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

College students are under increasing competition pressure, which has a negative impact on their mental health, as the pace of learning and life accelerates, as well as the increasingly difficult employment situation. As a result, emphasizing the importance of college students’ mental health and fully addressing it has become a top priority in the work of colleges and universities. However, some students and even teachers are currently unconcerned about mental illness, making it difficult for students with psychological abnormalities to receive timely detection and effective treatment. As a result, it is the responsibility of student management for colleges and universities to identify and intervene early in the mental health problems of college students. Through the use of multimodal data and neural network models, it is now possible to evaluate and predict the mental state of college students in real time, thanks to the advancement of intelligent technology. Therefore, a novel multimodal neural network model is proposed in this paper. Our model is divided into two branches in particular. The traditional mental health assessment and prediction algorithm, which is based on the improved BP neural network and the International Mental Health Scale SCL‐90, is one of the branches. Given how difficult it is to meet the requirements for the accuracy of college students’ mental health assessments using this method, our other branch is computer vision‐based facial emotion recognition of college students, which is used to aid in the evaluation of mental health assessments. Our model demonstrates competitive performance through simulation and comparative experiments.

Background and Aims: Objective: The increasing number of mental health problems in college students encourages researchers to explore new intervention methods. As an art form containing profound cultural connotations, traditional Chinese painting shows its unique potential for emotional expression and spiritual healing. Research Methodology: This study used an experimental design, and 120 college students participated in the initial mental health assessment, including anxiety (GAD-7), depression (PHQ-9), and self-efficacy (GSE). 30 students with obvious mental health problems were selected and randomly divided into an experimental group and a control group, 15 each. Result: The experimental group participated in the 8-week Chinese painting therapy course (once a week for 90 minutes each), and the control group did not receive any intervention. After the intervention, both groups again received the same mental health assessment. After the intervention, anxiety scores decreased by 25% (p <0.001), depression scores by 30% (p <0.001), and self-efficacy increased by 22% (p<0.001). Qualitative analysis showed that participants reported significant improvements in emotional expression, self-perception, and a sense of inner peace. Conclusion: Chinese painting healing has shown a remarkable effect in relieving anxiety and depression and improving self-efficacy. This study verified the feasibility of traditional Chinese painting as a tool for mental health intervention and suggested more widespread application in mental health services for college students.

In 2020, the epidemic spread rapidly and frightening. During the period of epidemic prevention and control, college students did not go out and lived in isolation at home, so some students had mental health problems, such as the lack of normal face-to-face social interaction, the unmet need to talk; family contradictions occurred from time to time, resulting in large fluctuations in college students’ mental health. This paper analyzes the causes of college students’ psychological problems under the background of the epidemic situation, and carries out the experimental data collection and statistical analysis in the form of scale test and telephone interview. The results show that college students are prone to psychological problems in the context of the epidemic, and senior students and female students are more likely to have psychological problems. This paper provides a theoretical basis for solving the mental health problems of college students, and has important reference significance.

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.

