Health Problems Of College Students Research Articles

Research on virtual entertainment robots based on machine learning algorithms providing psychological health services for college students

In modern society, college students are facing increasing psychological pressure and mental health problems. In this context, virtual entertainment robots have become a promising form of mental health services, which can utilize machine learning algorithms to provide personalized psychological support and guidance by analyzing a large amount of psychological data and user information. Study the use of sample calculation and screening methods to determine the number of samples and perform feature selection to improve algorithm performance. Then analyze the detection effect and evaluate the effectiveness of the algorithm. By designing the architecture of a virtual entertainment robot and adopting anti-interference strategies to ensure that the robot can accurately recognize mental health information, text recognition technology was implemented, its effectiveness was evaluated, and further multi-source information recognition was carried out to improve recognition accuracy. Finally, a psychological health evaluation system for college students was constructed, and corresponding psychological health service strategies were proposed to meet the needs of college students. The results of this study indicate that virtual entertainment robots based on machine learning algorithms can effectively provide mental health services, providing support and guidance for the mental health problems of college students.

The Interplay of Sleep Quality, Mental Health, and Sociodemographic and Clinical Factors among Italian College Freshmen.

Background/Objectives: Sleep and mental health are closely linked, with sleep deprivation increasing the risk of mental health problems in college students. This study aimed to analyze the role of sleep in the mental health status of a sample of Italian freshmen, considering various mental health outcomes and potential interactions between sleep and other relevant factors, such as sociodemographic characteristics, academic experiences, and mental health history. Methods: All freshmen from a medium-sized Italian university were invited to participate in a multidimensional online survey (n = 3756). Sleep quality was assessed through questions on average hours of sleep per night and on satisfaction of perceived sleep quality. Mental health outcomes included psychophysical well-being, psychological distress, substance use, and problematic internet use. Statistical analysis involved multivariate analysis of variance, followed by pairwise comparisons. Results: The sample (n = 721) exhibited low levels of well-being and a high prevalence of psychological distress (52.1%). Approximately one-third of students (n = 258) were dissatisfied with their sleep quality, and one-fourth (n = 186) reported inadequate sleep (less than 7 h per night). More specifically, 24.4% of students slept on average six hours per night, and 1.4% slept five hours or less. Satisfaction with perceived sleep quality significantly influenced well-being, psychological distress, and cannabis use (ηp2 = 0.02). Interaction effects were observed between satisfaction with sleep quality and drop-out intentions (ηp2 = 0.01), as well as between satisfaction with sleep quality and history of mental health diagnosis (ηp2 = 0.02), both of which were significant for psychological distress and cannabis use. Conclusions: This study highlights the influence of perceived sleep quality on academic distress among college freshmen, particularly those with higher intentions of leaving university and with a history of mental health diagnosis.

Research on the Psychological Status and Countermeasures of Chinese College Students

With the development of society and the popularisation of education, the psychological health problems of Chinese college students are getting more and more attention. This thesis aims to study the psychological status quo and countermeasures of Chinese college students, and to provide reference for solving college students' psychological problems. Firstly, by analysing the psychological status quo of college students, it is found that the mental health problems of college students are widespread, which are mainly manifested as anxiety, depression, low self-esteem and so on. Secondly, by analysing the factors affecting the psychological problems of college students, it is found that academic pressure, employment pressure, family factors and social environment all have an impact on college students' psychology. Next, strategies for coping with college students' psychological problems are proposed, including school psychological counselling services, mental health education and publicity, academic stress management, employment stress management, family support and care, and social support and advocacy. Finally, the importance and methods of preventing and intervening in college students' psychological problems are discussed. Through the study of college students' psychological problems and the proposed countermeasures, it can help college students improve their mental health and promote their overall development.

