accepted: July 14, 2023
Article in press: July 14, 2023
Published online: August 19, 2023
Research background

Mental health problems in college students have become the focus of academic and social attention. The main reason is that college students are facing many pressures and challenges, such as academic pressure, social pressure, career planning pressure, and so on. In addition, the rapid development of modern society and the competitive environment have also brought new challenges and threats to the mental health of college students. Therefore, the study of college students’ mental health has profound practical significance and theoretical value.

Research motivation

With the increasing pressure and challenges faced by college students, their mental health problems are becoming more and more serious, and the detection rate is increasing yearly.

Research objectives

Research on the mental health status of current college students and the construction of a risk prediction model can not only help us to understand the mental health problems faced by college students more comprehensively, but also provide a valuable reference for prevention strategies. It can also identify and intervene in mental health problems at an earlier time point to avoid further deterioration of the problem. In addition, future research in this field can also explore the relationship between mental health status and college students’ learning, life, and interpersonal communication, and provide useful support for improving the overall quality and development of college students.

Research methods

The objective of the current research was to examine the mental well-being of 40874 undergraduate and graduate students enrolled in various higher education institutions within a particular geographical area. Additionally, using logistic regression analysis, the determinants that exert a substantial influence on the psychological health of university students were investigated. This model has high accuracy and interpretability. This study used R software to construct a risk prediction nomogram model, which enabled researchers to understand the influence and relationship of different variables more intuitively and improved the visualization and comprehensibility of the research. In summary, this study adopted a variety of advanced research methods, which had a wide range of reference values and application prospects.

Research results

The survey found that 11.3% of college students had psychological problems. The risk factors for college students’ mental health include being from rural areas, non-single children, major life events, parents’ marital divorce, infrequent exercise, and no close friends. The area under the receiver operating characteristic (ROC) curve in the training set was 0.972, the specificity was 0.888, and the sensitivity was 0.972. The area under the ROC curve in the validation set was 0.979, the specificity was 0.942, and the sensitivity was 0.939. These findings reflect the current mental health status of college students and is of great significance for the public to raise attention and awareness of mental health problems. It provides a basis for the government and schools to formulate mental health policies and help to formulate effective mental health management measures. At present, there are still some problems in the design and application of college students’ mental health questionnaires, as some students could not understand or answer the questions in the questionnaire. More comprehensive and in-depth analysis and research are still needed to ensure the accuracy and reliability of the analysis and statistical results.

Research conclusions

From this research, we conclude that the current mental health status of college students is good. New methods used in this study include the use of a logistic regression model and the use of R software to construct a risk prediction nomogram model to explore the related factors affecting the mental health of college students. These methods can predict the risk factors related to college students’ mental health more accurately and provide more effective intervention measures and prevention methods.

Research perspectives

Assessing and monitoring the mental health of college students can help schools and other institutions to better understand the needs of college students and take timely measures to prevent the emergence of mental health problems. Future research should focus on developing more effective assessment tools and establishing tracking and monitoring systems.