Background With the popularization of higher education and the intensification of talent competition, the academic pressure faced by college students has become normalized and diversified, and the relationship between academic pressure and mental health has become a research focus in the fields of educational management and psychology. Traditional research mostly relies on questionnaire surveys and focuses on static correlations. It has limitations such as data lag and single indicators, and it is difficult to capture the dynamic patterns of the two changes over time. With the advancement of smart campus construction, the accumulation of multi-source big data such as course grades, attendance records, and mental health assessments has provided the possibility to break through traditional research bottlenecks. The study uses campus big data to quantitatively analyze the dynamic relationship and key influencing factors between academic stress and mental health. The study provides directions for colleges and universities to carry out academic adjustments and mental health intervention, and helps college students grow up healthily. Methods The study selected 2863 full-time undergraduates from a certain university as a sample, including 1521 boys and 1342 girls; 789 freshmen, 856 sophomores, 732 juniors, and 486 seniors. Campus big data was collected for 2 academic years (4 semesters), including academic data (credits, grades, attendance, homework submissions) and mental health data (depression and anxiety scale scores, psychological consultation records). Then we construct a comprehensive index of academic stress (including two dimensions: academic burden and performance) and core indicators of mental health (depression, anxiety level, comprehensive rating), and explore the dynamic correlation through Pearson correlation and cross-lagged analysis. Results Research shows that the academic stress index of college students peaks in their sophomore year and falls back in their senior year, with an average score of 58.3 ± 12.6; 18.3% of students have mild or above depression, and 15.7% have mild or above anxiety. There is a significant positive correlation between academic stress and depression and anxiety scores in each semester (r = 0.32, p<.001), and stress and psychological problems are significantly intensified before and after exam week. Academic stress in the previous semester can significantly predict mental health status in the next semester (β = 0.23, p<001), but the reverse prediction effect is weak. In addition, the correlation strength among female students, low academic performance, and sophomore students is higher (β = 0.41), while good social interaction and regular schedule can buffer the impact of stress. The results show that the impact of academic pressure on mental health is cumulative and significantly affects students' mental health. Discussion Through campus big data analysis, the study clarified that there is a significant positive correlation between college students' academic stress and mental health, and that the strength of the dynamic correlation is divided into groups (such as girls and students with academic difficulties). It also revealed the buffering effect of social support and work and rest patterns, indicating that colleges and universities should carry out special stress management to strengthen students' psychological endurance during peak stress periods, enrich campus social activities, and guide healthy work and rest. Future research can expand the sample scope, include family background, personality traits and other variables, and combine machine learning to build a risk prediction model to improve the accuracy of intervention, thereby providing a basis for colleges and universities to formulate differentiated psychological intervention. Funding: No. 24ZK0103; No. 2024SZA10; No. SCJG24B097.

Background With the intensification of social competition and the acceleration of the pace of life, the mental health problems of college students have become increasingly prominent, especially the students majoring in human resource management. Due to their professional characteristics, they need to have strong interpersonal communication skills and emotional management skills, so they have higher requirements for mental health. In recent years, scholars at home and abroad have conducted extensive research on the mental health problems of college students, especially the intervention measures of depression. The purpose of this study is to explore the effects of psychological intervention on depression of college students majoring in human resource management, in order to provide scientific basis and practical guidance for college mental health education. The research motivation is to identify and evaluate the effectiveness of psychological intervention, the idea is to verify the actual effect of psychological intervention measures through empirical research, and the outcome is expected to provide empirical support for mental health intervention in colleges and universities. Methods This study adopted a randomized controlled trial design, and selected 200 students majoring in human resource management from two universities as the research objects, randomly divided into the intervention group and the control group, 100 people in each group. The intervention group received 90 minutes of comprehensive psychological intervention once a week for 8 weeks, covering cognitive restructuring, emotional management skills, stress coping strategies and career planning guidance. The control group maintained the original state of learning and life, and did not receive additional intervention. The Beck Depression Inventory-II (BDI-II) was used as the main assessment tool to assess all participants before and after the intervention. Finally, semi-structured interviews were conducted to collect participants’ subjective feedback on the intervention. Results The data showed that there was no significant difference in BDI-II scores between the two groups before intervention (P>0.05), and after intervention, BDI-II scores in the intervention group were significantly decreased (P<0.01), while there was no significant change in the control group (P>0.05). Seventy-two percent of the students in the intervention group experienced relief from depression, compared with 28 percent in the control group. The interview results showed that students in the intervention group generally reported improvements in cognitive flexibility, emotional regulation, and career planning clarity. Discussion The results show that comprehensive psychological intervention has a significant effect on improving the depression of college students majoring in human resource management, which not only reduces the severity of depressive symptoms, but also improves the mental health level and career planning ability of students. The results not only enrich the theoretical system of college students’ mental health education, but also provide targeted intervention strategies for college students’ mental health education practice. Future research may further explore the long-term effects of different intervention modes, and how to better integrate psychological intervention into daily teaching and management in colleges and universities to form a continuous psychological support system.

