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Article

Social Support and Negative Emotions in the Process of Resilience: A Longitudinal Study of College Students

by
Yuqi Zhang
1 and
Hongshuo Chen
2,*
1
Hebei Key Laboratory of Mental Health and Brain Science, School of Psychology and Mental Health, North China University of Science and Technology, Tangshan 063210, China
2
Student Affairs Office, North China University of Science and Technology, Tangshan 063210, China
*
Author to whom correspondence should be addressed.
Societies 2025, 15(9), 238; https://doi.org/10.3390/soc15090238
Submission received: 21 July 2025 / Revised: 10 August 2025 / Accepted: 26 August 2025 / Published: 28 August 2025
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Abstract

Through three-wave longitudinal research, a survey was conducted with 3200 college students from a university in China, Hebei Province, with an interval of approximately one year between each survey. In the third wave of surveys, 1495 valid samples were obtained. The questionnaires included the Resilience Scale (RS-11), the Social Support Questionnaire (F-SozU K-14), and the Depression Anxiety Stress Scale (DASS-21). There are significant differences in social support and negative emotions across gender variables. There are significant differences in resilience, social support, and negative emotions across travel frequency in the past year. There are significant differences in negative emotions across vegetarianism. Resilience and social support measured in three waves were significantly negatively correlated with negative emotions, while resilience and social support were significantly positively correlated. The results of the random intercept cross-lagged panel model analysis indicate that W1 social support can significantly negatively predict W2 negative emotions, and W2 negative emotions can significantly negatively predict W3 resilience and social support. Ineffective or insufficient social support can cause negative emotions to negatively impact the resilience process while further reducing the individual’s perception of social support.

1. Introduction

Anxiety and depression are the most incapacitating mental disorders [1]. According to the 2022 World Mental Health Report, 13% of the world’s population had a mental disorder in 2019, of which 31.0% were anxiety disorders, 28.9% were depressive disorders, and 11.1% were congenital developmental disorders. The prevalence rates of depression and anxiety are 6.8% and 9.3%, respectively, in people aged 15 to 24 years [2]. According to China’s 2022 National Mental Health Report, the depression screening results put the depression rate for young people aged 18–24 years is as high as 24.1% [3], which is significantly greater than that of other age groups. Depression, anxiety, and stress are typical negative emotions, and in China, 59.2% of students reported academic stress, and excessive or persistent stress may have a negative impact on their health [4]. Universities should pay more attention to the mental health of students. After coping with stress, trauma, and challenges, scholars such as Garmezy and Werner proposed the concept of resilience in the 1970s, and for more than half a century, many researchers have developed different concepts and theoretical frameworks for resilience.
Recently, Masten defined resilience as the capacity of a dynamic system to adapt successfully through multisystem processes to challenges that threaten the function, survival, or development of the system [5]. It is clear that she has shifted our focus from “The Capability in Process” to “The Process of Capability.” The philosopher of science Karl Popper proposed the theory of three worlds in chronological order of generation from a macro perspective, and we can simply understand the three worlds as nature, human beings, and human society [6]. From a more objective macro perspective, human beings first have the ability of resilience, and with the gradual development of human society, the process of psychological resilience is becoming increasingly complex. The intrinsic resilience of all species in nature, including humans, as a general concept, is the key component (subset) of complex socioecological systems [7]. However, the current resilience process is a special concept of positive or negative adaptation outcomes of resilient individuals in internal and external environmental systems with the interaction and development of the time dimension, and the development trajectory varies from person to person and is affected by multiple systems and a variety of factors, such as genes, stressors, and lifestyles [8].

1.1. Related Works

Depression, anxiety, and stress are widely studied negative emotions in the field of mental health, and these three emotions not only have their own unique characteristics but in many cases also intertwine with each other to form more complex psychological states. Depression is a persistently low mood state that is often accompanied by symptoms such as low mood, loss of interest, fatigue, and reduced self-worth. It affects not only emotions but also the physiological and cognitive functions of individuals and is an important representative of negative emotions [9]. Anxiety is an individual’s anticipatory response to a potential threat, often manifested by excessive worry, nervousness, and physiological reactions (such as faster heartbeat), a state that may affect an individual’s functioning and quality of life and lead to the development of anxiety disorders in specific situations. Chronic stress can affect the serotonin and dopamine systems in the brain and result in low mood and loss of interest, which can cause or exacerbate depressive symptoms [10]. In addition, the study by Daviu et al. suggests that stress is an important trigger of anxiety symptoms and that long-term stress may trigger or exacerbate anxiety disorders [11].
From the perspective of the philosophy of science, human psychological resilience can be defined as the capacity of organisms in nature to utilize internal and external resources to maintain their “existence” in a specific environment. This is also consistent with the view of “existence precedes essence” in existentialist philosophy and humanistic psychology [12]. This process involves not only emotional and psychological aspects of regulation but also social support and environmental factors [13]. The stress faced by college students can come from a variety of sources, including academic burdens, uncertainty about future employment, personal relationships, and financial pressures. Students with greater resilience are more likely to deal with these stressors through effective coping strategies and avoid long-term symptoms of anxiety and depression [14]. Previous cross-sectional studies have shown that social support can partially or even fully mediate between the resilience of health care practitioners and psychological burden or stress [15]. However, there has been little research on the role of social support in the resilience process with regard to negative emotions and little research on the longitudinal relationship between the three.

