Evaluating music education interventions for mental health in Chinese university student: a dual fuzzy analytic method

Abstract

This study addresses the growing anxiety and depression among Chinese university students by evaluating and ranking music education strategies to alleviate these issues. We integrates Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). FAHP was utilized to determine the weight of factors such as academic pressures, social relationships, and cultural norms, while fuzzy TOPSIS ranked the effectiveness of music education interventions based on these weights. The results revealed that ‘Mental health stigma’ and ‘Academic Pressures and Rigidity’ are among the highest weighted factors, significantly impacting student anxiety. ‘Music Appreciation and Music-Based Self-Care’ emerged as the most effective strategy. These results highlight the importance of direct involvement in music-related activities for improving student mental health.

Introduction

Mental health issues, especially anxiety and depression, have become significant challenges within the university student population¹. Throughout their university life, students face multiple sources of stress, including academic challenges, social pressures, uncertainties about the future, and the tasks of personal growth². These stressors can exacerbate psychological anxiety and depression, negatively impacting the quality of life and academic performance of university students³. As stress levels rise, the prevalence of mental health problems among university students is on the rise, imposing substantial burdens on both individuals and society⁴. With the development of higher education in China, Chinese university students are confronted with substantial academic and non-academic pressures. Previous research has suggested that moderate stress can be conducive to student success, whereas extreme stress can lead to anxiety, depression, and psychological issues. Within the context of university life, students are grappling with academic stress, employment concerns, and health-related worries⁵.

Academic pressures remain a major source of stress, with students facing rigorous coursework, high expectations from family and society, and competitive grading systems. The pressure to perform well academically can lead to sleepless nights, unhealthy coping mechanisms such as substance use, and a fear of failure. This academic stress is often compounded by the need to plan for the future, with many students feeling uncertain about their career prospects. The highly competitive job market in China means that students are constantly worried about securing internships, job offers, and ultimately, stable employment. Students often encounter an overwhelming volume of coursework that demands extensive study hours, leaving little time for rest or leisure activities. This intense academic workload frequently results in sleep deprivation, which can negatively affect cognitive function, emotional regulation, and overall health⁵. As a result, students may resort to unhealthy coping mechanisms such as excessive caffeine consumption, substance use, or skipping meals, which further degrade their physical and mental well-being. Family expectations add another layer of pressure. Many Chinese students feel the weight of their parents’ and relatives’ hopes and dreams, which can be burdensome. Families often invest heavily in their children’s education, both financially and emotionally, leading students to fear disappointing their loved ones. This fear of failure can manifest as chronic anxiety, leading to a detrimental cycle of stress and academic underperformance. The societal expectation to excel academically is equally demanding. In a culture that places a high value on educational success, students often feel compelled to achieve top grades to secure a prestigious place in society⁶. This competitive environment is exacerbated by grading systems that rank students against one another, fostering a sense of competition rather than collaboration. Such an environment can create feelings of inadequacy and self-doubt among students who struggle to meet these high standards. Planning for the future adds to the academic stress, as students must balance their current studies with long-term career planning. Many students feel unprepared for the job market, unsure of how to navigate the path from graduation to employment. The pressure to secure internships and gain relevant work experience during their studies can be overwhelming, as students try to build a competitive resume while maintaining their academic performance. The job market in China is highly competitive, with a large number of graduates competing for a limited number of positions. This intense competition means that students are under constant pressure to distinguish themselves through academic achievements, extracurricular activities, and networking. The uncertainty about securing stable employment after graduation can lead to persistent worry and stress, as students contemplate their future prospects and the potential consequences of not finding a suitable job.

Non-academic pressure is also a major source of stress for university students, significantly impacting their mental and emotional well-being. The transition to university life is often challenging, as students must adjust to living away from home, establishing new routines, and managing increased responsibilities independently⁷. This transition can generate considerable psychological insecurity and feelings of homesickness, especially for those who are far from their family support systems. Interpersonal relationships are another critical area of non-academic pressure. Students frequently face conflicts with roommates, which can create a tense and uncomfortable living environment. Disagreements over shared spaces, differing lifestyles, and personal habits can lead to chronic stress and affect a student’s ability to relax and concentrate on their studies⁸. Additionally, managing romantic relationships can be particularly stressful. The emotional ups and downs of romantic involvement, coupled with the demands of academic life, can lead to significant emotional strain. Breakups or conflicts within these relationships can be a major source of distress, impacting a student’s mental health and academic performance⁹. The rise of social media adds another layer of pressure, as students often compare their lives to the curated images and achievements of their peers, leading to feelings of inadequacy and low self-esteem. Peer pressure to engage in certain behaviors, such as partying or substance use, can conflict with academic goals and personal values, creating internal conflict and stress. Furthermore, social pressures also play a significant role in non-academic stress. The need to fit in, make new friends, and navigate the social landscape of university life can be daunting. Balancing work and personal life is another substantial non-academic stressor. Many students take on part-time jobs to support themselves financially, which can interfere with their academic responsibilities. Juggling work schedules with study time, social activities, and personal care can lead to burnout and exhaustion. Financial difficulties, in particular, are a significant source of anxiety. The high cost of tuition, textbooks, and living expenses means that many students are under constant financial strain, which can lead to stress about paying bills and affording basic necessities¹⁰. Environmental factors, such as chaotic living conditions, also add to non-academic pressure. Students living in cramped dormitories or shared apartments with limited privacy and noise disturbances may find it difficult to study or relax. Additionally, the increased use of technology for academic purposes can lead to physical strain, such as eye fatigue and poor posture, which can further exacerbate stress levels.

These challenges can have adverse effects on academic success and social adaptation, potentially resulting in poor academic performance, dropout, social isolation, and even more severe deterioration of mental health¹¹. Anxiety and depression not only adversely affect individuals but also present challenges to society and the education system as they can lead to declining academic performance, academic dropout, and long-term negative impacts on an individual’s career prospects and social relationships¹². Finding effective interventions to alleviate anxiety and depression symptoms has become an urgent task¹³. Music education has gained increasing attention as a potential non-pharmacological intervention for mental health in recent years¹⁴. Music education involves the process of developing an individual’s musical literacy through music learning, performance, composition, and appreciation¹⁵. As a non-pharmacological approach to mental health, music education has shown evident positive effects within the Chinese university student population¹⁶. Numerous studies have explored the relationship between music education and mental health, showing its potential to reduce anxiety and improve academic performance. One study on female music education students aimed to understand the mechanisms through which music alleviates psychological stress¹⁷. This research shows that the degree of liking the music was the most crucial factor in stress reduction, highlighting the importance of personal preference in music therapy. Another investigation focused on the benefits of music listening for induced state anxiety, using behavioral and physiological evidence¹⁸. Their study involved 62 subjects who were exposed to an anxiety-inducing visual stimulus before listening to either happy or neutral music. The findings indicated that both types of music effectively alleviated state anxiety, with distinct brain mechanisms underlying the effects. A review of literature on music education and socio-emotional learning (SEL) explored how music education contributes to the development of SEL skills, such as emotional regulation, empathy, and social interaction¹⁵. A systematic review and meta-analysis on the effectiveness of music therapy for stress reduction analyzed 47 studies with 2,747 participants and found that music therapy had a medium-to-large effect on reducing stress-related outcomes¹⁹. Another systematic review of the association between music education and mental health promotion found mixed outcomes²⁰. While six of the eleven studies reviewed reported a positive association, five reported a negative association. The positive outcomes were particularly notable in studies where music education was treated as an intervention, highlighting its potential as a therapeutic tool for mental health treatment despite the high prevalence of mental health issues among those with a music education background. Another study used fuzzy computing to analyze the impact of music education on students’ mental health²¹. The study extracted musical features and classified music types to determine their effectiveness in mental health interventions. Results showed that selecting appropriate music for interventions significantly improved students’ mental health, with psychological status scores improving by 0.73 times after the intervention, demonstrating the study’s validity.