Background/Objectives: There is only limited research work is going on in the field of facial expression recognition on low resolution images. Mostly, all the images in the real world will be in low resolution and might also contain noise, so this study is to design a novel convolutional neural network model (FERConvNet), which can perform better on low resolution images. Methods: We proposed a model and then compared with state-of-art models on FER2013 dataset. There is no publicly available dataset, which contains low resolution images for facial expression recognition (Anger, Sad, Disgust, Happy, Surprise, Neutral, Fear), so we created a Low Resolution Facial Expression (LRFE) dataset, which contains more than 6000 images of seven types of facial expressions. The existing FER2013 dataset and LRFE dataset were used. These datasets were divided in the ratio 80:20 for training and testing and validation purpose. A HDM is proposed, which is a combination of Gaussian Filter, Bilateral Filter and Non local means denoising Filter. This hybrid denoising method helps us to increase the performance of the convolutional neural network. The proposed model was then compared with VGG16 and VGG19 models. Findings: The experimental results show that the proposed FERConvNet_HDM approach is effective than VGG16 and VGG19 in facial expression recognition on both FER2013 and LRFE dataset. The proposed FERConvNet_HDM approach achieved 85% accuracy on Fer2013 dataset, outperforming the VGG16 and VGG19 models, whose accuracies are 60% and 53% on Fer2013 dataset respectively. The same FERConvNet_HDM approach when applied on LRFE dataset achieved 95% accuracy. After analyzing the results, our FERConvNet_HDM approach performs better than VGG16 and VGG19 on both Fer2013 and LRFE dataset. Novelty/Applications: HDM with convolutional neural networks, helps in increasing the performance of convolutional neural networks in Facial expression recognition. Keywords: Facial expression recognition; facial emotion; convolutional neural network; deep learning; computer vision

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

While substantial evidence links single lifestyle factors to mental health, research on how multiple co-occurring healthy lifestyle behaviors relate to mental health, particularly among college students, remains limited. This study aimed to identify distinct profiles of healthy lifestyle behaviors and examine their associations with mental health among college students. This cross-sectional study included 1340 college students (mean age = 19.4 years; SD = 1.2). Self-reported data were collected on diet, sleep, physical activity (PA), screen time, and sedentary behavior. Mental health was assessed using the Depression, Anxiety and Stress Scale-21 (DASS-21). Latent profile analysis was used to identify groups with similar lifestyle characteristics. Multiple linear regression was applied to examine associations between lifestyle profiles and mental health. Three lifestyle profiles were identified: "Active Engagement" (n = 520, 38.8%), "Moderate Engagement" (n = 478, 35.7%), and "Negative Engagement" (n = 342, 25.5%). These groups showed significant differences in anxiety, depression and stress levels (p < 0.001). Monthly family income and sex predicted profile membership. Compared with the "Active Engagement" group, the "Moderate Engagement" and "Negative Engagement" groups showed a higher risk of mental health problems. Our findings suggest that interventions specifically targeting the active lifestyle pattern (characterized by the higher PA, low sedentary behavior and screen time) may be particularly effective for improving mental health in college students, moving beyond simply promoting single health behaviors.

This article explores the intervention effect and related strategies of music psychology group assistance in alleviating anxiety among college students. The study analyzed the current situation of anxiety among college students, including factors such as academic pressure, social pressure, and future anxiety, as well as various factors that affect anxiety, such as academic pressure, social factors, and family background. This article puts forward some strategies based on the theory of music psychology group assistance, including universities providing support and resources, society actively promoting mental health, and individuals actively participating in self-management. The comprehensive application of these strategies can help college students better manage anxiety and improve their mental health. Music psychology group assistance is a promising mental health intervention method that can help college students effectively relieve anxiety and improve their mental health and quality of life. This study provides a useful reference for universities, society, and individuals to better deal with the mental health problems of college students.

