Health Problems Of College Students Research Articles

The design of a mental health service platform for college students based on multi-modal information

Mental health plays a significant role in the comprehensive growth and development of college students. It refers to the positive development of the mental state which determines their mental or physical potential. The mental health problems of college students increase day by day and become a crucial aspect for families, colleges, universities and society. In existing methods, issues occur due to large variations in the symptoms of mental illness which further results in more than one disease condition at the same time. This creates complexities in the accurate diagnosis of mental health. Based on the present situation of student's mental health, an early warning platform is developed for observing the effect of social media, bad emotions and other factors on the minds of college students. Subsequently, relevant treatment is provided to overcome the diseases caused by mental illness and to improve mental health. Mental health platforms are constructed to improve the health state of college or university students and protect them from psychological crisis. In this paper, a specific framework and module for testing and training schemes based on deep neural model for the analysis of mental state of college students is introduced. The corresponding experimental analysis is carried on the generated set of data for improved validation.

Disparities in mentoring and mental health problems of U.S. college students in science and engineering during the COVID-19 pandemic

This study examines the relationships between mentoring support and mental health problems (i.e. depression and anxiety) among college students in science and engineering in the United States during the COVID-19 pandemic. Descriptive results from a nationwide survey of 2352 undergraduates (from 43 institutions in 25 states), indicate that students who identified as women, underrepresented racial/ethnic minorities, of lower socioeconomic status (SES), and persons with disabilities reported higher levels of mental health problems at the beginning of the COVID-19 pandemic (early summer 2020). Structural equation modelling results indicate that student perceptions of instrumental and psychosocial mentoring support were positively associated with mentoring satisfaction (MS), which in turn was negatively associated with their mental health problems. Moderation analysis findings show that the relationship between MS and mental health problems is stronger for low-SES and non-disabled students. Implications for research and efforts to address the mental health problems of college students are discussed.

An Intelligent Mental Health Identification Method for College Students: A Mixed-Method Study.

Mental health assessments that combine patients' facial expressions and behaviors have been proven effective, but screening large-scale student populations for mental health problems is time-consuming and labor-intensive. This study aims to provide an efficient and accurate intelligent method for further psychological diagnosis and treatment, which combines artificial intelligence technologies to assist in evaluating the mental health problems of college students. We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score. The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively. The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students' psychological problems.

The Relationships between Prospection, Self-Efficacy, and Depression in College Students with Cross-Lagged Analysis.

Depression is one of the most prevalent and disabling mental health problems in college students. Previous studies have established cross-sectional associations between negative bias in prospection e.g., increased negativity in future simulation, low self-efficacy, and depressive symptoms. Nevertheless, the temporal bidirectional associations between them are rarely examined. In the current study, we collected valid data on 276 college students at two time points within a 10 week interval. Cross-lagged panel analysis was applied to investigate the relationships between proportions of negative future events, levels of self-efficacy, and depressive symptoms. Results suggested depressive symptoms predict subsequent proportions of negative prospections and levels of self-efficacy. Inversely, neither prospection nor self-efficacy predicted depression. Temporal correlations between prospection and self-efficacy were also not significant. Since this is one of the first studies that attempts to figure out temporal links between these mutually informing factors, more longitudinal research is needed to draw a firm conclusion. This study provides new insights into the relationship between negative biases in cognitions and depressive symptoms and highlights the need to intervene early with depressive symptoms before any possible cognitive distortions in college students.

Effects of Aerobic Exercise on Sleep Quality and Mental Health of College Students.

Objective In recent years, people's living standards have been greatly improved, but at the same time, it also brings many health problems, among which mental health problems and sleep problems of college students are important problems. The aim of the study is to explore the effect of physical activity intervention on college students' mental health and provide a new way of thinking for colleges and universities to carry out mental health interventions. Methods This paper focuses on the physical health problems of college students and explores the relationship between aerobic exercise, emotion, and sleep quality among college students. The SPSS statistical method was used to analyze the differences in aerobic exercise, emotion, and sleep quality among college students in terms of demographic variables; to explore the relationship between aerobic exercise, emotion, and sleep quality; and to construct a structural equation model using the relationship between them. Results/Discussion. This study explores the relationship between aerobic exercise and sleep quality and psychological health of college students, further analyzes the path model of the role of aerobic exercise and psychological health on sleep quality of college students, and verifies it through experiments, which proves to help universities and relevant departments understand the current situation of exercise and sleep quality of college students and provide timely and effective design of mandatory aerobic exercise courses for college students who lack exercise and have sleep problems. At the same time, it helps college students to understand their sleep situation and adjust their aerobic exercise courses according to their sleep status.