Additionally, a systematic review and meta-analysis on the role of music in promoting health and well-being revealed significant benefits, further emphasizing the therapeutic potential of music²². The study found that active musical participation had beneficial effects on cognitive and psychosocial functioning, while passive listening mainly improved cognitive aspects. A systematic review of community interventions for anxiety and depression, focusing on activities like music groups, exercise, and gardening, included 31 studies with 2898 participants and found that community interventions, particularly community music and exercise, showed promise in reducing anxiety and depression symptoms²³. Another investigation found that students who participated in ethnic music and music education classes exhibited significantly lower anxiety levels compared to those who did not, suggesting that incorporating ethnic music culture and music education into university curricula can effectively reduce anxiety among college students²⁴. The study found that students who participated in ethnic music and music education classes exhibited significantly lower anxiety levels compared to those who did not. This suggests that incorporating ethnic music culture and music education into university curricula can effectively reduce anxiety among college students. Further, an examination of the Dualistic Model of Passion showed that harmonious passion for music leads to positive emotional experiences and improved psychological well-being, highlighting the importance of the nature of passionate engagement in determining the psychological impact of music²⁵. In summary, these studies highlight the therapeutic potential of music education in reducing psychological anxiety and aiding in the management of academic pressures. Whether through personal preference, the type of passionate engagement, or the integration of cultural elements, music plays a crucial role in enhancing mental health and well-being.

Understanding the psychological anxiety and depression among Chinese university students is crucial for developing effective music education strategies to improve their mental health. Therefore, in order to more comprehensively understand and solve the problem of psychological anxiety and depression among Chinese university students, a series of quantitative assessment tools and methods have been employed^26,27. These include the Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS)²⁸. To demonstrate the superiority of these methods, a comparative analysis was conducted with other fuzzy multi-criteria decision-making (MCDM) methods, such as fuzzy Delphi, fuzzy VIKOR, and fuzzy DEMATEL. Each of these methods offers unique advantages but also comes with certain limitations when applied to the context of this study. The fuzzy Delphi method is renowned for its systematic approach to achieving consensus among experts. However, it primarily focuses on filtering and refining expert opinions and lacks the detailed weighting capabilities provided by FAHP. Without precise weight determination, it becomes challenging to prioritize interventions effectively. Fuzzy VIKOR excels in ranking alternatives and identifying compromise solutions by focusing on the closeness to the ideal solution. Despite its strengths, fuzzy VIKOR does not handle the fuzziness in decision-makers’ judgments as robustly as fuzzy TOPSIS. Fuzzy TOPSIS, on the other hand, incorporates the fuzziness in both the criteria weights and the performance ratings, ensuring a more comprehensive evaluation. This robustness is vital for making reliable decisions in the complex and uncertain domain of mental health interventions. Fuzzy DEMATEL is particularly useful for understanding the cause-effect relationships among factors, offering insights into the interdependencies and feedback loops within the system. While this method is excellent for mapping out the relationships and influences between factors, it falls short in providing a clear ranking of intervention strategies. The lack of a straightforward ranking mechanism can complicate the decision-making process when multiple interventions need to be evaluated and prioritized.

In contrast, the FAHP method effectively determines the weights of various factors contributing to anxiety and depression²⁹. By integrating expert judgments into a hierarchical structure and employing fuzzy logic to handle the inherent uncertainty, FAHP provides a comprehensive understanding of the mental health landscape. This method allows for a nuanced analysis of the importance of each factor, facilitating targeted interventions. Fuzzy TOPSIS complements FAHP by ranking the effectiveness of music education interventions based on the weights derived from FAHP. By combining FAHP’s detailed weighting process with fuzzy TOPSIS’s robust ranking mechanism, the proposed approach enhances the accuracy and reliability of the evaluation process. This integrated approach offers a superior framework for assessing and ranking music education interventions, ensuring that the selected strategies are both effective and contextually relevant. Consequently, the proposed FAHP and fuzzy TOPSIS methods stand out as the most suitable and effective tools for this study, demonstrating their superiority over other fuzzy MCDM methods in the context of evaluating mental health interventions for Chinese university students.

Materials and methods

Theoretical framework

Mental health problems, especially anxiety and depression, have become a serious point of concern in university life. Music education was identified as a potential intervention that could help alleviate these problems. The following is a flowchart of our study and its interpretation.

As shown in Fig. 1, the research is divided into four complete phases, beginning with a “Literature Review”, which constructs the methodology of the research through an extensive review of existing academic works. This is followed by the “Model Construction” phase, which involves identifying and analyzing the key factors that affect the objectives of our research. Then, in the “Method Implementation,” phase, the results of the study are calculated. The last phase is “Discussion and Conclusion”, including weight analysis of research factors and ranking of interventions.

Fig. 1

The fow chart of research framework.

Causes of psychological anxiety and depression

Psychological anxiety and depression are complex psychological states, and their intrinsic mechanisms cover multiple dimensions, including emotional response, cognitive state, physical response, and social pressure³⁰. The complex interaction of these factors together shapes an individual’s mental health. As shown in Fig. 2, this model of psychological anxiety and depression visualizes the interplay between these complex factors. A deeper understanding and study of these intrinsic mechanisms can help us better understand and cope with psychological anxiety and depression, providing useful insights for improving mental health.

Fig. 2

Psychological anxiety and depression mechanism diagram.

As shown in Fig. 2, the psychological anxiety and depression model reflects these complex internal mechanisms, emphasizing their interaction and influence.In terms of emotional responses, the inability to experience happiness and the constant state of tension and anxiety create an emotional landscape that is often bleak and overwhelming. The fluctuations in mood and irritability can strain relationships and social interactions, leading to further isolation and loneliness. The sensations of fear and panic disrupt daily routines and hinder the ability to engage in meaningful activities, creating a barrier to experiencing moments of joy and satisfaction. Cognitively, anxiety and depression significantly impact thought processes. Individuals may struggle with concentration and memory, finding it challenging to focus on tasks or retain information. This cognitive impairment is often accompanied by a tendency towards pessimistic thinking, where the individual constantly expects the worst outcomes and doubts their abilities. Such negative thinking patterns can lead to excessive worry about future events and a pervasive sense of uncertainty. Physical responses serve as a tangible manifestation of the psychological turmoil within. The rapid heartbeat, muscle tension, and other physical discomforts are not only distressing but also contribute to a heightened state of arousal that perpetuates anxiety. The chronic fatigue and sleep disturbances further weaken the body’s resilience, making it more difficult to cope with stressors and recover from daily challenges. These physical symptoms create a feedback loop where the body’s stress response fuels the psychological symptoms, and vice versa. Socially, anxiety and depression can lead to significant changes in behavior and interaction patterns. Individuals may withdraw from social activities and relationships, feeling too overwhelmed to engage with others. This social withdrawal can lead to feelings of loneliness and isolation, which further deepen the emotional and cognitive symptoms. Academic procrastination and avoidance of responsibilities are common, as individuals feel unable to cope with the demands placed upon them. Overall, the multidimensional nature of anxiety and depression requires a holistic approach to understanding and treatment. Addressing the emotional, cognitive, physical, and social aspects simultaneously can lead to more effective interventions and support strategies.