Path analysis of digitally empowered mental health services for university students

Abstract We investigate the viability of using data mining technologies to identify college students’ mental health issues in light of the rising number of these issues. To address the limitations of the traditional Apriori algorithm in data mining of mental health problems, an improved Apriori algorithm is proposed using the classification rule mining method. The relationship between various factors and the mental health problems of college students is better explored by this algorithm. Ultimately, the mental health care pathway that was developed during the exploration was used to conduct a comparative trial between those who received mental health services and those who did not. The experimental group’s mean score in the hyperactive concentration inability dimension was 3.25 after getting mental health care for three weeks, which was 22.6% higher than the control group’s mean score. The aspects of emotional symptoms, pro-social conduct, and total difficulty score also showed significant variations (p<0.05). The mean scores of the experimental group in the pro-social behavior and emotional symptoms dimension in the course of the study and the pre-experiment were 2.17 and 0.57, accordingly, which both demonstrated highly significant differences (p<0.01) in the between-group comparison of the differences in scores at different evaluation times with the control group.

Mental Health Problems of Chinese International College Students in the United States

Mental health problems are prevalent among students in American universities and colleges, particularly among those from other countries or regions. Despite considerable research on the relatively high prevalence of mental health problems among international college students and potential reasons they under-utilize student support services, little is known about the mental health experiences of students from China to study in the United States. This paper provides insight into the challenges and interventions surrounding the mental health problems of college students from China to study in the United States. This paper provides a synthesis of available findings and addresses the factors triggering mental health issues experienced by Chinese international college students studying in the United States, the challenges they face when gaining access to mental health services, and potential interventions to boost their mental well-being. Additionally, this paper emphasizes the significance of providing a supportive environment that encourages and facilitates the help-seeking behavior of Chinese international college students, aiming to offer a valuable resource for mental health practitioners, policymakers, and higher education professionals.

Comprehensive psychological crisis intervention of college students with anxiety disorder

BackgroundAnxiety disorder is one of the common mental health problems in college students, which hurts their study, work, and life. Comprehensive psychological crisis intervention is a complete psychological treatment method expected to be essential in treating anxiety disorders in college students.Subjects and MethodsOne hundred college students with anxiety disorder were selected as research subjects and randomly divided into two groups. The experimental group received comprehensive psychological crisis intervention treatment and comprehensive intervention measures such as psychological assistance, cognitive behavioral therapy, and intimate relationship training. The control group received traditional psychotherapy, including counseling and medication. The Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS), and other assessment tools were used to carry out psychological measurements of the two groups of patients before, after, and at the follow-up point, respectively. The collected information was statistically analyzed by SPSS23.0 software.ResultsAfter the comprehensive psychological crisis intervention treatment, the anxiety and depression levels of the experimental group were significantly reduced (P<0.001), and life satisfaction was significantly increased (P<0.001). Compared with the control group, the experimental group showed obvious advantages in curative effect.ConclusionsComprehensive psychological crisis intervention has shown remarkable efficacy in college students with anxiety disorders, can effectively reduce anxiety and depression, and improves the life satisfaction of patients. This approach may become an essential option for treating anxiety disorders in college students.Acknowledgement2021 Humanities and Social Sciences Research Project for Basic Research Business Expenses of Provincial Undergraduate Universities in Heilongjiang Province. No. 2021- kyywf-0384.

The therapeutic effect of ikebana teaching combined with ACT on obsessive-compulsive disorder college students

BackgroundIn recent years, due to academic pressure, changes in interpersonal relationships, and living environments, the mental health problems of college students have gradually become prominent, with obsessive-compulsive disorder (OCD) becoming an indispensable part. Patients with obsessive-compulsive disorder often suffer from recurring obsessive-compulsive beliefs and behaviors, seriously affecting their daily lives and academic performance. Therefore, seeking effective intervention methods is crucial.Subjects and MethodsThe experiment selected 60 students with obsessive-compulsive disorder from a particular school and randomly divided them into an experimental group and a control group, with 30 students in each group. The experimental group received practical teaching of flower arrangement art and acceptance commitment therapy (ACT). The control group only used traditional psychological counseling methods. The experimental period is six months, and the data is statistically processed using SPSS 24.0 and Excel.ResultsAfter a 6-month intervention, the obsessive-compulsive symptoms in the experimental group were significantly reduced, with a total effective rate of 90%, while the effective rate in the control group was 60%. In addition, the experimental group’s self-efficacy, emotional regulation ability, and quality of life also significantly improved.Conclusions The combination of flower arrangement art practice teaching and acceptance commitment therapy has shown sound intervention effects in treating obsessive-compulsive disorder college students. This combined treatment method can not only effectively alleviate obsessive-compulsive symptoms but also enhance the patient’s psychological resilience and self-management ability. Promoting this method has significant practical value in future mental health work.AcknowledgementHunan Province General Higher Education Teaching Reform Research Project (General Education Project) (No. HNJG-2020-1170).