Impact of Corona Virus Disease-19 Pandemic on Co-Assistant Mental Health

Background: The Coronavirus Disease 2019 (COVID-19) pandemic has an impact on the physical and mental health of individuals and all of society. During the COVID pandemic, all social and physical activities of each individual are limited. Physical-social distancing or physical restrictions have an impact on mental health and psychological problems. This anxiety is also experienced by medical students as Co-Assistants (young doctors) during their education in hospitals. Objectives: This study aimed to discuss matters related to the impact of the COVID-19 on medical students’ mental health during the pandemic. Method: This cross-sectional study used the Self-Reporting Questionnaire-20. Mental health problems of college students during the pandemic were assessed and analyzed for their level of anxiety. This study involved 69 medical students as Co-Assistants (young doctors) in the professional study program using purposive sampling technique. Results: The results of the study found that among Co-Assistants who underwent clinical clerkship during the COVID-19 pandemic as much as 44.9% experienced mental health disorders. We also found that the female Co-Assistants were twice as likely to experience mental health problems as male Co-Assistants. The most common type of mental health problem was experiencing symptoms of depression. Conclusion: During the two years of the pandemic, while living the ‘new normal’, Co-Assistants felt the fear of being infected with the lack of personal protective equipment, economic problems for their parents, and facing the challenges of distance education. In comparing the mental health problems of the Co-Assistants, the most common during the COVID-19 pandemic were the symptoms of depression.

Research on the Prevention of Mental Illness of College Students by Health Education under the Background of Big Data

With the arrival of the big data era, the public's way of life and thinking habits have undergone great changes, but also brought new opportunities and challenges to society. At the same time, college students are influenced by work, life, emotions and so on. These combined factors cause an increasing number of college students to have different degrees of psychological problems. The traditional health education of college students is mainly carried out according to the teacher's personal experience. This method is not timely and targeted, which leads to the mental health problems of college students that can not be effectively solved. This paper, combined with the characteristics of the background of the big data era, after analyzing and integrating the data obtained by WeChat , QQ , Microblog and other social platforms, uses the FCM algorithm to cluster the data, analyze and evaluate the the psychological status of students in time, predict the psychological problems of college students, and test through systematic music prevention method on the basis of clustering results. According to this process, when students have psychological problems, they can intervene as soon as possible, protect students' mental health, prevent the occurrence of psychological problems in advance, and promote students' physical and mental health. The health education proposed in this paper plays a very important role in the prevention of mental illness of college students under the background of big data. Through investigation, it is found that this educational method has an obvious effect on the majority of college students.

Research on the Prevention of Mental Illness of College Students by Health Education under the Background of Big Data

With the arrival of the big data era, the public's way of life and thinking habits have undergone great changes, but also brought new opportunities and challenges to society. At the same time, college students are influenced by work, life, emotions and so on. These combined factors cause an increasing number of college students to have different degrees of psychological problems. The traditional health education of college students is mainly carried out according to the teacher's personal experience. This method is not timely and targeted, which leads to the mental health problems of college students that can not be effectively solved. This paper, combined with the characteristics of the background of the big data era, after analyzing and integrating the data obtained by WeChat , QQ , Microblog and other social platforms, uses the FCM algorithm to cluster the data, analyze and evaluate the the psychological status of students in time, predict the psychological problems of college students, and test through systematic music prevention method on the basis of clustering results. According to this process, when students have psychological problems, they can intervene as soon as possible, protect students' mental health, prevent the occurrence of psychological problems in advance, and promote students' physical and mental health. The health education proposed in this paper plays a very important role in the prevention of mental illness of college students under the background of big data. Through investigation, it is found that this educational method has an obvious effect on the majority of college students.