Music education intervention mechanisms for psychological anxiety

In the aspect of relieving the psychological anxiety of university students, music education integrates biology, social psychology and culture into a whole, forming a comprehensive relationship³¹. Promoting mental health and emotional balance through music education, taking into account biological, socio-psychological and cultural influences.

Biologically, music has a profound impact on the human brain and body. Listening to or creating music can trigger the release of neurotransmitters such as dopamine and endorphins, which are associated with feelings of pleasure and well-being. Music can also reduce the levels of cortisol, a stress hormone, thereby alleviating anxiety and promoting relaxation. These physiological effects highlight the potential of music education to directly influence the biological mechanisms underlying stress and anxiety, offering a natural and accessible method for students to manage their mental health. From a socio-psychological perspective, music education provides students with opportunities for social interaction and connection. Participating in musical activities, whether in a choir, band, or informal jam session, fosters a sense of belonging and community. This social support is crucial for mental health, as it helps students feel connected and less isolated. Additionally, the collaborative nature of many musical endeavors teaches important social skills such as communication, teamwork, and empathy, which can further enhance emotional resilience and reduce anxiety. Culturally, music education allows students to explore and express their identities. Engaging with music from different cultures broadens students’ perspectives and promotes cultural understanding and appreciation. This cultural engagement can be particularly empowering for students from diverse backgrounds, as it validates their cultural heritage and fosters a sense of pride and self-worth. Moreover, music often serves as a powerful outlet for emotional expression, enabling students to articulate feelings that might be difficult to express through words alone. This emotional release can be therapeutic, helping to alleviate the psychological burden of anxiety.

The integration of these biological, socio-psychological, and cultural dimensions in music education creates a holistic approach to mental health. By addressing the multiple facets of student well-being, music education not only helps to reduce anxiety but also promotes overall emotional balance and resilience. Educators can design music programs that incorporate these elements, providing students with a supportive and enriching environment that nurtures their mental health. Thus, universities can implement various music-related initiatives to support student mental health. For example, these might include offering music therapy sessions, creating spaces for informal musical gatherings, and incorporating music into broader wellness programs. By leveraging the power of music, universities can create a more supportive and emotionally balanced campus environment. In conclusion, the integration of music education into efforts to relieve psychological anxiety among university students represents a comprehensive approach that considers biological, socio-psychological, and cultural influences. By promoting mental health and emotional balance through music, educators can provide students with effective tools to manage anxiety and enhance their overall well-being. This holistic approach highlights the importance of interdisciplinary strategies in addressing complex psychological issues and highlights the unique role of music education as a multifaceted therapeutic modality.

Evaluation model construction

To identify the factors included in the model, an interdisciplinary literature review was conducted to draw on factors from previous studies³². Thus, on the basis of interdisciplinary theoretical framework, this paper constructs an evaluation model of psychological anxiety and depression of Chinese university students based on fuzzy analytic hierarchy process. The proposed evaluation model consists of 6 main factors and 19 sub-factors (see Fig. 3). The six main factors include academic pressures and rigidity, parental expectation facotor, cultural facotor, social relationships, financial stress, mental health stigma.

Fig. 3

The evaluation model for psychological anxiety and depression.

The first main factor, academic pressures and rigidity (A), includes three sub-factors that significantly impact students’ mental health³³. The competitive grading systems (A1) in place create a high-pressure environment where students constantly strive for top grades, leading to stress. This intense competition can result in a relentless pursuit of academic excellence, often at the expense of students’ well-being. Additionally, the uncertainty about future career prospects and job security (A2) further contributes to anxiety, as students are often unsure about their professional futures and the stability of their potential careers. Societal expectations (A3) regarding academic success also add to the burden, as students feel immense pressure to meet these often unrealistic standards. These societal pressures can create a sense of inadequacy and fear of failure, exacerbating anxiety and depression among students. Parental expectation factors (B) form the second major component of the model. High parental expectations for academic achievement (B1) can lead to significant stress and anxiety among students. Parents’ aspirations for their children to excel academically can place an immense burden on students, who may feel obligated to meet these high standards. The cultural expectation to honor and meet parental aspirations, known as filial piety culture (B2), can be a considerable stressor. In many Asian cultures, filial piety involves a deep sense of duty and responsibility towards one’s parents, often leading to additional pressure on students to succeed academically and make their parents proud. Furthermore, the fear of disappointing parents (B3) can cause substantial anxiety and depression, as students worry about failing to meet their parents’ high expectations. This fear can result in a constant state of stress and a heightened risk of mental health issues. The third factor, cultural factors (C), includes elements that shape students’ experiences and mental health. The pressure to conform to cultural norms (C1) can significantly impact mental health, as students may feel compelled to adhere to societal expectations and traditional values, often at the expense of their own individuality and preferences. This pressure can lead to internal conflict and stress. Additionally, the importance placed on group solidarity (C2), which sometimes suppresses individual needs, can be challenging for students. The cultural emphasis on saving face (C3) can also prevent students from seeking help for mental health issues, further exacerbating their anxiety and depression.

Social relationships (D) represent the fourth main factor affecting students’ mental health³⁴. Social dynamics that affect a student’s sense of identity and self-worth (D1) can have a profound impact. Positive social interactions and supportive relationships can enhance students’ self-esteem and overall well-being, while negative interactions can lead to feelings of inadequacy and depression. Challenges in forming and maintaining friendships (D2) can contribute to feelings of loneliness and anxiety. The ability to establish and sustain meaningful relationships is crucial for emotional support and mental health. Additionally, social isolation (D3) resulting from a lack of social interaction and support can lead to depression and anxiety. Students who feel isolated may struggle to find the emotional support they need, exacerbating their mental health issues. Financial stress (E) is the fifth factor in the model. Constant financial concerns (E1) can cause chronic stress, as students worry about their ability to afford tuition, living expenses, and other costs associated with their education. Financial difficulties can create a constant state of worry and insecurity. The feeling of being alone in dealing with financial burdens, termed economic solitude (E2), can exacerbate anxiety. Students who lack financial support may feel overwhelmed by their financial responsibilities, leading to increased stress and anxiety. The stigma and shame associated with financial difficulties (E3) can prevent students from seeking help, adding to their stress and contributing to a cycle of financial and mental health issues.

Finally, mental health stigma (F) is the sixth factor influencing students’ psychological well-being^35,36. Cultural norms (F1) surrounding mental health can affect students’ willingness to seek help and their perception of mental health issues. Mental health issues are stigmatized, leading to a reluctance to acknowledge and address these concerns. The fear of discrimination (F2) due to mental health concerns can prevent students from accessing necessary support. Students may worry about being judged or treated differently if they disclose their mental health struggles. Additionally, the collectivist values prevalent in the culture (F3) can sometimes lead to the neglect of individual mental health needs. In collectivist cultures, the needs and well-being of the group are often prioritized over those of the individual, which can result in the neglect of personal mental health issues. Public misunderstanding (F4) about mental health issues within the community can contribute to stigma, further preventing students from seeking help.

Solutions to music education practices

Music education offers a range of effective solutions for addressing psychological anxiety and depression among university students, as shown in Table 1. These solutions include various methods that leverage the emotional, social, and cognitive benefits of music to improve mental health and well-being.

Table 1 Solutions of psychological anxiety and depression.