Research on practical education model of mental health development of college students in higher education institutions in the context of big data

Abstract The advent of the big data era brings opportunities and challenges to the innovation of mental health education for college students in higher education institutions. In this paper, we monitor the mental health of college students in higher education institutions by collecting students’ consumption data, access control data, network data and historical achievement data, and use a convolutional neural network to extract features from behavioral data and then input the feature set into random forest model for training. The recognition effect of the RF algorithm is analyzed by comparing it with other algorithms, and the problems of college students’ mental health education are analyzed based on the correlation of features. The accuracy of identifying mental health problems of college students based on RF analysis of big data reached 0.72, the recall rate reached 0.58, and the F1-Measure was 0.67. For the correlation of different features, the most significant correlation effect was the time spent in the dormitory on rest days and the number of swiping access cards, with the correlation coefficients reaching 0.2719 and -0.2191, respectively. The analysis based on big data can accurately grasp the current situation and problems of mental health education of college students in higher education institutions and promote the comprehensive development of mental health education of college students.

Building a Psychological Counseling and Mental Health Education System for College Students

With the development of society and changes in educational concepts, the mental health problems of college students have become increasingly prominent. To improve the mental health level of college students, it is crucial to construct a comprehensive psychological counseling and mental health education system. This paper analyzes the mental health problems of college students, discusses the necessity of building a psychological counseling and mental health education system, and proposes key elements and strategies for system construction, including improving psychological counseling service institutions, strengthening the construction of faculty, conducting comprehensive mental health education involving all members, and establishing an effective evaluation mechanism. These measures contribute to enhancing the level of psychological counseling and mental health education for college students and promoting their comprehensive development and well-being.

Web-Based Counseling Problem-Solving Model for Improving Mental Health in Students During the Covid-19 Pandemic

Background: There was an increase of mental health problems during the Covid-19 Outbreak caused by fears of contracting COVID-19, Increasing mental health problems in college students due to the Covid-19 outbreak caused by fears of contracting COVID-19, difficulty understanding online learning materials, decreased academic achievement, limited activities, finances, and boredom during the stay at home. Objective: To analyze a web-based problem-solving counseling model to improve mental health in students during the Covid-19 pandemic.Method: Quasi-experimental design using a pre-post test with a control group. The population was all first-year students at Surakarta College of Health, who were sampled with a purposive sampling technique. Inclusion criteria: students who were experiencing anxiety, stress, depression, and suicide risk. There were a total of 60 respondents in the control group and 60 respondents in the intervention group. Data was collected using thequestionnaires Suicide Behavior Questioner-Revisi (SBQ-R) and Depression Anxiety Stress Scales (DASS). The web application intervention consisted of up-to-date testimonials, questionnaires, inspirational videos, and counseling options. The tests used were the dependent test and the independent test.Result: There was a significant improvement of participant mental health with and results found a reduction to the the risk of suicide, depression, stress, and anxiety.Conclusion: The Web-Based Problem-Solving Counseling Model is effective in improving students' mental health during the COVID-19 pandemic( p0.05), and can be used as an alternative strategyfor students to address their mental health problems while maintaining privacy and low levels of stigma.

Application of image super-resolution recognition and artificial intelligence system in repairing students' psychological education problems

With the continuous development of society, people's life pressure is constantly increasing, and the mental health problems of college students are becoming increasingly prominent, bringing many challenges to their education and management. Universities should not only cultivate students' theoretical and professional knowledge and practical skills, but also attach importance to their mental health and effectively implement psychological education. Therefore, it is very necessary to develop and design a simple and effective student psychological evaluation system. As a new form of ideological and political transformation in universities in the era of big data, online ideological and political work has potential development space. It is necessary to carry out mental health education in universities, fully utilize online education forms, and improve ability of universities to repair mental health problems. Based on this, this system designs and implements software for typical image resolution based recognition and artificial intelligence. The use of B/S architecture in the development and use of. net technology and web server technology will enable more students to connect and use different terminals. In addition, an algorithm for image super-resolution recognition was proposed, which uses clustering convolution to improve residual blocks, improves modeling ability by extracting features on a larger scale, reduces the number of parameters to improve model calculation efficiency, and enables mental health educators and managers to work better. This article combines image super-resolution recognition technology with artificial intelligence technology to apply it to the process of psychological education in universities, thereby promoting the development of problem repair applications.