Construction and Analysis of Emotion Recognition and Psychotherapy System of College Students under Convolutional Neural Network and Interactive Technology.

This study's aim is to effectively establish a psychological intervention and treatment system for college students and discover and correct their psychological problems encountered in a timely manner. From the perspectives of pedagogy and psychology, the college students majoring in physical education are selected as the research objects, and an interactive college student emotion recognition and psychological intervention system is established based on convolutional neural network (CNN). The system takes face recognition as the data source, adopts feature recognition algorithms to effectively classify the different students, and designs a psychological intervention platform based on interactive technology, and it is compared with existing systems and models to further verify its effectiveness. The results show that the deep learning CNN has better ability to recognize student emotions than backpropagation neural network (BPNN) and decision tree (DT) algorithm. The recognition accuracy (ACC) can be as high as 89.32%. Support vector machine (SVM) algorithm is adopted to classify the emotions, and the recognition ACC is increased by 20%. When the system's K value is 5 and d value is 8, the ACC of the model can reach 92.35%. The use of this system for psychotherapy has a significant effect, and 45% of the students are very satisfied with the human-computer interaction of the system. This study aims to guess the psychology of students through emotion recognition and reduce human participation based on the human-computer interaction, which can provide a new research idea for college psychotherapy. At present, the mental health problems of college students cannot be ignored; especially every year, there will be news reports of college students' extreme behaviors due to depression and other psychological problems. An interactive college student emotion recognition and psychological intervention system based on convolutional neural network (CNN) is established. This system uses face recognition as the basic support technology and uses feature recognition algorithms to effectively classify different students. An interaction technology-based psychological intervention platform is designed and compared with existing systems and models to further verify the effectiveness of the proposed system. The results show that deep learning has better student emotion recognition ability than backpropagation neural network (BPNN) and decision tree algorithm. The recognition accuracy is up to 89.32%. Support vector machine algorithm is employed to classify emotions, and the recognition acceptability rate increases by 20%. When K is 5 and d is 8, the acceptability rate of the model can reach 92.35%. The effect of this system in psychotherapy is remarkable, and 45% of students are very satisfied with the human-computer interaction of this system. This work aims to speculate students' psychology through emotion recognition, reduce people's participation via human-computer interaction, and provide a new research idea for university psychotherapy.

Design of Intelligent Evaluation System for College Students' Mental Health Based on Big Data.

The mental health problems of college students have attracted the attention of all sectors of society. In order to keep college students in a good mental state and effectively analyze their mental health, an intelligent evaluation system of college students' mental health based on big data is designed. Based on big data technology, this article constructs an intelligent evaluation system for college students' mental health, which is divided into six layers, namely, application layer, decision layer, interface layer, analysis layer, data layer, and basic layer. Then, the mental health data of college students were collected based on C/S architecture. On the basis of extracting and integrating data characteristics, six evaluation indexes of personality, will, emotion, depression, fear, and psychosis were screened, and then, the intelligent evaluation was completed according to the weight of indexes. On the basis of the preliminary verification of the performance of the system in this article, according to the comparative experimental results, the mental health data acquisition time of the system is less, the accuracy of data feature extraction and the recall rate of evaluation results are higher.

Predictive Analysis of Student Stress Level Using ML

College students are suffering from many mental health problems including mental stress, somatization, obsessive, interpersonal sensitivity, depression, anxiety, hostility, fear, paranoia and psychosis, which can bring a lot of negative effects to them. Obviously, the mental health problems of college students not only directly affect their own growth, but also affect the stability of the campus. Most colleges also pay more and more attention to students' psychological crisis monitoring and prevention. All Colleges simply analyze whether students have mental health problems or what kind of problems they have. It cannot find hidden relationships in the psychological data. We need a system to handle the student mental health problems, here we are mainly concentrating on student stress prediction. There are so many factors related to stress such as work load, assignments, family issues, friends issues, attendance, teaching etc. Machine learning is a subject to predict future based on the past data. Using machine learning techniques we predict student stress level and also proposed system will give suggestions based on the stress levels of student.