Music education offers a range of effective solutions for addressing psychological anxiety and depression among university students. One significant method is Music Appreciation and Music-Based Self-Care (S1). This approach encourages students to engage in listening to music as a form of self-care^37,38,39. Activities such as attending concerts, creating personal playlists, and actively engaging with various genres of music can significantly enhance emotional well-being. Research has shown that listening to music can reduce stress, improve mood, and provide a sense of comfort and relaxation, making it a valuable tool for self-care and mental health maintenance. Additionally, digital music platforms provide an accessible means for individuals to explore, share, and connect with music, further enhancing their engagement and emotional well-being. These platforms offer a vast library of music genres, artists, and playlists that cater to diverse tastes and preferences, allowing users to discover new music that resonates with their emotional states. Aigital platforms often incorporate social features that enable users to connect with friends, family, and even strangers who share similar musical interests, building a virtual community that supports emotional well-being. Combining these methods, both music education and digital music platforms, creates a comprehensive approach to leveraging music for mental health benefits. By integrating traditional music appreciation practices with modern digital tools, students can experience the full spectrum of music’s therapeutic potential, promoting overall emotional resilience and mental health.

Another impactful approach is Music Instruction and Expressive Arts (S2), which involves formal music education, such as learning to play an instrument or vocal training. This method integrates expressive arts, allowing students to express their emotions through music^40,41. The process of learning and creating music can be therapeutic, providing an outlet for emotional expression and helping to manage anxiety and depression. Studies have demonstrated that engaging in music instruction not only develops musical skills but also fosters a sense of accomplishment and boosts self-esteem, contributing to overall emotional well-being. Music instruction offers structured learning experiences that challenge students to develop discipline, patience, and perseverance. As students progress in their musical journey, they experience a sense of achievement and mastery, which can significantly enhance their self-confidence. Moreover, the act of playing an instrument or singing requires focused attention and concentration, providing a healthy distraction from stressors and negative thoughts. This immersive engagement can create a state of flow, a mental state where individuals are fully absorbed in an activity, leading to reduced stress and increased satisfaction. Furthermore, expressive arts integration within music instruction allows students to explore and communicate their emotions creatively. Composing music, improvising, or interpreting existing pieces encourages self-expression and emotional release. This creative process can serve as a form of self-reflection and emotional processing, enabling individuals to better understand and cope with their feelings.

Music Composition and Group Music-Making (S3) is another effective solution. This approach focuses on creating music, either individually or in groups. Group music-making activities, such as joining a band or choir, foster social interaction and collective emotional expression. The collaborative nature of these activities helps build a sense of community and support, which can alleviate feelings of isolation and loneliness^42,43. Research indicates that participating in group music-making can reduce anxiety and depression by promoting positive social connections and providing a shared emotional experience. Music composition allows for personal creativity and self-expression. Writing and arranging music provide an opportunity to convey one’s thoughts, emotions, and experiences uniquely and personally. This creative outlet can be therapeutic, helping individuals process and make sense of their emotions. Composing music can also offer a sense of control and agency, as individuals have the freedom to shape their musical creations according to their preferences and emotional needs. Participating in group music-making can also provide a platform for individuals to express emotions that may be difficult to articulate through words. The collective experience of creating and performing music can lead to a profound sense of shared understanding and empathy among participants. This emotional connection can be particularly beneficial for individuals dealing with mental health challenges, offering a sense of validation and support from others who share similar experiences.

Interdisciplinary collaboration (S4) combines music with other disciplines such as therapy, psychology, or education to create holistic approaches to mental health^44,45. This integrative method leverages the strengths of each discipline to provide more comprehensive care and support for individuals. For example, collaborations between music educators and mental health professionals can lead to the development of integrated programs that address both the emotional and psychological needs of students. These programs might include joint workshops, co-taught courses, or collaborative research projects that explore the intersections of music and mental health. Music therapy sessions are a key component of such interdisciplinary collaboration, offering structured environments where trained therapists use music to address specific psychological needs. These sessions help manage stress, anxiety, and depression through a variety of techniques. Therapists might employ listening to music, creating music, singing, and moving to music to facilitate emotional expression and processing. Each technique is chosen based on the individual’s needs and therapeutic goals. The therapeutic use of music can evoke memories, reduce physiological arousal, and promote relaxation. For example, listening to familiar songs can bring back positive memories, providing comfort and a sense of continuity. Creating music can offer an outlet for expressing complex emotions that might be difficult to articulate with words. Singing can improve breathing and posture, which are often linked to relaxation and stress relief. Moving to music can help release physical tension and improve mood through physical activity. Music therapy provides a safe space for individuals to explore their emotions and work through psychological challenges. In a typical session, the therapist might begin with a musical activity designed to help the individual feel comfortable and engaged. This might be followed by a more targeted intervention aimed at addressing specific emotional or psychological issues. The session might conclude with a discussion of the individual’s experiences and feelings, helping to integrate the therapeutic work into their broader understanding of themselves and their emotional lives.

Music Meditation (S5) utilizes music as a tool for meditation and mindfulness practices. Music meditation sessions can help reduce stress and promote relaxation, improving overall mental health. The calming effects of music, combined with meditation techniques, can create a peaceful environment conducive to mental clarity and emotional stability^46,47. Evidence supports the use of music meditation for reducing symptoms of anxiety and depression, highlighting its potential as a non-invasive and accessible mental health practice. The calming effects of music, combined with meditation techniques, can create a peaceful environment conducive to mental clarity and emotional stability. Music with a slow tempo, minimal lyrics, and harmonious tones is often chosen to enhance the meditative experience. These elements help to slow down the listener’s heart rate, reduce blood pressure, and decrease levels of the stress hormone cortisol. This physiological response can significantly contribute to a sense of relaxation and inner peace. Meditation practices, such as focused breathing, visualization, and body scans, are often integrated with music to deepen the meditative state. For instance, participants might be guided to synchronize their breath with the rhythm of the music, promoting a state of mindfulness and presence. Visualization techniques may involve imagining serene landscapes or calming scenes while listening to ambient sounds, further enhancing the relaxation experience. Studies have shown that regular participation in music meditation can lead to significant reductions in anxiety levels, improved mood, and enhanced overall mental health. Additionally, the practice of music meditation encourages self-reflection and emotional awareness, allowing individuals to process their feelings in a safe and supportive environment. In summary, Music Meditation (S5) is a valuable approach to enhancing mental health through the combination of music and meditation techniques. By creating a calming and peaceful environment, music meditation can reduce stress, alleviate anxiety and depression, and promote overall emotional stability.

Music engagement (S6) refers to active participation in various musical activities, such as joining music clubs, attending workshops, participating in musical theater, or engaging in spontaneous music-making sessions with friends^48,49. This active engagement in music stimulates the brain’s reward system, releases dopamine, and fosters a sense of joy and fulfillment. These activities provide a healthy distraction from stressors, enhance social bonds, and promote a positive self-image. Engaging with music in a social context fosters a sense of belonging and community, which is crucial for emotional well-being. Participating in music-related activities can improve cognitive functions, such as memory and attention, due to the mental stimulation involved in learning and performing music. Moreover, music engagement often requires teamwork and collaboration, fostering essential social skills and building a sense of camaraderie among participants. These shared experiences can lead to lasting friendships and support networks, which are vital for maintaining mental health and emotional stability. Culturally specific music practices further enhance this engagement by allowing individuals to connect with their heritage and cultural identity, fostering a deeper sense of belonging and emotional stability. Engaging with traditional music, folk songs, and cultural rituals through music provides a meaningful way for individuals to honor their cultural roots and maintain a connection to their community. These practices often involve communal participation, such as group singing, dancing, and musical performances, which strengthen social bonds and reinforce cultural values. By participating in culturally specific music practices, individuals can experience a sense of continuity and identity, which is essential for emotional resilience and well-being. Engaging in these practices not only preserves cultural heritage but also offers a therapeutic outlet for expressing and processing collective experiences and emotions. This integration of general music engagement with culturally specific practices creates a comprehensive approach to leveraging music for mental health benefits, promoting overall emotional resilience and mental health. In summary, music engagement, whether through general activities or culturally specific practices, offers a wealth of benefits for mental health and emotional resilience. By stimulating the brain, fostering social connections, and providing a means of cultural expression, music serves as a powerful tool for enhancing quality of life. Integrating these practices into everyday life and therapeutic contexts can maximize their positive impact, helping individuals navigate the complexities of modern life with greater emotional strength and stability.