Predictive Analysis of Student Stress Level using Machine Learning

1. 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. 2. Obviously, the mental health problems of college students not only directly affect their own growth, but also affect the stability of the campus. Colleges increasingly prioritize monitoring and preventing students' psychological crises. 3. All Colleges simply analyze whether students have mental health problems or what kind of problems they have. It fails to uncover underlying relationships within psychological data. 4. We require a comprehensive system to address student mental health concerns, with a specific focus on predicting and managing student stress. 5. There are so many factors related to stress such as work load, assignments, family issues, friends issues, attendance, teaching etc... 6. Machine learning is a subject to predict future based on the past data. By leveraging machine learning techniques, we can accurately predict student stress levels. Furthermore, our proposed system will provide personalized suggestions based on these stress levels

Research on the application of digital music in mental health education in colleges and universities

Abstract The interdisciplinary study of mental health education and digital music in colleges and universities is an indispensable part of China’s education discipline system. To solve the current problems of mental health education in colleges and universities, this paper analyzes the promotion role of digital music and the development of mental health in colleges and universities. The hierarchical analysis method is used to build a mental health assessment index system in colleges and universities. Using the VPMCD method, based on the intrinsic relationship between different index characteristics, a digital music-based psychological health assessment model for colleges and universities is established to assess the psychological health problems of college students. According to the psychological assessment results, the listening psychological intervention method is used to psychologically intervene with students. And by calculating the main melody of digital music materials, the type of music intervention materials used was determined. Experimental results: The listening psychological intervention method successfully led to the slow recovery of students with severe and moderate mental health problems and the complete recovery of students with mild psychological problems. 11 students (9.2% of the total) were completely cured of their psychological problems among the freshmen students in college A. The number of students who reduced their psychological problems was 86, accounting for 81.1% of the total. 26 students, accounting for 32.3% of the total, were completely cured of their psychological problems in their sophomore year at College A. The number of students who reduced their psychological problems was 56, accounting for 51.3% of the total. It proves that: digital music plays a supplementary role in college mental health education and proposes a new way of implementation for college mental health education. It promotes the diversified development of mental health education in colleges and universities as well as the cultivation of positive psychological qualities of college students and guides them to grow up positively and healthily.

Impact of family functioning on mental health problems of college students in China during COVID-19 pandemic and moderating role of coping style: a longitudinal study

BackgroundDuring the COVID-19 pandemic, college students were required to stay at home and maintain social distance for the entire spring semester of 2020. There is little research on how family functioning influenced mental health problems and how coping styles moderated the relationship between family functioning and mental health problems among college students during their stay-at-home period.MethodsA total of 13,462 college students (age = 16–29 years) completed four online surveys between February and October 2020, namely the outbreak phase, remission phase, online study phase, and school reopening phase in Guangdong Province, China. Family functioning was assessed by the Family APGAR; coping styles were assessed by the Simplified Coping Style Questionnaire (SCSQ), depression symptoms and anxiety symptoms were evaluated by the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder Scale (GAD-7) respectively. Generalized estimating equations were used to assess associations between variables, the logit link function was used to estimate the odds ratio of different subgroups, the Newton–Raphson method was used to estimate parameters, and the Wald test was used to test the main effect and the interaction effect.ResultsThe incidence rates of depression increased during the stay-at-home period from 33.87%, 95% CI (29.88%, 38.10%) to 40.08% 95% CI (35.76%, 44.55%) after schools reopened, χ2 = 193.68, p < 0.001. The incidence rates of anxiety increased from 17.45%, 95% CI (14.59%, 20.73%) to 26.53%, 95% CI (16.94%, 23.67%) over the entire period, χ2 = 195.74, p < 0.001. The percentages of students with highly functional, moderately dysfunctional and severely dysfunctional family functioning were 48.23%, 43.91 and 7.86% at T1 and 46.20%, 45.28%, and 8.52 at T4, respectively. The percentage of subjects with active coping style was 23.9%, negative coping style was 17.4%, strong response coping was 26.9%, and weak response coping was 31.7%. The incidence rate of depression and anxiety for different family functioning groups varied at different time points, and the interaction effect was significant (χ2 = 52.97, p < 0.001 and χ2 = 51.25, p < 0.001, respectively). The incidence rate of depression and anxiety for different family functioning groups with different coping styles also varied at different time points, the interaction effect was likewise significant (χ2 = 862.09, p < 0.001 and χ2 = 583.29, p < 0.001, respectively).ConclusionsHaving a severely dysfunctional family and a negative coping style increase the incidence rates of depression and anxiety. These findings highlight the importance of paying special attention to college students' family functioning and promoting appropriate coping strategies during and after COVID-19.