1.2. Theories and Hypotheses

Numerous studies have shown that resilience is determined by the circumstances in which children grow [16]. However, relatively few longitudinal follow-up studies have been conducted on the three variables of resilience, social support, and negative emotions. The concept of Qualia in the philosophy of the mind argues that the consciousness of the individual not only has physical properties determined by the material basis of the first world proposed by Popper but also has subjective experience [17] and active purposefulness [18] and is able to think rationally and make judgments about behavior. Because humans purposefully created human society first and were influenced by society afterwards, this is consistent with causality. The theoretical framework underlying this study is Bronfenbrenner’s theory of the ecology of human development, which emphasizes the dynamic interaction between the development of individuals and their environmental systems [19]. As shown in Figure 1, at the individual level, resilient individuals in an ecosystem will produce different developmental outcomes through their unique lifestyles (e.g., different leisure and dietary styles will result in different levels of resilience, social support resources, and negative emotions). At the resource level, as proposed by resource conservation theory, the internal and external resources that individuals possess to cope with stress include objects (e.g., cars, houses), conditions (e.g., social statuses in different environmental systems, such as students, employees, and citizens), personal characteristics (e.g., personality traits and skills), and energy (e.g., time, money, knowledge, and information). Individuals strive to preserve, protect, and build resources, and what threatens them is the potential or actual loss of these precious resources. The resources that individuals possess lead to outcomes in terms of stress and well-being [20]. This theory integrates internal and external resources. This is similar to the buffering hypothesis, which emphasizes the importance of psychological or material resources for mental health [21]. Finally, when individuals lack sufficient support resources, they seek the resources they need (such as previously perceived available social support) through their own subjective experiences generated by Qualia to cope with developmental challenges. They are not simply passively influenced by the environment and resources. Of course, when these support resources are available, they can cope with negative emotions, but when social support resources are ineffective, it causes distress [22]. This is also consistent with Wagnild and Young’s definition of resilience that states that resilience can be understood as the ability to successfully utilize internal and external resources to cope with developmental tasks [23]. This study assumes that individual development is the result of interactions with multiple layers of the environment rather than being passively influenced by the environment. Individuals with resilience are able to actively seek and utilize resources from various systems to cope with adversity through adaptive strategies in the face of adversity or stress, thereby changing their developmental outcomes (e.g., emotional states).
In summary, based on the results of three waves of surveys conducted over two years among college students (emerging adults), this study proposes four hypotheses. H1: Resilience, social support, and negative emotions differ significantly across gender, travel frequency in the past year, and dietary style. H2: Resilience, social support, and negative emotions are significantly correlated. H3: Over time, social support can significantly influence negative emotions, while negative emotions can also significantly influence resilience and social support. H4: The chain of resilience process in the ecosystem proposed in this study, within an ecosystem context, encompasses the specific effects and influence pathways of social support resources on developmental outcomes, as shown in Figure 1. The above hypotheses are investigated in order to provide empirical support for psychological health intervention for college students and a theoretical basis for educators and mental health practitioners.

2. Materials and Methods

2.1. Participants and Procedures

In November 2020, 3200 freshman college students were selected through cluster sampling at a university in Hebei Province, China, to participated in this mental health survey project. The survey was conducted in the classroom through online methods, the same survey was conducted every one year or so, and a total of three waves were completed. All participants were college students, had no mental illness, and signed an informed consent form. A rigorous screening protocol was employed in this study to ensure the quality and completeness of the survey data collected. Specifically, the following criteria were employed: (1) Completeness: each questionnaire was carefully checked to verify that all the questions were adequately answered and that nothing was missed. (2) Standardized responses: questionnaires that exhibited significant repetition or randomness in the responses were considered invalid and excluded from the final analysis. (3) Consistency: cross-validation of multiple responses from the same participant was conducted to identify and remove all samples with obvious contradictions or inconsistencies. Through this systematic screening process, 3051 valid samples were obtained in the first-wave survey, with an effective response rate of 95.31%. A total of 2266 valid samples were obtained from the second-wave follow-up survey of 3051 people, with an effective response rate of 74.27%. The third-wave follow-up survey of 2266 people was carried out, and 1495 valid samples were finally obtained, with an effective response rate of 65.98%. The response rate of each survey is higher than the general expectation standard of 60% [24]. According to Chinese mainland law and ethics, this study only categorizes gender as male or female. In the data measured in the third wave, 816 of the participants were males, accounting for 54.60%. Furthermore, through difference tests, there were no significant differences between the second and third samples that were lost to follow-up and the follow-up samples in terms of resilience, social support, and negative emotions scores.

2.2. Measures

Sociodemographic questionnaires were used to collect information such as gender, age, diet style (non-vegetarians and those who do not eat meat, fish, eggs, and milk), and the frequency of travel in the past year. In addition, the questionnaires also included the Resilience Scale (RS-11) [25], the Social Support Questionnaire (F-SozU K-14), and the Depression Anxiety Stress Scales (DASS-21).
Psychological resilience is measured using the Chinese version of the RS-11 Resilience Scale [26]. This is a single-dimensional scale with a total of 11 items that adopts a 7-point Likert scoring method (ranging from 1 for “completely disagree” to 7 for “completely agree”). The scoring rule is summed, and there was no reverse scoring. Higher scores indicate higher levels of psychological resilience. The Cronbach’s alpha coefficients for the three waves measured in this study are 0.895, 0.965, and 0.967, respectively.
The measurement of social support resources uses the F-SozU K-14 Social Support Questionnaire. This is a single-dimensional scale with a total of 11 items that adopts a 5-point Likert scoring method (ranging from 1 for “completely inconsistent” to 5 for “completely consistent”). The scoring rule was summed, and there was no reverse scoring. Higher scores indicate more perceived support resources [27]. The Cronbach’s alpha coefficients of the three waves measured in this study were 0.951, 0.979, and 0.980, respectively.
Negative emotions were measured via the Depression Anxiety Stress Scales (DASS-21), which assesses a broad range of psychological distress symptoms. It is composed of three 7-item subscales for depressive, anxiety, and stress symptoms over the past week. Responses can be averaged within each subscale or across all three for a total item score, and it adopts a 4-point Likert scoring method (ranging from 1 for “completely inconsistent” to 4 for “completely consistent”). The scoring rule is summed with no reverse scoring. Greater total scores represent stronger negative emotions [28]. The Cronbach’s alpha coefficients for the three waves measured in this study are 0.958, 0.987, and 0.988, respectively.

2.3. Statistical Analysis

Common method bias was tested using Amos 24.0. Spearman’s rank correlation was used to analyze the correlation between resilience, social support, and negative emotions [29]. Independent samples t-tests were performed to examine differences in resilience, social support, and negative emotions across demographic variables. Random intercept cross-lagged panel analysis was carried out using Mplus 8.3. The absolute value of the skewness of the W1 negative emotions score was less than 2, and the absolute value of the kurtosis was less than 5. The absolute values of the skewness and kurtosis of resilience, social support, and negative emotion scores measured by the other waves were all less than 2. However, under any distribution, standard deviation is an effective measure of variability, so this study’s score report can use the mean and standard deviation [30]; when the sample size is greater than 1000, the independent samples t-test can still be used for difference analysis [31].