Fuzzy analytic hierarchy process

The Analytic Hierarchy Process (AHP) is a leading method in Multiple Criteria Decision Making (MCDM) that combines quantitative and qualitative criteria for precise outcomes, but its precise numerical comparisons can be challenging to assess. To address imprecision and unpredictability in user preferences, the Fuzzy Analytic Hierarchy Process (FAHP) uses fuzzy numbers to facilitate comparisons, making it effective in applications like addressing university students’ anxiety through music education by systematically considering and evaluating criteria.

Step1 Triangular fuzzy numbers (TFN). In the context of evaluating music education interventions for mental health, think of TFNs as a way to capture the uncertainty in experts’ opinions about the importance of different factors affecting student mental health. For example, when experts rate how much “academic pressure” impacts anxiety, they might not be certain about the exact degree of impact but can provide a range. Thus, a fuzzy number A on R to be TFN if its membership function ${\mu }_{ \widetilde{A}} \left(x\right)$: R → [0,1] is equal to following Eq. (1)

$$ \mu_{{\tilde{A}}} (x) = \left\{ \begin{gathered} \frac{x - l}{{m - l}} \, for \, l \le x \le m \hfill \\ \frac{u - x}{{u - m}} \, for \, m \le x \le u \hfill \\ 0 \, otherwise \hfill \\ \end{gathered} \right. $$

(1)

where l $\le m\le u$, l and u can be expressed the lower and upper values of the respectively support of $\widetilde{A}$, and m for the modal value (as Fig. 4). When l = m = u, the TFN becomes a non-fuzzy number. The membership function ${\mu }_{ \widetilde{A}} \left(x\right)$ describes the degree to which a real number x belongs to the fuzzy set $\widetilde{A}$. This function is piecewise linear and increases from zero to one as x moves from l to m, then decreases from one to zero as x moves from m to n. R represents the domain over which the fuzzy number is defined, encompassing all possible real number values. For example, the minimum impact they believe academic pressure might have is represented by the lower value (l). The most likely impact is denoted by the modal value (m). Finally, the maximum impact they think is possible is indicated by the upper value (u).

Fig. 4

A triangular fuzzy number, A ̃ = (l, m, u).

The linguistic terms and graphical representation of triangular linguistic labels are shown in Table 2. Namely, the importance of one factor over another is divided into six levels. For example, the “academic pressure” may be very strongly more important (VSMI).

Table 2 Linguistic scales and fuzzy scales for importance.

Step 2 Creating a Fuzzy pair-wise comparison matrix.

The process of Chang’s FAHP can be discussed as follows⁵⁰, the fuzzy pairwise matrix ${\widetilde{A}=({\widetilde{a}}_{ij})}_{n\times n}$ can be mathematically defined as follow:

$$ \tilde{A} = \left[ {\begin{array}{*{20}c} {1} & {\tilde{a}_{12} } & \cdots & {\tilde{a}_{1n} } \\ {\tilde{a}_{21} } & 1 & \cdots & {\tilde{a}_{2n} } \\ \cdots & \cdots & \cdots & \cdots \\ {\tilde{a}_{n1} } & {\tilde{a}_{n2} } & \cdots & 1 \\ \end{array} } \right] = \left[ {\begin{array}{*{20}c} {1} & {\tilde{a}_{12} } & \cdots & {\tilde{a}_{1n} } \\ {1/\tilde{a}_{12} } & 1 & \cdots & {\tilde{a}_{2n} } \\ \cdots & \cdots & \cdots & \cdots \\ {1/\tilde{a}_{1n} } & {1/\tilde{a}_{2n} } & \cdots & 1 \\ \end{array} } \right] $$

(2)

where Each factor ${\widetilde{a}}_{ij}$ represents the fuzzy relative importance of factor a i over factor j. In this study, when evaluating different factors affecting student mental health (like academic pressure, social relationships, and financial stress), experts compare these factors two at a time. Instead of giving a precise importance level, they provide a range to express uncertainty. For example, academic pressure is somewhat more important than social relationships, but it is uncertain how much.

For S_i, the fuzzy synthetic extent value, with respect to the i-th object is express as:

$$ S_{i} = \sum\limits_{j = 1}^{m} {M_{ij} \otimes \left[ {\sum\limits_{i = 1}^{n} {\sum\limits_{j = 1}^{m} {M_{ij} } } } \right]}^{ - 1} $$

(3)

Step 3 Calculating the weights. This step involves calculating the relative importance (weights) of various factors affecting mental health using the degree of possibility between fuzzy numbers. The degree of possibility of S_j = (l_j, u_j, m_j) ⩾ S_i = (l_i, u_i, m_i) is denoted as:

$$ V(S_{j} \ge S_{i} ) = height(S_{i} \cap S_{j} ) = \left\{ \begin{gathered} 1 \, if \, m_{j} \ge m_{i} \hfill \\ 0 \, if \, l_{i} \ge u_{j} \hfill \\ \frac{{l_{i} - u_{j} }}{{(m_{j} - u_{j} ) - (m_{i} - l_{i} )}} \, otherwise \hfill \\ \end{gathered} \right. $$

(4)

This quantifies the likelihood that one factor, expressed as a fuzzy number V (S_i ≥ S_j), is at least as important as another factor, represented by S_j = (l_j, u_j, m_j). The formula for this degree of possibility considers three cases: if m_j is greater than or equal to m_j, if l_j is greater than or equal to u_j, and an otherwise case that involves calculating the overlap between the two fuzzy numbers.

To compare between S_j and S_i, it is required to calculate both V (S_i ≥ S_j) and V (S_j ≥ S_i). The minimum degree of possibility d(i) of V(S_j ≥ S_i) for i, j = 1, 2, . . ., k is calculated as follows:

$$ \begin{gathered} \begin{array}{*{20}c} {V(S \ge S_{1} ,S_{2} ,S_{3} , \cdots ,S_{k} )} \\ { = V[(S \ge S_{1} ),(S \ge S_{2} ) \cdots (S \ge S_{k} )]} \\ \end{array} \hfill \\ = minV(S \ge S_{i} ),i = 1, \, 2, \, 3, \cdots ,k \hfill \\ \end{gathered} $$

(5)

If assumed $d\left( {A_{i} } \right) = \min V(S \ge S_{i} )for \, i = 1,2,, \cdots ,k.$ Then, the weight vector W is given as:

$$ W = \left( {d\left( {A_{1} } \right),d\left( {A_{2} } \right), \cdots ,d\left( {A_{n} } \right)} \right)^{T} $$

(6)

where A_i (i = 1,2,…, n) are n elements. The weight vector W is constructed using the minimum degrees of possibility for each factor. This weight vector is normalized to sum to one, ensuring that it accurately represents the relative importance of each factor. In this step, we determine the relative importance (weights) of each factor affecting mental health. By comparing the degree of possibility of different factors, we calculate a normalized weight vector W that reflects the significance of each factor.