BackgroundPrimary schoolteachers play a pivotal role in the education of children, highlighting the importance of addressing their psychological well-being and mental health. While the Depression, Anxiety, and Stress Scale-21 (DASS-21) is a globally recognized instrument for evaluating mental health, its systematic validation among primary schoolteachers, particularly in a cross-national context, remains understudied. The present cross-cultural study evaluated the psychometric properties of DASS-21 among primary schoolteachers in Spain and China to compare the DASS-21 between teachers across Western and Eastern cultures.MethodsThe study sample comprised 1,350 Spanish and 2,580 Chinese primary schoolteachers. The DASS-21, alongside the Emotional Exhaustion Scale for Teachers, was used to evaluate its psychometric properties. The construct and concurrent validity of the DASS-21 were examined using Rasch analysis, Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). Additionally, measurement invariance was tested across two national teacher cohorts.ResultsRasch analysis confirmed the validity of most DASS-21 items among both the Spanish and Chinese teachers, with only the same item from the Anxiety subscale showing inadequate fit in both countries. CFA across both countries initially favored a bifactor model, which was subsequently excluded due to problematic factor loadings. Instead, a single-factor model provided the best fit for Chinese teachers, while a correlated three-factor model was optimal for Spanish teachers. SEM demonstrated the DASS-21’s concurrent validity with emotional exhaustion, with consistent findings across both samples. After excluding Differential Item Functioning (DIF) items, strict measurement invariance between Spanish and Chinese teachers was achieved, as verified by multi-group CFA.ConclusionThe DASS-21 exhibits commendable psychometric properties, rendering it a suitable instrument for evaluating the mental illness of primary schoolteachers in both Spain and China.

Colleges and universities in China, while being responsible for providing talents to the society, have also integrated the function of shaping the spiritual quality of talents. In addition, with the society's emphasis on psychological problems of college students, physical education and health education have become important courses for shaping the mental and mental health of talents. Therefore, this article focuses on the intervention of college sports and health on the mental health of college students. Firstly, the background environment of sports and health theory is analyzed. Secondly, it analyzes the common mental health problems of college students. Thirdly, it explains how to use physical education and health teaching to guide students to establish a correct mental health environment. It is intended to be able to use education intervention to comprehensively improve the quality of talents.

Association of mental health assessment with postoperative outcomes following major surgery in older individuals.

This paper analyzes the Status quo, specific educational mode and characteristics of students' mental health education in the foreign universities, and to learn advanced mental health education from foreign, to inspire the mental health education for college students in china

ABSTRACTMental health problems in school‐age children continue to be a major concern internationally. The school setting has increasingly been viewed as having a potential role in supporting the assessment of children's mental health, yet there is a paucity of studies which examine teachers' perceptions of this. There is significant debate regarding whether teachers should be responsible for identifying early signs of mental health difficulties in children. This qualitative study explores teachers' perceptions of assessment of mental health in primary schools. Within each of the three schools purposively sampled to take part in the study, class teachers and Special Educational Teachers working with children aged 8–12 years were invited to participate in semi‐structured interviews (n = 11). The researcher applied a deductive approach, based on Bronfenbrenner's (1979) framework as adapted by Harvest (2018), followed by an inductive approach, to code the data. The key theme that emerged was teachers' concern about a lack of training to equip them with the necessary skills to identify children who were experiencing mental health difficulties. This lack of training impacted on their confidence and knowledge in identifying children who might be at risk of mental health difficulties. Teachers also expressed uncertainty about their role regarding the assessment of mental health. Opinions varied on the effectiveness of communication between parents and teachers when assessing children's mental health. Findings suggest a need for further research to determine whether (more) teacher training in assessment of mental health will assist children who may be at risk of mental health difficulties.