3. Results

3.1. Common Method Bias Assessment

The data in this study are self-reported by college students and may have common method bias. Therefore, the common method bias (CMB) examination was performed by the Unmeasured Latent Method Construct (ULMC) method through comparison of the main fit indices of confirmatory factor analysis and confirmatory factor analysis including method factor [32]. In the first wave, ΔIFI = 0.022, ΔTLI = 0.017, and ΔCFI = 0.022, which were all lower than 0.10, and ΔRMSEA = 0.004 and ΔSRMR = 0.011, which were both lower than 0.05. In the second wave, ΔIFI = 0.033, ΔTLI = 0.031, and ΔCFI = 0.033, which were all lower than 0.10, and ΔRMSEA = 0.012 and ΔSRMR = 0.013, which were both lower than 0.05. In the third wave, ΔIFI = 0.036, ΔTLI = 0.034, and ΔCFI = 0.036, which were all less than 0.10, and ΔRMSEA = 0.014 and ΔSRMR = 0.008, which were all lower than 0.05. From this, we can conclude that there was no serious common method bias in this study [33].

3.2. Demographic Characteristics

Through descriptive statistics, the resilience score measured in the first wave was 57.97 ± 9.831, the social support score was 57.38 ± 9.804, and the negative emotions score was 6.99 ± 9.026. The resilience score measured in the second wave was 57.64 ± 12.829, the social support score was 54.57 ± 11.581, and the negative emotions score was 15.50 ± 16.110. The resilience score measured in the third wave was 56.09 ± 13.050, the social support score was 52.69 ± 11.884, and the negative emotions score was 36.83 ± 16.480. As shown in Table 1, we used independent samples t-tests to explore whether there were differences in psychological resilience, social support, and negative emotions among college students related to demographic variables such as gender, travel frequency in the past year, and whether they were vegetarians. The results revealed that there were no significant gender differences in resilience scores at W1 t (1493) = 1.41, W2 t (1479) = 0.58, and W3 t (1492) = 0.38, but the social support scores of females at W1 t (1493), W2 t (1490) = −2.50, and W3 t (1487) = −2.75 were significantly higher than those of males. At the same time, the negative emotions of females at W1 t (1492) = 3.38, W2 t (1475) = 9.18, and W3 t (1492) = 8.10 were significantly lower than those of males. Based on the score report at W1 time, students who traveled once or more had significantly higher resilience t (1493) = −7.26 and social support t (1493) = −6.35 and significantly lower negative emotions t (1493) = 2.42 compared to students who did not travel in the past year. At the W2 time, students who traveled once or more had significantly higher resilience t (1493) = −3.18 and social support t (1493) = −4.04 and significantly lower negative emotions t (1493) = 3.42 compared to students who did not travel in the past year. Similarly, at the W3 time, students who traveled once or more had significantly higher resilience t (1493) = −4.23 and social support t (1493) = −5.79 and significantly lower negative emotions t (1169) = 4.52 compared to students who never traveled in the past year. For the current vegetarian variable, the negative emotions of non-vegetarians were significantly lower than those of vegetarians at W1 t (1057) = −2.43, W2 t (909) = −6.74 and W3 t (742) = −8.08. Therefore, H1 was partly confirmed.

3.3. Correlation Analysis

As shown in Table 2, at times W1, W2, and W3, resilience and social support were significantly positively correlated, and both resilience and social support were significantly negatively correlated with negative emotions (all p < 0.001). Therefore, H2 was confirmed.

3.4. The RI-CLPM for Resilience, Social Support, and Negative Emotions

A RI-CLPM with the three variables was tested (Figure 2). All main fit indices fell within the acceptable range. Chi-square = 73.143, degrees of freedom = 12, p < 0.001, RMSEA = 0.058, 95% confidence interval for RMSEA is 0.046–0.072, CFI = 0.989, and SRMR = 0.029. Table 3 presents the parameter estimates of interest. At the between-person stable trait level, resilience was highly positively correlated with social support (r = 0.858, p < 0.001), while resilience was significantly negatively correlated with negative emotions (r = −0.705), and social support was significantly negatively correlated with negative emotions (r = −0.597) (both p < 0.001). This indicates that there is a stable associative pattern between resilience, social support, and negative emotions over the long term. Gender significantly predicts an individual’s social support and negative emotions. In the long term, females’ social support is significantly higher than males’ (β = 0.111, p < 0.001), and their negative emotions are significantly lower than males’ (β = −0.277, p < 0.001).
At the within-person level, contemporaneous covariances indicate that at the same time point, when an individual reports higher resilience compared to their baseline, they also tend to report higher social support. Over time, the contemporaneous covariances between resilience and negative emotions weaken, with r decreasing from −0.297 to −0.028 (p = 0.317). The contemporaneous covariance between social support and negative emotions also weakens over time from significant to marginally significant, with r decreasing from −0.278 to −0.052 (p = 0.069) [34]. However, the contemporaneous covariance between resilience and social support gradually strengthens over time, with r increasing from 0.527 to 0.771 (p < 0.001). The autoregressive coefficient represents the predictive effect of a person’s reported score at one time point on the next time point. The autoregressive coefficient β for negative emotions ranges from 0.184 to 0.366, which is more stable than the autoregressive coefficients for resilience and social support. This indicates that negative emotions are more stable and persistent within individuals. Cross-lagged analysis showed that social support significantly predicted a decrease in negative emotions from W1 to W2. However, the increase in negative emotions from W2 to W3 significantly predicted a decrease in resilience and social support. Therefore, H3 was partly confirmed, because they did not occur simultaneously but were divided into two stages.
Combining the results of contemporaneous covariances, it can be seen that as negative emotions continue to increase, resilience levels gradually become less correlated with negative emotions. This process increasingly requires social support, but starting from W2, the covariances between social support and negative emotions also gradually decreases, and social support gradually becomes unrelated to negative emotions. According to Optimal Matching Theory (OMT), mismatched support causes individuals to experience greater distress [35]. This indicates that at W1, social support has a buffering effect on negative emotions. However, contrary to expectations, negative emotions did not decrease. (The mean scores increased from 6.99 to 15.50.) Subsequently, the gradual mismatch between social support and negative emotions leads to the failure of the buffering effect, causing negative emotions to further escalate (the mean scores increased from 15.50 to 36.83) and negatively impacting individual resilience and social support.