Step 4 The consistency tests.

To ensure the attainment of reliable and rational assessment outcomes, it becomes imperative to subject the evaluation process to a rigorous examination of consistency. Inconsistencies can lead to incorrect weight calculations, which would undermine the validity of the evaluation of music education interventions. By rigorously checking the consistency of the comparisons using the Consistency Index (CI) and the Consistency Ratio (CR), the study ensures that the derived weights accurately reflect the relative importance of the factors affecting mental health.

$$ CI = \frac{{\lambda_{\max } - n}}{n - 1} $$

(7)

$$ CR = \frac{CI}{{RI}} $$

(8)

where RI is the random index and ${\lambda }_{max}$ represent the maximum eigenvalue. If CR $\le$ 0.1, the consistency of the judgment matrix is acceptable range in Table 3. This rigorous approach guarantees that the evaluation of music education interventions is both robust and credible, addressing the mental health challenges faced by students.

Table 3 The random consistency index.

Fuzzy topsis

Applying fuzzy TOPSIS to music education strategies offers a promising approach to address university students’ anxiety by providing a structured and systematic framework for multi-criteria decision-making. This method leverages the capabilities of fuzzy TOPSIS to navigate complex decision-making while utilizing the unique potential of music education to tackle a significant issue in higher education. By integrating positive ideal solutions (PIS) and negative ideal solutions (NIS), fuzzy TOPSIS combines analytical rigor with the holistic well-being of students. The steps are described as follows:

Step 1 Table 4 gives the respective scoring criteria and scoring scales of each language variable to determine the scoring value.

Table 4 The important degree definition of fuzzy feature vector weighting method.

Step 2 Construct the fuzzy matrix.

Through a set of k decision makers (D₁, D₂, D₃, …, D_k), including m alternatives (A₁, A₂, A₃, …, A_m) and n criteria (C₁, C₂, C₃, …, C_n), construct the fuzzy matrix of the alternatives:

$$ D = \begin{array}{*{20}c} {A_{1} } \\ {A_{2} } \\ \vdots \\ {A_{m} } \\ \end{array} \mathop {\left( {\begin{array}{*{20}c} {r_{11} } & {r_{12} } & \cdots & {r_{1n} } \\ {r_{21} } & {r_{22} } & \cdots & {r_{2n} } \\ \vdots & \vdots & \vdots & \vdots \\ {r_{m1} } & {r_{m2} } & \cdots & {r_{mn} } \\ \end{array} } \right)}\limits^{{\begin{array}{*{20}c} {C_{1} } & {C_{2} } & \cdots & {C_{n} } \\ \end{array} }} $$

(9)

where r_mn is the rating of alternative A_m with respect to criterion C_n. The number of alternatives is denoted as m, and the number of criteria is denoted as n.

Step 3 Aggregate fuzzy rating for the solutions.

The fuzzy evaluation of the n-th decision maker is $\tilde{X}_{abN} = (l_{abN} ,p_{abN} ,u_{abN} )$, where a = 1,2,…, m, b = 1,2,…, n, and then the fuzzy evaluation of aggregation is given.

$$ \begin{gathered} a_{ij} = \mathop {\min }\limits_{K} \{ l_{abK} \} \hfill \\ b_{ij} = \frac{1}{k}\sum\limits_{n = 1}^{k} {P_{abN} } \hfill \\ c_{ij} = \mathop {\max }\limits_{K} (u_{abK} ) \hfill \\ \end{gathered} $$

(10)

where the lower, modal, and upper bounds of the fuzzy number are denoted as l_abK, P_abN, and u_abK, respectively. The number of decision makers is denoted as k.

Step 4 The normalized fuzzy decision matrix $\tilde{B}$ is defined as follow:

$$ \tilde{B} = [p_{ij} ]_{m \times n} $$

(11)

where p_ij represents the normalized fuzzy value for the ith alternative with respect to the jth criterion, i = 1, 2,…,m and j = 1,2,…n

$$ \tilde{p} = (\frac{{a_{ij} }}{{c_{j}^{*} }},\frac{{b_{ij} }}{{c_{j}^{*} }},\frac{{c_{ij} }}{{c_{j}^{*} }}){\text{ and }}c_{j}^{*} = \max c_{ij} $$

(12)

$$ \tilde{p} = (\frac{{a_{j}^{ - } }}{{c_{ij} }},\frac{{a_{j}^{ - } }}{{b_{ij} }},\frac{{a_{j}^{ - } }}{{a_{ij} }}){\text{ and }}a_{j}^{ - } = \min c_{ij} $$

(13)

where the lower bound a_ij, modal value b_ij, and upper bound c_ij define the fuzzy number for the ith alternative with respect to the jth criterion. The maximum upper bound ${c}_{j}^{*}$ for the jth criterion is calculated as max(c_ij), and the minimum lower bound ${a}_{j}^{-}$ for the jth criterion is calculated as min(c_ij).

Step 5 The weighting fuzzy normalization decision matrix is as follows:

$$ \tilde{V} = [\tilde{v}_{ij} ]_{m \times n} \, i = 1,2,...,m{\text{ and }}j = 1,2,...,n,where \, \tilde{V} = \tilde{p}_{ij} \times w_{j} $$

(14)

where $\widetilde{V}$ is the weighting fuzzy normalized decision matrix, and w_j​ is the weight of criteria j.

Step 6 Determine the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) as following:

$$ A^{ + } = \{ v_{1}^{ + } ,...,v_{n}^{ + } \} ,where \, v_{j}^{ + } = \{ \max (v_{ij} ) \, if \, j \in J;\min (v_{ij} ) \, if \, j \in J{\prime} \} ,j = 1,2,...,n $$

(15)

$$ A^{ - } = \{ v_{1}^{ - } ,...,v_{n}^{ - } \} ,where \, v_{j}^{ - } = \{ \min (v_{ij} ) \, if \, j \in J;\max (v_{ij} ) \, if \, j \in J{\prime} \} ,j = 1,2,...,n $$

(16)

where the FPIS is represented by ${A}^{+}$ and the FNIS by ${A}^{-}$. ${v}_{ij}$ denotes the weighted fuzzy normalized rating for alternative i with respect to criterion j. The maximum value of ${v}_{ij}$ across all alternatives is denoted as max(${v}_{ij}$), and the minimum value as min(${v}_{ij}$).

Step 7 Calculate the distance between the fuzzy positive ideal solution (FPIS) and the fuzzy positive ideal solution (FNIS).

$$ d_{i}^{ + } = \{ \sum\limits_{j = 1}^{n} {(v_{ij} - v_{ij}^{ + } )^{2} } \}^{\frac{1}{2}} ,i = 1,...,m $$

(17)

$$ d_{i}^{ - } = \{ \sum\limits_{j = 1}^{n} {(v_{ij} - v_{ij}^{ - } )^{2} } \}^{\frac{1}{2}} ,i = 1,...,m $$

(18)

where, ${D}_{i}^{+}$ represents the distance of alternative i from the FPIS, and ${D}_{i}^{-}$ represents the distance of alternative i from the FNIS. ${v}_{ij}^{+}$ is the FPIS value for criterion jjj, and ${v}_{ij}^{-}$ is the FNIS value for criterion j.