Studying the Impact of Health Education on Student Knowledge and Behavior through Big Data and Cloud Computing

Artificial intelligence and big data, as emerging technologies that have attracted much attention in recent years, have broad application and development space in improving the development of intelligent and refined education in colleges and universities. The application of artificial intelligence and big data to the mental health education practice of college students has a very positive effect on accurately discovering and scientifically solving the mental health problems of college students. In order to combine big data and cloud computing platform organically, this paper introduces an intelligent algorithm based on multi-output support vector regression (MSVR) model and immune clone selection algorithm (ICSA). At the same time, we couple the two to obtain a new intelligent algorithm, namely, immune multiple output support vector regression (ICSA-MSVR) algorithm. Based on the prediction results of health education on students' knowledge and behavior by cloud computing platform, the necessary conditions for three intelligent algorithms to complete the task are summarized. Numerical experimental results show that ICSA-MSVR plays a role in both local search and global search, and is more effective in large-scale cloud computing task scheduling. In addition, in task scheduling, when the task completion time is short, ICSA-MSVR has a lower load imbalance than ICSA and MSVR, which can achieve better load balancing, and the load between virtual machines is closer. Finally, combined with the problems and the needs of students’ health education, suggestions are put forward to deepen the application of technology in students’ mental health education. This approach can provide corresponding ideas and reference methods for improving the scientificity, pertinence, and effectiveness of mental health education.

Research on identifying psychological health problems of college students by logistic regression model based on data mining

Abstract With the popularisation of education, the number of college students is increasing day by day, and there are also more students with psychological health problems. Whether students’ psychological abnormalities can be detected in time is one of the main problems faced by colleges and universities at present. Adopting digital technology to mine, collect and analyse the data generated by psychological health education in colleges can effectively solve the dynamic development of students’ psychological health problems. Therefore, in this paper, the psychological health problems of college students are identified and classified by establishing an improved logistic regression model. The behaviour characteristics are quantified and the differences are combined according to students’ relationships with their classmates, life rules and economic conditions. The test results show that the regression effect of the model is excellent, which can identify college students’ psychological health problems and improve the intervention and treatment of educators on students’ psychological problems.

Handling of college students’ network public opinion crisis based on big data analysis

As an important public utility organization and academic research institute, Colleges feature high density people, high social attention, and relatively free atmosphere of interchange ideas, where network opinion crisis are frequent. Therefore, it is critical for colleges to concentrate more on the control and management of the network opinion crisis. In the paper, it compares events of college students’ online public opinion crisis with other references. Social online public opinion crisis solutions is use for reference. Aiming at the peculiarities of college students’ mental health, it is concluded the research coping strategies based on innovative and comprehensive use of big data technology. We establish a complete emergency protection system to effectively deal with the mental health problems of college students.

Prevalence of mental health problems among college students in mainland China from 2010 to 2020: A meta-analysis