4. Discussion

4.1. Factors Influencing Resilience, Social Support, and Negative Emotions

There were no significant differences in resilience by gender in terms of demographic differences, as previous researchers have concluded [36]. Females have significantly lower negative emotions than males, which suggests that females’ problem-solving and social support strategies were significantly better than males in the academic scenario. Females tend to use emotional regulation strategies more frequently than men and are more flexible in how they are used [37]. The resilience and social support scores of college students who have not traveled in the past year are significantly lower than those of students who have traveled at least once or more in the past year, and the negative emotions of students who have not traveled are also significantly greater than those of students who travel frequently, which is in line with the socioecological theory that the development of individuals is affected by various layers of environmental systems and is also in line with the “Multilevel Leisure Mechanisms Framework” proposed by Daisy Fancourt et al. This framework argues that leisure activities can support individual transitions, build individual resilience, and have a positive impact on mental health [38]. In terms of diet style, the results of three waves of surveys conducted from the first year of college (emerging adulthood) revealed that the negative emotions of nonvegetarians became significantly lower than those of vegetarians as they aged. This aspect provides some evidence for the recent study of the mental health of vegetarians by Thierry Gagné and Vanessa Kurdi, who also suggested that the development of self-identity in adolescence can be influenced by stressful events in the course of development or changes in diet style after a life change (such as a positive event such as a higher level of education or a traumatic experience such as a move or breakup) [39]. The fact that college students enter a new environment to live and study is also a change in their living environment and living habits because each individual’s cognitive and personality tendencies are different. For college freshmen, whether it is a sublimation of the mind or posttraumatic stress that can cause a shift in diet style, this transition can be a positive event or a traumatic experience.
The correlation results in this study show that resilience, social support, and negative emotions are all significantly correlated in the temporal dimension, which is consistent with the model of conservation of resources. Individuals’ intrinsic psychological resilience is an intrinsic resource in terms of personal characteristics (such as personality traits, skills, or abilities); perceived social support is a social resource in terms of conditions (e.g., social identities in different environments such as friend or student). The acquisition, retention, loss, or potential loss of these intrinsic or extrinsic resources is significantly related to negative emotions such as stress in college students. The significant increase in negative emotions and the sustained decrease in social support over the course of two years may be due to the academic pressures and social challenges faced by students in their second year, resulting in an actual or potential loss of an individual’s social identity resource as a student or classmate [20]. Taken together, whether college students travel, relax, or experience positive or negative experiences, these influencing factors are inseparable from the appropriate support system of the university during the university period. Because of the cultural differences across different ethnic groups and regions, universities should consider not only individual differences, but also cultural diversity and differences when developing mental health efforts or supporting resources and “act according to actual circumstances” [40].

4.2. Relationships Among Resilience, Social Support, and Negative Emotions over Time

The results of the between-person level of variation show that resilience, social support, and negative emotions are significantly correlated with each other among individuals, which is consistent with the hypothesis proposed in this study. In addition, social support and negative emotions were significantly influenced by gender. This suggests that not only do young women perceive more social support, but their social support is more strongly associated with mental health [41]. At the within-person level, social support in the first wave significantly negatively predicted negative emotions in the second wave. This reason is most likely due to the fact that freshmen feel less stress and challenge when they first start school. The gradual increase in academic stress or social challenges from the first to the second year of college is accompanied by a significant rise in negative emotions. As time passes from sophomore to junior year, more pressures and challenges arise, such as English exams, skills qualification exams, and the upcoming pressures of employment and internships in the fourth year of college. Therefore, in the second year, the need to mobilize and utilize more support resources is almost simultaneous with the process of negative emotions, activating resilience. However, the cross-lagged path coefficients provide a contrast: although negative emotions significantly affect the resilience process, the need for support resources is higher, and it is more likely that internal and external resources will influence developmental outcomes. This is in line with the example model of risk and resilience factors theory proposed by Masten [5]. The psychological resilience process is dynamic, and many resilience factors, such as social support, self-regulation, belonging, and positive coping, are activated by stressors. The psychological resilience process involves multiple resilience factors and their interactions with time-varying symptom networks. Since the resilience process is activated around the second wave, individuals are driven to seek additional support resources (internal and external) to cope with upcoming stresses and challenges.

4.3. Social Support Has Two Effects as a Dual Factor

This study found that social support, a traditional “protective factor” throughout the resilience process, is likely to be the “risk factor” that leads to negative emotions. Combined with the cross-lag results of this study, it can be seen that social support may be a risk factor for negative emotions in the process of resilience (negative social support). There are two possible explanations as to whether social support is a risk factor or a protective factor. The first possible explanation, as proposed by the model of risk and resilience factors and the buffering hypothesis [5,42], is that under the premise of sufficient support resources, individuals activate the resilience process in the face of adversity or stressors, which increases the demand for and utilization of support resources and reduces the individual’s perceived social support. Adequate social support at this time is a protective effect in the resilience process, which can maintain or alleviate the negative emotional effects generated by the stressor, which is also consistent with the neural model of social support proposed by Rui Pei et al., which suggests that social support is essential in buffering negative emotions [43]. The second possible scenario is that positive social support can cushion the health effects of traumatic experiences, as proposed by Aldomini et al., while negative social support that does not help is associated with distress [22]. When available support resources are insufficient or ineffective, it increases the vulnerability and distress of the individual, causing negative emotions to rise instead of decreasing in the face of stressors or adversity. Therefore, inadequate social support has a risk effect on the resilience process. It is clear from the cross-lagged effects of this study that social support is more likely to have a risk effect in this study. Under the premise of insufficient support resources, college students’ resilience in the face of stressors or adversity cannot mobilize sufficient or effective support resources, which increases their vulnerability and causes pain, leading to further negative emotions. This finding provides a new perspective for the study of social support, and the support resource can be the “dual-factor” of coping with negative emotions in the process of resilience. It is reasonable to assume that “All factors can exert a protective or risk effect under different conditions in the course of human development.” This is just as Rutter has suggested, that “process” is more important than “factor”; any factor may be a risk factor in one case but can be a protective factor in another, and it is the process rather than the factor that determines the functioning of the individual [44].