Step 8 The Closeness coefficient (CC_i) of each alternative is calculate:

$$ CC_{i} = \frac{{d_{i}^{ - } }}{{d_{i}^{ - } + d_{i}^{ + } }},i = 1,...,m. \, C_{i} \in (0,1) $$

(19)

Where, CC_i denotes the closeness coefficient of alternative i, ${D}_{i}^{+}$ is the distance of alternative i from the FPIS, and ${D}_{i}^{-}$ is the distance of alternative i from the FNIS.

Step 9 The alternatives are ranked. Alternatives according to their ideal solution for CC_i value generally in descending order.

Figure 5 presents the flowchart of this method, which consists of two primary steps. The first step involves determining the weights of key factors based on expert opinions. In the second step, the computed weight values are used as input data for fuzzy TOPSIS.

Fig. 5

The fow chart of research methodology.

Result and discussion

Analysis of FAHP results

Following the FAHP procedure, six comparison matrices of hierarchical structure were aggregated and constructed. A total of 38 individuals, comprising music experts, school administrators, and psychologists, were strategically organized into six distinct discussion groups. Their aim was to construct a fuzzy judgment matrix that encapsulated the relative importance and interrelationships of the criteria and sub-criteria essential to our research objectives. Table 5 shows the comparison matrix of the six main factors while Table 6–11 shows the other six comparison matrices of the sub-factors subordinated to each category.

Table 5 Comparison matrix of the main factors.

Table 6 Comparison matrix of “Academic pressures and rigidity”.

Table 7 Comparison matrix of “Parental expectation factor”.

Table 8 Comparison matrix of “Cultural factor”.

Table 9 Comparison matrix of “Social relationships”.

Table 10 Comparison matrix of “Financial stress”.

Table 11 Comparison matrix of “Mental health stigma”.

As is shown in Fig. 6, “Mental health stigma,” with the highest weight of 0.179, indicates a significant concern among students. The stigma associated with mental health issues can lead to reluctance in seeking help, exacerbating feelings of isolation and anxiety. This highlights the need for more open conversations and support systems around mental health in the educational environment. Close behind are “Academic Pressures and Rigidity” and “Cultural factor,” each with a weight of 0.175. These two factors suggest that the educational system’s demands and the broader cultural expectations play equally significant roles in student anxiety. The rigidity of academic systems and the cultural norms that dictate certain behaviors and lifestyles can create a highly pressurized environment for university students. On the other hand, cultural factors include social norms and expectations, resulting in any deviation from the norm or expectation being a source of anxiety and worry. “Financial stress” follows closely with a weight of 0.168, highlighting the impact of economic pressures on student well-being. The stress associated with managing finances, coupled with the fear of future financial instability, can significantly contribute to a student’s anxiety levels, affecting their academic performance and overall mental health. “Parental expectation factor,” with a weight of 0.167, reflects the strong influence of family expectations on students. In a culture where parental aspirations often dictate educational and career choices, the pressure to meet these expectations can be a major source of anxiety and stress for students. “Social relationships,” weighted at 0.136, although lower than the other factors, still plays a crucial role. This weight suggests that the quality and nature of social interactions and relationships are important determinants of student mental health. Issues like peer pressure, social isolation, or challenging interpersonal dynamics can significantly impact a student’s emotional well-being. Each factor contributes to creating an environment where students face multiple sources of stress and anxiety.

Fig. 6

Comparison chart of the weights of the main factors.

Next, we analyze the global weights, as shown in Table 12 and Fig. 7. At the top of the ranking is “Competitive grading systems” with a weight of 0.090. Following closely are “Conformity” and “Constant worry,” with weights of 0.085 and 0.083 respectively. The competitive grading systems drive a relentless pursuit of academic excellence, often at the expense of mental health. “Conformity” highlights the societal expectation for students to align their behaviors and achievements with established norms. This pressure can stifle individuality and lead to significant psychological distress, as students may feel compelled to suppress their unique traits and aspirations in favor of group acceptance. “Constant worry” captures the pervasive anxiety that students experience, not only regarding their academic performance but also their social standing and future prospects. This environment fosters a culture of perfectionism and fear of failure, where students are constantly striving to meet exceptionally high standards. “Identity and self-worth” and “saving face” hold considerable importance, with weights of 0.065 and 0.051, respectively. These factors show that students’ self-esteem is closely tied to societal perceptions and their ability to maintain a positive social image. Other notable factors include “Cultural norms” (0.053), “Stigma and shame” (0.049), and “Societal expectations” (0.048). These weights suggest that this cultural environment tends to force students to conform to social standards, which increases anxiety levels. Moreover, factors such as “Filial piety culture” (0.041), “Fear of disappointing parents” (0.046), and “Fear of discrimination” (0.039) are somewhat lower in the ranking but still noteworthy. These factors shows that students are not only anxious about academic performance but also about how deviations from expected behaviors might result in social exclusion or discrimination.

Table 12 The local weights of the factors and the global weights of the sub-factors.

Fig. 7

Radar diagrams of global weights of the sub-factors.

Analysis of fuzzy TOPSIS results

The study aimed to rank six critical solutions for addressing student psychological issues, using fuzzy TOPSIS, based on the judgments of 38 focus group discussion members. The members of the focus group provided their judgments using a linguistic scale, as detailed in Table 4. The comprehensive fuzzy evaluation matrix containing input from all 38 focus group discussion members is shown in Table 13. This aggregation follows the rule outlined in Eq. (10). The weighted fuzzy evaluation matrix, constructed according to Eqs. (11) to (13), can be found in Table 13.

Table 13 Aggregated fuzzy TOPSIS evaluation matrix.

All the solutions proposed for alleviating psychological issues in university students are considered beneficial. The distances from the Fuzzy Positive Ideal Solution (FPIS) and the Fuzzy Negative Ideal Solution (FNIS) were calculated using Eqs. (14–16), respectively. The final step involved calculating the proximity coefficients, computed using Eq. (17). The comprehensive results, including the rankings of the critical solutions, are demonstrate in Table 14. The solutions are sorted from the highest to the lowest CC_i value, as the higher the CC_i value, the closer the solution is to the ideal solution in fuzzy TOPSIS, as shown in Table 15. Music Appreciation and Music-Based Self-Care (S1) emerges as the most preferred solution with the highest CC_i value. This suggests that activities involving music appreciation and self-care through music are seen as the most effective in mitigating psychological issues among students. These activities could include listening to music, attending music events, and using music as a form of relaxation and stress relief. Music Composition and Group Music-Making (S3) and Music Instruction and Expressive Arts (S2) also score relatively high. These solutions focus on active participation in music, either through composing and playing music in a group or through formal instruction and expressive arts. These activities can provide a creative outlet for emotions, foster a sense of accomplishment, and build a supportive community.

Table 14 Weighted fuzzy evaluation matrix.

Table 15 Aggregated fuzzy TOPSIS evaluation matrix.

Music Meditation (S5) and Music Engagement (S6) are in the middle range of the spectrum. Music meditation involves using music as a meditative tool to calm the mind and reduce stress, while music engagement encompasses a broader involvement in music-related activities. Both are considered effective but to a lesser extent compared to the top-ranked solutions. Interdisciplinary Collaboration (S4), with the lowest CC_i value, is seen as the least effective of the listed solutions. While still valuable, this approach, which may involve collaborations between different academic disciplines to address student mental health, is perceived as less directly impactful compared to the more music-centered interventions.