Role of the Psychological Test and Evaluation System Based on the Internet of Things in the Early Warning of Psychological Dangers of College Students

The mental health and psychological crisis of college students has aroused the attention of all walks of life. College students are unable to cope with various pressures, and crisis situations such as suicide are not uncommon. Solving the psychological crisis of college students is urgent. The psychological crisis of college students is a major psychological imbalance that occurs when individual college students or groups believe that they cannot solve, handle, and control themselves or face unimaginable learning, work, and life events. This paper aims to study the role of psychological evaluation system based on the internet of things in the early warning of college students' psychological crises. This paper uses the SCL-90 scale method to quickly classify the mental health problems of college students.

Effectiveness of a guided internet-based intervention for procrastination among university students – A randomized controlled trial study protocol

Procrastination is a widespread problem that is highly prevalent among the young adult population and is associated with several negative consequences. However, current evidence on the effectiveness of e-health interventions for procrastination either lack a comparison to an inactive control, do not include a student population or are of poor quality. This protocol describes the design of a trial that will overcome these limitations and examine the effectiveness of a guided internet-based intervention (GetStarted) to reduce problematic procrastinating behaviors in college students compared to a waitlist control. This study will be a two-armed randomized controlled trial with a calculated sample size of N = 176. Participants will be students from seven universities in the Netherlands. The intervention group will receive a four-week e-coach-guided intervention for procrastination. The waitlist control group will get access to treatment four weeks after randomization. Assessments will take place at baseline, post-test (4 weeks post-baseline) and follow-up (6 months post-baseline). Data will be analyzed with an intent-to-treat principle. The primary outcome is change in procrastination behaviors measured on the Irrational Procrastination scale (IPS). Secondary outcomes are depression, anxiety, stress, and quality of life. Additionally, sociodemographic characteristics of the participants, satisfaction with treatment, program usability, satisfaction with e-coach and treatment adherence will be examined as potential moderators. The results from this study can build evidence for the effectiveness of a guided internet-based intervention for treating procrastination in college students. Should it be effective, GetStarted could provide a flexible, low-intense and cost-effective treatment for procrastination and prevent common mental health problems in college students. Trial registrationThis trial is registered at ClinicalTrials.gov Protocol Registration and Results System (Trial number: NCT05478096).

The Visualization Analysis of Sleep Quality Research Literature of Chinese College Students Based on Citespace

This study explores the current research hotspots and trends of sleep quality in college students by bibliometric analysis, and to provide reference for further research in this field. The study uses CNKI as the retrieval data source and use Citespace and the visualization fuction of CNKI to visualized the included literatures. A total of 212 literatures were included in this study. The publication of relevant literature shows an increasing trend in general, and there is no cooperative relationship between the publishing institutions. Research hotspots include the status quo, influencing factors and intervention measures of sleep quality of college students. The regression analysis of sleep quality and mental health problems of college students are recent research frontiers. Future research needs to strengthen collaboration between different institutions and authors. In order to solve the problem of college students’ sleep quality, it needs not only self-adjustment, but also the joint efforts and care of school, family and government, and the implementation of intervention measures to solve this problem.

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.