4.4. Recommendations for Mental Health Works

Looking at it from a comprehensive point of view, universities should provide students with adequate support resources from the first year of school to graduation and consider gender considerations to reduce their negative emotions such as stress [45]. Colleges should provide students with appropriate social support, including emotional support, for example, by promoting their sense of belonging and security and effectively reducing their anxiety and depression, thereby improving their mental health [46], which is also in line with Maslow’s Hierarchy of Needs [47]. Social interaction support, such as teacher–student interaction, can provide students with a sense of support, which not only helps them engage academically but also deepens their sense of identity with the school and understanding of the value of learning, thereby regulating negative emotions such as stress and anxiety [48]. In addition, schools should cultivate more diverse coping skills from the perspective of mental health education to increase the psychological resilience of college students and their ability to access social support resources [49]. As an umbrella term, social connection can include concepts such as loneliness, social cohesion, social isolation, social participation, and social support and can be divided into three aspects: structure (such as social networks, social integration, and social isolation), function (such as perceived social support and belonging), and quality (such as relationship satisfaction, tension, and social ambivalence) [50]. It can be seen that the provision of high-quality social connections at the university is beneficial to the overall health of the students, as individuals are able to meet their psychological needs and achieve their developmental goals through the support they receive through social connections, as well as allowing them to have a subjective experience of well-being in their lifelong development.

5. Conclusions

This study conducted three waves of surveys over a period of more than two years among college students and found significant differences in resilience, social support, and negative emotions in terms of travel frequency over the past year, as well as a significant impact of vegetarianism on negative emotions. This indicates that healthy leisure activities and dietary style can promote individuals’ resilience process, increase perceived support resources, and promote better developmental outcomes. Throughout the process, during the W1 to W2 stage, support resources have a buffering effect, but during the W2 to W3 stage, the buffering effect is no longer significant and ineffective or mismatched support resources are more likely to further increase negative emotions. Ineffective social support no longer has a significant predictive effect in a high-stress developmental context, and high negative emotions are detrimental to the individual’s resilience process and predict lower perceived social support. The chain of resilience process in the ecosystem has been largely verified. This indicates that in the context of gradually increasing stress, anxiety, and depression, resilience increasingly requires social resources throughout the process. However, resilience itself, as a capacity to maintain an individual’s “existence,” is not a form of knowledge or skill. It cannot directly produce developmental outcomes; individuals are more likely to utilize internal and external resources for development.

6. Limitations

Although this study has made some progress, these results and hypotheses have only been partially verified among Chinese college students. Whether they are representative requires the inclusion of more regions and groups. For example, although the chain of resilience process in the ecosystem has been partially verified, broader mechanisms remain to be further explored. In particular, further research is needed to explore which support resources mitigate or exacerbate negative developmental outcomes. Another important insight from this study is that in future longitudinal studies, researchers should investigate the specific experiences of participants to conduct a more comprehensive explanation of outcomes and predictions in combination with individual subjective experiences. For example, by combining specific sources of chronic stress, it is possible to analyze resilience resource utilization and buffering mechanisms in greater detail.

Author Contributions

Conceptualization, methodology, investigation, software, formal analysis, investigation, writing—original draft preparation, and validation, Y.Z.; conceptualization, validation, writing—review and editing, resources, supervision, project administration, funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Social Science Foundation of Hebei Province in 2020, grant number: HB20JY055.

Institutional Review Board Statement

This study does not include any clinical trials. This survey research was approved by the Medical Ethics Committee of North China University of Science and Technology (Ref. No. 2020198; Approval Date: 17 December 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. All participants signed informed consent forms.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the authors.

Acknowledgments

We are especially grateful to Hongxia Ma, director of the College Student Mental Health Education and Counseling Center at North China University of Technology, for her great support and comprehensive guidance. We also thank the School of Psychology and Mental Health, North China University of Science and Technology.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PRpsychological resilience
SSsocial support
NEnegative emotions