In summary, the analysis indicates a strong preference for solutions that directly involve students in music-related activities, whether through appreciation, creation, or meditation. These activities are likely seen as more engaging and directly beneficial for mental health compared to more indirect or collaborative approaches. This insight can guide universities and educational institutions in designing and implementing programs to support the mental well-being of their students.

The advantages of FAHP and fuzzy TOPSIS

In this study, the FAHP and fuzzy TOPSIS methods are compared in detail with other fuzzy methods such as Fuzzy Delphi method (FDM), Fuzzy Inference System (FIS) and Fuzzy DEMATEL to show the relative strengths and advantages of FAHP and fuzzy TOPSIS, as shown in Table 16.

Table 16 The advantages of FAHP and fuzzy TOPSIS.

As shown in Table 16, the FAHP method results in relatively balanced weights across factors, with slightly higher weights for “Mental Health Stigma” (0.179) and “Cultural Factors” (0.175). This balanced distribution indicates a comprehensive consideration of all factors impacting mental health. In the Fuzzy Delphi Method (FDM), “Mental Health Stigma” receives the highest weight (0.215), indicating a strong consensus among experts on its critical impact. Other factors have relatively lower and more evenly distributed weights. However, this approach might overlook some nuanced interdependencies between factors, potentially missing out on the subtle influences that different factors exert on each other. The Fuzzy Inference System (FIS) produces weights similar to FAHP but with a slightly different distribution. It gives a bit more weight to “Financial Stress” (0.175) and “Parental Expectations” (0.165). FIS provides a rule-based evaluation that reflects practical considerations, emphasizing the importance of economic and familial pressures on mental health. This method is effective for practical decision-making but may lack the comprehensive factor evaluation found in FAHP. Fuzzy DEMATEL assigns the highest weight to “Mental Health Stigma” (0.230), followed by “Cultural Factors” (0.180). This method highlights strong causal relationships among the factors. This helps in identifying key points for interventions, making it useful for strategic planning and targeted action. However, it may not provide a straightforward ranking of interventions, which can complicate decision-making.

“Music Appreciation and Self-Care” and “Music Meditation” consistently rank as the top interventions, demonstrating their perceived effectiveness in improving mental health. In contrast, “Music Instruction and Expressive Arts” generally ranks in the middle. This suggests that although music instruction and expressive arts are beneficial, they may not be perceived as the most critical interventions compared to others. Meanwhile, “Music Engagement” and “Music Composition and Group Music-Making” often rank lower. The lower rankings imply that these interventions may not consistently produce the desired mental health benefits or may be more context-dependent. The scores across methods for the same intervention vary slightly, reflecting the different weighting and evaluation processes inherent in each method. It highlights the need for a tailored approach when evaluating and implementing interventions to ensure that the chosen methods align with the specific goals and conditions of the mental health programs. The comparative analysis demonstrates the strengths of FAHP and fuzzy TOPSIS in providing detailed, balanced weighting and robust, consistent ranking of interventions. While Fuzzy Delphi Method and Fuzzy DEMATEL offer valuable insights into expert consensus and causal relationships, respectively, they do not match the granularity and reliability of FAHP and fuzzy TOPSIS in this context. Fuzzy Inference System provides a practical, rule-based approach but lacks the flexibility to handle multiple criteria as effectively as FAHP. In conclusion, FAHP and fuzzy TOPSIS stand out for their comprehensive evaluation capabilities, making them highly suitable for assessing and prioritizing mental health interventions.

Conclusion

This research, aimed at addressing psychological anxiety and depression among Chinese university students, has utilized the FAHP and fuzzy TOPSIS methodologies to evaluate and rank various solutions.

Our findings reveal that “Mental health stigma,” “Academic Pressures and Rigidity,” and “Cultural factor” are among the most weighted factors, indicating their significant influence on student mental health. The highest weight of “mental health stigma” (0.179) emphasizes the need for more intelligent information systems to solve mental health problems in educational environments. Similarly, “Academic Pressures and Rigidity” and “Cultural factor,” both with a weight of 0.175, highlight the intense stress induced by educational demands and cultural expectations. The study also noted that “financial pressures” and “parental expectation factors” are also the main causes of student anxiety, reflecting the complexity of the issue. Furthermore, the role of “Social relationships” cannot be overlooked, as it plays a crucial part in shaping students’ emotional well-being.

The fuzzy TOPSIS analysis, incorporating judgments from 38 focus group discussion members, ranked six critical solutions to these issues. The comprehensive results show that “Music Appreciation and Music-Based Self-Care” emerged as the top solution, followed by “Music Composition and Group Music-Making” and “Music Instruction and Expressive Arts.” These findings suggest that activities involving music appreciation and self-care through music resonate strongly with students, providing them with a creative outlet for expression and a means to build supportive communities. Furthermore, music engagement, encompassing both active participation and passive listening, has shown promising links to improved well-being, emotional competence, and reduced symptoms of depression and anxiety. This is particularly evident in the context of internalizing psychopathology, where music has demonstrated potential both as a therapeutic intervention and a means of self-regulation. Moreover, the study emphasizes the importance of considering music engagement as part of a broader spectrum that includes both genetic and environmental influences on mental health.

In conclusion, this study contributes significantly to the understanding of psychological anxiety and depression among Chinese university students. The integration of FAHP and fuzzy TOPSIS methodologies with insights from music and mental health research offers a comprehensive framework for educational institutions to design supportive programs.

Limitations and future research

The limitations of this study impacts the generalizability of the findings in several ways. First, a small sample size may not accurately represent the broader population of university students. The specific views and experiences of the 38 participants might not capture the diversity of the student body across different regions, institutions, and cultural backgrounds. Second, with a limited number of participants, the study may lack the statistical power needed to detect smaller effects or variations, resulting in less reliable conclusions, particularly when dealing with complex phenomena such as psychological well-being and the effectiveness of music-based interventions. Third, small samples are more susceptible to biases introduced by individual differences. Finally, the ability to generalize the findings to other settings or populations is constrained. For example, the particular university environment and cultural context, might not be applicable elsewhere, limiting the study’s external validity. In addition, this study focuses on Chinese university students, which introduces certain limitations to the generalizability of the findings. Chinese higher education is characterized by intense academic demands, competitive grading, and significant societal expectations. These factors create a unique set of pressures that might not be as pronounced in other cultural or educational contexts. As a result, the specific psychological challenges faced by Chinese university students, such as high parental expectations, cultural norms around saving face, and a strong emphasis on academic achievement, may not be directly applicable to students in different countries or educational systems.

Future research should aim to expand the scope to include more diverse student populations. By engaging a broader range of participants from various cultural backgrounds and educational settings, researchers can develop a more comprehensive understanding of the factors affecting student mental health globally. This could help identify universal elements of effective interventions, as well as those that need to be tailored to specific cultural or demographic contexts. Including participants from different academic disciplines, socioeconomic backgrounds, and cultural contexts would provide a more holistic view of the factors influencing mental health and the effectiveness of music-based interventions. Furthermore, investigating the long-term effects of these interventions on mental health is also crucial. Longitudinal studies could track changes over time and provide insights into the sustainability of the interventions’ effects. This approach could reveal how ongoing participation in music education impacts mental health and overall well-being in the long term. Additionally, exploring other factors that might influence student well-being, such as social support systems, extracurricular activities, and individual differences in coping mechanisms, would provide a more comprehensive understanding of the effectiveness of these interventions.