<p id="p00005">College students are a huge part and an important member of the young people. Their physical and mental health not only directly affects their own long-term development, but also has a significant influence on the future of the nation. Therefore, the mental health of college students has become the focus of families, schools and even the whole society. In recent years, some studies have conducted meta-analyses on the prevalence of a certain mental health problem (such as depression and sleep problem) among college students in China, but such studies cannot reflect the mental health status of college students comprehensively. And some of the previous studies cannot reflect the current status of contemporary college students’ mental health. Furthermore, the selection of moderator variables was not comprehensive enough to reveal the key factors affecting the prevalence. Therefore, we employed a meta-analysis in this study to estimate the prevalence of typical mental health problems among college students in mainland China from 2010 to 2020, and the moderating effects of publication year, measurement tools and detection standards, detection time, region, birthplace, only child or not were further analyzed. For the selection of indicators, mental health problems were divided into two categories: internalizing problems and externalizing problems. The indicators of internalizing problems include anxiety, depression, sleep problem, somatization, and suicidal ideation, while the indicators of externalizing problems include nonsuicidal self-injury and suicide attempt. <br/>Through the process of literature search and selection, 128 studies (136 effect sizes) of anxiety, 237 studies (244 effect sizes) of depression, 56 studies (58 effect sizes) of sleep problem, 49 studies (49 effect sizes) of somatization, 31 studies (31 effect sizes) of nonsuicidal self-injury, 51 studies (51 effect sizes) of suicide ideation, and 8 studies (8 effect sizes) of suicide attempt were included in this study. Homogeneity test indicated that random effects model was appropriate for the meta-analysis. The p-curve analysis illustrated no publication bias. Ultimately, the results of the main effect test showed that the prevalence rates of sleep problem, depression, nonsuicidal self-injury, anxiety, suicidal ideation, somatization, and suicide attempt were 23.5%, 20.8%, 16.2%, 13.7%, 10.8%, 4.5%, and 2.7%, respectively. The results indicate that internalizing problems, especially sleep problem and emotional problem, are more serious among Chinese college students compared to externalizing problems. <br/>The results of the moderating effect indicated that (1) The prevalence of anxiety, depression, sleep problem and suicide attempt among college students has increased significantly in the last decade, while the prevalence of self-harm has declined significantly; (2) The prevalence of anxiety, depression, sleep problem, and somatization varied significantly between measurement tools and detection standards, and the prevalence of suicidal ideation differed to a significant extent depending on the detection time. Therefore, the fluctuation of prevalence was accounted by measurement tools, detection standards and detection time. (3) There existed obvious regional differences in the prevalence of sleep problem and suicidal ideation, with the feature of the worst mental health among college students in western China and better mental health among college students in northeastern and central China. (4) The prevalence of mental health problems among demographical variables including gender, only child or not, and birthplace showed no significant difference, which indicated that gender, only child or not, urban or rural areas were not the critical factors influencing college students’ mental health. <br/>In summary, by employing the method of meta-analysis, this study is the first study to systematically investigate the prevalence of the typical mental health problems of college students in mainland China from 2010 to 2020. The results clarified the controversy over the inconsistent prevalence in previous studies and explored the main reasons for the inconsistent findings. Thus, this meta-analysis is conducive to promoting subsequent studies and educational practice.

Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Mental health and mental health problems of college students are becoming more and more obvious, and there is more and more negative news caused by psychological problems, and society from all walks of life has given high attention to this problem. Given the new situations and new problems, how to keep up with the times and reform and innovate in the content, method, and path of psychological education in colleges and universities is an important work of ideological and political education in colleges and universities. Because fine-grained category information can provide rich semantic clues, fine-grained parallel computing techniques are widely used in tasks such as sensitive feature filtering, medical image classification, and dangerous goods detection. In this study, we adopt a fine-grained parallel computing programming approach and propose a multiobjective matrix regular optimization algorithm that can simultaneously perform the joint square root, low-rank, and sparse regular optimization for bilinear visual features, which is used to stabilize the higher-order semantic information in bilinear features, improve the generalization ability of features, and apply it to the construction of mental health education models for college students to promote the construction of mental health education bases, improve mental health education network platform, and strengthen the construction of mental health education data platform. A new practical aspect has been added to the abstract. The saliency-guided data augmentation method in this study is an improvement on random data augmentation but reduces the randomness in the data augmentation process and significantly improves the results. The best result belongs to SCutMix data augmentation, which improves by 1.9% compared to the baseline network.

Screening on Collect Students: Postural Risk Factors of Spinal Musculoskeletal Problems and Scoliosis