References

  1. O’Leary, K. Global increase in depression and anxiety. Nat. Med. 2021, 398, 1700–1712. [Google Scholar] [CrossRef]
  2. WHO. World Mental Health Report: Transforming Mental Health for All; World Health Organization: Geneva, Switzerland, 2022. [Google Scholar]
  3. Li, X.; Baierna, A.; Tan, C. Building an integrated mental health education system in schools. Chin. J. Sch. Health 2024, 45, 615–619. [Google Scholar] [CrossRef]
  4. Geng, Y.; Gu, J.; Zhu, X.; Yang, M.; Shi, D.; Shang, J.; Zhao, F. Negative emotions and quality of life among adolescents: A moderated mediation model. Int. J. Clin. Health Psychol. 2020, 20, 118–125. [Google Scholar] [CrossRef]
  5. Masten, A.S.; Lucke, C.M.; Nelson, K.M.; Stallworthy, I.C. Resilience in Development and Psychopathology: Multisystem Perspectives. Annu. Rev. Clin. Psychol. 2021, 17, 521–549. [Google Scholar] [CrossRef] [PubMed]
  6. Heller, M. Philosophy in Science; Springer: Berlin/Heidelberg, Germany, 2011; pp. 113–119. [Google Scholar]
  7. Moore, J.W.; Schindler, D.E. Getting ahead of climate change for ecological adaptation and resilience. Science 2022, 376, 1421–1426. [Google Scholar] [CrossRef] [PubMed]
  8. Scheffer, M.; Bolhuis, J.E.; Borsboom, D.; Buchman, T.G.; Gijzel, S.M.W.; Goulson, D.; Kammenga, J.E.; Kemp, B.; van deLeemput, I.A.; Levin, S.; et al. Quantifying resilience of humans and other animals. Proc. Natl. Acad. Sci. USA 2018, 115, 11883–11890. [Google Scholar] [CrossRef]
  9. Gu, D.; Dupre, M.E. Encyclopedia of Gerontology and Population Aging; Springer International Publishing: Berlin/Heidelberg, Germany, 2021. [Google Scholar]
  10. Cowen, P.J. Cortisol, serotonin and depression: All stressed out? Br. J. Psychiatry 2018, 180, 99–100. [Google Scholar] [CrossRef]
  11. Daviu, N.; Bruchas, M.R.; Moghaddam, B.; Sandi, C.; Beyeler, A. Neurobiological links between stress and anxiety. Neurobiol. Stress 2019, 11, 100191. [Google Scholar] [CrossRef]
  12. Hounkpatin, H.O.; Wood, A.M.; Boyce, C.J.; Dunn, G. An Existential-Humanistic View of Personality Change: Co-Occurring Changes with Psychological Well-Being in a 10 Year Cohort Study. Soc. Indic. Res. 2014, 121, 455–470. [Google Scholar] [CrossRef]
  13. Windle, G. What is resilience? A review and concept analysis. Rev. Clin. Gerontol. 2011, 21, 152–169. [Google Scholar] [CrossRef]
  14. Morales-Rodríguez, F.M.; Martínez-Ramón, J.P.; Méndez, I.; Ruiz-Esteban, C. Stress, Coping, and Resilience Before and After COVID-19: A Predictive Model Based on Artificial Intelligence in the University Environment. Front. Psychol. 2021, 12, 647964. [Google Scholar] [CrossRef]
  15. Ong, H.L.; Vaingankar, J.A.; Abdin, E.; Sambasivam, R.; Fauziana, R.; Tan, M.-E.; Chong, S.A.; Goveas, R.R.; Chiam, P.C.; Subramaniam, M. Resilience and burden in caregivers of older adults: Moderating and mediating effects of perceived social support. BMC Psychiatry 2018, 18, 27. [Google Scholar] [CrossRef]
  16. Southwick, S.M.; Sippel, L.; Krystal, J.; Charney, D.; Mayes, L.; Pietrzak, R.H. Why are some individuals more resilient than others: The role of social support. World Psychiatry 2016, 15, 77–79. [Google Scholar] [CrossRef]
  17. Salti, M.; Bergerbest, D. The Idiosyncrasy Principle: A New Look at Qualia. Perspect. Psychol. Sci. 2022, 17, 1794–1799. [Google Scholar] [CrossRef] [PubMed]
  18. Beckermann, A.; McLaughlin, B.P.; Walter, S. The Oxford Handbook of Philosophy of Mind; Oxford University Press: Oxford, UK, 2009. [Google Scholar]
  19. Bronfenbrenner, U. The Ecology of Human Development: Experiments by Nature and Design; Harvard University Press: Cambridge, MA, USA, 1981. [Google Scholar]
  20. Hobfoll, S.E. Conservation of resources. A new attempt at conceptualizing stress. Am. Psychol. 1989, 44, 513–524. [Google Scholar] [CrossRef] [PubMed]
  21. Cohen, S. Social Relationships and Health. Am. Psychol. 2004, 59, 676–684. [Google Scholar] [CrossRef] [PubMed]
  22. Aldomini, M.; Lee, J.W.; Nelson, A.; Hwang, R.S.; Alharbi, K.K.; Sinky, T.H.; Quronfulah, B.S.; Khan, W.A.; Elamin, M.O.; Nour, M.O. The Moderating Role of Social Support on the Impact of Adverse Childhood Experiences on Life Satisfaction and Mental Health in Adulthood. Clin. Epidemiol. Glob. Health 2025, 32, 101933. [Google Scholar] [CrossRef]
  23. Wagnild, G.M.; Young, H.M. Development and psychometric evaluation of the Resilience Scale. J. Nurs. Meas. 1993, 1, 165–178. [Google Scholar]
  24. Fincham, J.E. Response Rates and Responsiveness for Surveys, Standards, and the Journal. Am. J. Pharm. Educ. 2008, 72, 43. [Google Scholar] [CrossRef]
  25. Schumacher, J.; Leppert, K.; Gunzelmann, T.; Strauß, B.; Brähler, E. Die Resilienzskala-Ein Fragebogen zur Erfassung der psychischen Widerstandsfähigkeit als Personmerkmal. Z. Klin. Psychol. Psychiatr. Psychother. 2005, 53, 16–39. [Google Scholar]
  26. Gao, Z.H.; Yang, S.Q.; Margraf, J.; Zhang, X.C. Reliability and Validity Test for Wagnild and Young’s Resilience Scale (RS–11) in Chinese. China J. Health Psychol. 2013, 21, 1324–1326. [Google Scholar] [CrossRef]
  27. Fydrich, T.; Sommer, G.; Tydecks, S.; Brähler, E. Fragebogen zur sozialen Unterstützung (F-SozU): Normierung der Kurzform (K-14). (Social support questionnaire (F-SozU): Standardization of short form (K-14).). Z. Med. Psychol. 2009, 18, 43–48. [Google Scholar] [CrossRef]
  28. Lovibond, P.F.; Lovibond, S.H. The Structure of Negative Emotional States: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav. Res. Ther. 1995, 33, 335–343. [Google Scholar] [CrossRef]
  29. Bishara, A.J.; Hittner, J.B. Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches. Psychol. Methods 2012, 17, 399–417. [Google Scholar] [CrossRef]
  30. Altman, D.G.; Bland, J.M. Standard deviations and standard errors. Br. Med. J. 2005, 331, 903. [Google Scholar] [CrossRef] [PubMed]
  31. Fagerland, M.W. t-tests, non-parametric tests, and large studies—A paradox of statistical practice? BMC Med. Res. Methodol. 2012, 12, 78. [Google Scholar] [CrossRef] [PubMed]
  32. Richardson, H.A.; Simmering, M.J.; Sturman, M.C. A Tale of Three Perspectives: Examining Post Hoc Statistical Techniques for Detection and Correction of Common Method Variance. Organ. Res. Methods 2009, 12, 762–800. [Google Scholar] [CrossRef]
  33. Bozionelos, N.; Simmering, M.J. Methodological threat or myth? Evaluating the current state of evidence on common method variance in human resource management research. Hum. Resour. Manag. J. 2021, 32, 194–215. [Google Scholar] [CrossRef]
  34. Olsson-Collentine, A.; van Assen, M.A.L.M.; Hartgerink, C.H.J. The Prevalence of Marginally Significant Results in Psychology Over Time. Psychol. Sci. 2019, 30, 576–586. [Google Scholar] [CrossRef]
  35. Merluzzi, T.V.; Philip, E.J.; Yang, M.; Heitzmann, C.A. Matching of received social support with need for support in adjusting to cancer and cancer survivorship. Psychooncology 2016, 25, 684–690. [Google Scholar] [CrossRef]
  36. Amoadu, M.; Agormedah, E.K.; Obeng, P.; Srem-Sai, M.; Hagan, J.E., Jr.; Schack, T. Gender Differences in Academic Resilience and Well-Being Among Senior High School Students in Ghana: A Cross-Sectional Analysis. Children 2024, 11, 512. [Google Scholar] [CrossRef] [PubMed]
  37. Goubet, K.E.; Chrysikou, E.G. Emotion Regulation Flexibility: Gender Differences in Context Sensitivity and Repertoire. Front. Psychol. 2019, 10, 935. [Google Scholar] [CrossRef] [PubMed]
  38. Fancourt, D.; Aughterson, H.; Finn, S.; Walker, E.; Steptoe, A. How leisure activities affect health: A narrative review and multi-level theoretical framework of mechanisms of action. Lancet Psychiatry 2021, 8, 329–339. [Google Scholar] [CrossRef] [PubMed]
  39. Gagné, T.; Kurdi, V. Vegetarianism and mental health: Evidence from the 1970 British Cohort Study. J. Affect. Disord. 2024, 351, 607–614. [Google Scholar] [CrossRef]
  40. Kim, H.S.; Sherman, D.K.; Taylor, S.E. Culture and social support. Am. Psychol. 2008, 63, 518–526. [Google Scholar] [CrossRef]
  41. Johansen, R.; Espetvedt, M.N.; Lyshol, H.; Clench-Aas, J.; Myklestad, I. Mental distress among young adults–Gender differences in the role of social support. BMC Public Health 2021, 21, 2152. [Google Scholar] [CrossRef]
  42. Cohen, S.; Wills, T.A. Stress, social support, and the buffering hypothesis. Psychol. Bull. 1985, 98, 310–357. [Google Scholar] [CrossRef]
  43. Pei, R.; Courtney, A.L.; Ferguson, I.; Brennan, C.; Zaki, J. A neural signature of social support mitigates negative emotion. Sci. Rep. 2023, 13, 17293. [Google Scholar] [CrossRef]
  44. Rutter, M. Psychosocial resilience and protective mechanisms. Am. J. Orthopsychiatry 1987, 57, 316–331. [Google Scholar] [CrossRef]
  45. McLean, L.; Gaul, D.; Penco, R. Perceived Social Support and Stress: A Study of 1st Year Students in Ireland. Int. J. Ment. Health Addict. 2022, 21, 2101–2121. [Google Scholar] [CrossRef]
  46. Gopalan, M.; Linden-Carmichael, A.; Lanza, S. College Students’ Sense of Belonging and Mental Health Amidst the COVID-19 Pandemic. J. Adolesc. Health 2022, 70, 228–233. [Google Scholar] [CrossRef] [PubMed]
  47. Harris, P. Maslow, Abraham (1908–1970) and Hierarchy of Needs. In The Palgrave Encyclopedia of Interest Groups, Lobbying and Public Affairs; Harris, P., Bitonti, A., Fleisher, C.S., Binderkrantz, A.S., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 1–3. [Google Scholar]
  48. Chen, C.; Bian, F.; Zhu, Y. The relationship between social support and academic engagement among university students: The chain mediating effects of life satisfaction and academic motivation. BMC Public Health 2023, 23, 2368. [Google Scholar] [CrossRef] [PubMed]
  49. Cao, F.; Li, J.; Xin, W.; Cai, N. Impact of social support on the resilience of youth: Mediating effects of coping styles. Front. Public Health 2024, 12, 1331813. [Google Scholar] [CrossRef]
  50. Holt-Lunstad, J. Social connection as a critical factor for mental and physical health: Evidence, trends, challenges, and future implications. World Psychiatry 2024, 23, 312–332. [Google Scholar] [CrossRef] [PubMed]
Societies 15 00238 g001
Figure 1. The chain of resilience process in the ecosystem. Note, within the ecosystem, the resilience process of individuals includes internal and external resources, as proposed by resource conservation theory. Under low stress, individuals will build more internal resources (such as healthy lifestyle or personality traits) and external resources (such as perceived family, peer, school, and community support), which will promote or hinder developmental outcomes. When stressors activate the resilience process, individuals will seek and utilize resources to cope with or buffer the effects of stress, which has a protective effect on developmental outcomes. However, when perceived social resources are insufficient or ineffective in providing help, it can have a risky effect on developmental outcomes. This chain evolves over time; for instance, the developmental outcome of W2 is the resilient individual of W3, and so on.