Background: Overuse of electronic products and decrease in levels of physical activity may lead to an increased incidence of spinal health problems in college students. This study aims to 1) identify the incidence of spinal health problems in college students, 2) to explore the key postural factors in development of spinal musculoskeletal dysfunction and scoliosis, and 3) to clarify new strategies for early identification and prevention of scoliosis. Methods: A cross-sectional screening by static posture assessment, selective functional movement assessment (SFMA) and Adam’s Forward Bend Test,was conducted to 306 college students from October to December 2019. Descriptive analysis, Spearman correlation analysis and binomial logistic regression analysis were performed on the collected data. Results: Static postural assessment was completed among 306 college students, 53% of students’ external auditory meatus and acromioclavicular joint were not in the same gravitational line, 45% had forward-head posture, 42% had uneven shoulders. When performing SFMA, we found spinal musculoskeletal dysfunctions, including abnormalities of multi-segment spine flexion (32%), cervical rotation to right (18%), cervical rotation to left (16%), etc. In Adam’s Forward Bend Test, 10.78% had a positive result, which suggested structural or functional scoliosis. Correlation analysis showed that the top three postural factors related to spinal musculoskeletal dysfunctions included uneven shoulders, forward-head, position of thorax and pelvis rotated. And top three postural factors related to scoliosis were spinous processes line deviating from midline, winged scapula and head rotation off midline. Logistic regression analysis displayed that there are two high risk factors for scoliosis, including external auditory meatus and acromioclavicular joint not in the same vertical line (OR = 6.314), and deviation of spinous processes line (OR = 12.743). Conclusions: Poor posture, spinal musculoskeletal dysfunction and scoliosis affect a significant proportion of college students. While their spinal musculoskeletal dysfunction and scoliosis are closely related to poor posture.

Evaluation and Analysis of College Students’ Mental Health from the Perspective of Deep Learning

College students are easily affected by the outside world, which leads to mental health problems, so it is particularly important to accurately evaluate and analyze the mental health status of college students. At present, the evaluation and analysis model of college students’ mental health is inaccurate and inefficient, which cannot analyze the mental health problems of college students. In order to evaluate and analyze the mental health problems of college students more accurately, this paper designs an evaluation and analysis model of college students’ mental health from the perspective of in-depth learning. The accuracy of model evaluation and analysis is improved, and a better comparison result is obtained. Firstly, the BP neural network model was compared with the logistic model and ARIMA model, and the results showed that the accuracy of the BP neural network model was more than 70% in five comparisons and was higher than that of the logistic model and ARIMA models. Second, the BP deep learning method is compared with several conventional methods (KNN, MF, NCF, and DMF) in the comparison phase of the model. The RMSE, MAE, and MAPE of the BP method are lower than those of the other four traditional methods. Finally, in the comparative experiment, the precision and AUC of the BP model are improved by 2%, and the three indicators of precision, recall, and F1 are also higher than those of other models. Through the specific evaluation of the five indicators of the four college students, from the five indicators of psychological adaptability, frustration, emotional stability, temperament, and personality, the mental health of the four college students is better.

Problems and Countermeasures of College Students' Mental Health Education.

Compared with the previous teaching environment and model, college students need more self-awareness and self-discipline to learn better. In order to solve many mental health problems of college students, this work studies that the school should increase policy support to relevant parties and strengthen the cultivation of college students' psychological counseling ability, so as to provide more psychological counseling services for college students. This work studies and analyzes the common problems in contemporary college students' mental health education, such as lack of teachers, mere formality, imperfect system, and poor pertinence, and puts forward the corresponding countermeasures for college students' mental health education. When college students enter the university, they need to further improve their knowledge and ability to achieve professional development in a relatively loose learning environment. University is a key period for college students' personality improvement and physical and mental development. With the increasing pressure of social employment, the psychological pressure of college students is increasing. This study provides a reference for the mental health problems of college students.

Correlation of Childhood Psychological Abuse and Neglect With Mental Health in Chinese College Students During the COVID-19 Pandemic.

Experience of childhood maltreatment is a major factor affecting adult mental health. The purpose of this study was to understand the association of childhood psychological abuse and neglect with mental health in college students during the COVID-19 pandemic. An online questionnaire survey was conducted from February 21 to March 12, 2020. The participants were 200 students at a university of physical education in Shaanxi Province, China. Participants completed the Child Psychological Abuse and Neglect Scale and the Mental Health Self-Report Questionnaire. Regarding childhood maltreatment experience, 52.5% of respondents screened positive for childhood psychological abuse, 55.8% for psychological neglect, and 43.6% for both. Moreover, 37.6% of participants screened positive for psychological health problems during the pandemic. Childhood psychological abuse and neglect were positively associated with mental health problems during the COVID-19 pandemic. A regression analysis revealed that the reproving dimension of psychological abuse was a risk factor for mental health problems in college students during the COVID-19 pandemic.