Figure 1. The chain of resilience process in the ecosystem. Note, within the ecosystem, the resilience process of individuals includes internal and external resources, as proposed by resource conservation theory. Under low stress, individuals will build more internal resources (such as healthy lifestyle or personality traits) and external resources (such as perceived family, peer, school, and community support), which will promote or hinder developmental outcomes. When stressors activate the resilience process, individuals will seek and utilize resources to cope with or buffer the effects of stress, which has a protective effect on developmental outcomes. However, when perceived social resources are insufficient or ineffective in providing help, it can have a risky effect on developmental outcomes. This chain evolves over time; for instance, the developmental outcome of W2 is the resilient individual of W3, and so on.
Societies 15 00238 g001
Societies 15 00238 g002
Figure 2. The RI-CLPM for three variables and three waves. Note, R = resilience, S = social support, N = negative emotions. Parameters and non-significant paths are not shown for simplicity. * p < 0.05, ** p < 0.01, *** p < 0.001. The double arrows in the figure indicate contemporaneous covariances correlation, while the single arrows indicate effect path coefficients.
Figure 2. The RI-CLPM for three variables and three waves. Note, R = resilience, S = social support, N = negative emotions. Parameters and non-significant paths are not shown for simplicity. * p < 0.05, ** p < 0.01, *** p < 0.001. The double arrows in the figure indicate contemporaneous covariances correlation, while the single arrows indicate effect path coefficients.
Societies 15 00238 g002
Table 1. Demographic characteristics of 1495 samples (score, M ± SD).
Table 1. Demographic characteristics of 1495 samples (score, M ± SD).
VariableNW1PRW1SSW1NENW2PRW2SSW2NENW3PRW3SSW3NE
Male81658.29 ± 9.84056.64 ± 9.9727.71 ± 9.63181657.81 ± 14.30953.89 ± 12.26718.81 ± 17.57381656.21 ± 14.23551.94 ± 12.49439.85 ± 17.570
Female67957.57 ± 9.81358.27 ± 9.5296.13 ± 8.16567957.43 ± 10.79155.37 ± 10.65311.52 ± 13.09967955.95 ± 11.47353.61 ± 11.04733.19 ± 14.252
p 0.1590.001<0.001 0.5600.0130.001 0.7010.006<0.001
Travel frequency
Not have88956.47 ± 9.70956.06 ± 9.9347.46 ± 8.74885856.73 ± 13.08953.53 ± 11.66516.72 ± 16.59296755.04 ± 12.97151.39 ± 11.70438.2 ± 16.88
Once or more60660.16 ± 9.60159.3 ± 9.2896.31 ± 9.38563758.86 ± 12.37555.96 ± 11.32813.85 ± 15.29452858.02 ± 12.98655.08 ± 11.85434.3 ± 15.421
p <0.001<0.0010.016 0.001<0.001<0.001 <0.001<0.001<0.001
Vegetarian or not
Not a vegetarian91058.02 ± 9.77157.87 ± 9.6536.52 ± 8.19796157.65 ± 11.69354.8 ± 11.17413.3 ± 14.403103655.92 ± 12.72552.72 ± 11.86234.41 ± 14.999
No fish or/and meat58557.88 ± 9.93156.61 ± 9.9957.73 ± 10.14653457.61 ± 14.66454.15 ± 12.2819.45 ± 18.1545956.48 ± 13.76152.64 ± 11.94742.27 ± 18.293
p 0.7910.0160.015 0.9530.300<0.001 0.4590.902<0.001
Note, psychological resilience (PR), social support (SS), and negative emotions (NE). W1, W2, and W3 represent the first, second, and third waves, respectively.
Table 2. Correlations between psychological resilience, social support, and negative emotions.
Table 2. Correlations between psychological resilience, social support, and negative emotions.
VariableW1PRW1SSW1NEW2PRW2SSW2NEW3PRW3SSW3NE
W1PR1
W1SS0.378 ***1
W1NE0.353 ***0.410 ***1
W2PR0.641 ***0.295 ***0.313 ***1
W2SS0.352 ***0.714 ***0.365 ***0.425 ***1
W2NE0.319 ***0.351 ***0.779 ***0.390 ***0.441 ***1
W3PR−0.491 ***−0.238 ***−0.246 ***−0.439 ***−0.260 ***−0.248 ***1
W3SS−0.290 ***−0.267 ***−0.284 ***−0.287 ***−0.314 ***−0.291 ***0.384 ***1
W3NE−0.244 ***−0.209 ***−0.304 ***−0.237 ***−0.238 ***−0.314 ***0.320 ***0.479 ***1
Note, psychological resilience (PR), social support (SS), and negative emotions (NE). W1, W2, and W3 represent the first, second, and third waves, respectively. *** p < 0.001.
Table 3. Parameter estimates for RI-CLPM.
Table 3. Parameter estimates for RI-CLPM.
95% CI
PredictorOutcomepLowUpStandardized Coefficient
Autoregressive
W_R1W_R20.0010.0620.2250.098
W_R2W_R30.0000.0620.2250.143
W_S1W_S20.0070.0340.2120.095
W_S2W_S30.0080.0340.2120.120
W_N1W_N20.0000.3130.4210.184
W_N2W_N30.0000.3130.4210.366
Cross-lagged
W_S1W_N20.008−0.337−0.050−0.096
W_N2W_R30.000−0.155−0.067−0.148
W_N2W_S30.000−0.144−0.064−0.159
Trait Covariances
R_traitS_trait0.00024.98537.2990.858
R_traitN_trait0.000−26.153−13.210−0.705
S_traitN_trait0.000−23.122−11.042−0.597
GenderS_trait0.0020.5082.2180.111
GenderN_trait0.000−3.591−1.840−0.277
WP Contemporaneous Covariances
W_R1W_S10.00025.17237.9470.527
W_S1W_N10.000−22.706−9.932−0.278
W_R1W_N10.000−24.756−11.086−0.297
W_R2W_S20.00068.06086.1570.693
W_S2W_N20.000−24.465−6.759−0.108
W_R2W_N20.028−21.296−1.122−0.066
W_R3W_S30.00076.18292.2060.771
W_S3W_N30.069−15.1530.614−0.052
W_R3W_N30.317−13.2614.312−0.028
Note, CI = confidence interval, WP = within-person. Only significant and critical path parameters were reported.
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Zhang, Y.; Chen, H. Social Support and Negative Emotions in the Process of Resilience: A Longitudinal Study of College Students. Societies 2025, 15, 238. https://doi.org/10.3390/soc15090238

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Zhang Y, Chen H. Social Support and Negative Emotions in the Process of Resilience: A Longitudinal Study of College Students. Societies. 2025; 15(9):238. https://doi.org/10.3390/soc15090238

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Zhang, Yuqi, and Hongshuo Chen. 2025. "Social Support and Negative Emotions in the Process of Resilience: A Longitudinal Study of College Students" Societies 15, no. 9: 238. https://doi.org/10.3390/soc15090238

APA Style

Zhang, Y., & Chen, H. (2025). Social Support and Negative Emotions in the Process of Resilience: A Longitudinal Study of College Students. Societies, 15(9), 238. https://doi.org/10.3390/soc15090238

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