Complementing each other: a topological analysis of prosocial behaviors and meaning in life among master's degree students in medical schools: based on longitudinal tracking data

Complementing each other: a topological analysis of prosocial behaviors and meaning in life among master's degree students in medical schools: based on longitudinal tracking data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Complementing each other: a topological analysis of prosocial behaviors and meaning in life among master's degree students in medical schools: based on longitudinal tracking data Niu Yangtong, Guo Li, Li Yuting, Li Xinying, Cai Wenwei, Tong Shiyu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7164933/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The complex and multidimensional internal attributes of prosocial behavior, as crucial aspects of the growth of contemporary graduate students in medical schools, have not yet been systematically explored. Using a network analysis approach to explore the core features, dynamic evolution, and external associations of graduate students in medical schools’network adaptation, this study explored the interrelationships between prosocial behaviors and meaning in life among graduate students inmedical schools. It was found that there is a bidirectional facilitative relationship between the two, with individuals with high meaning in life being more likely to engage in prosocial behaviors, which in turn enhances the individual's meaning in life. Among them, altruistic behavior is the core dimension of prosocial behavior, which is closely related to anonymity, emotions,and the public. In addition, prosocial behaviors showed dynamic changes over time, with helping behaviors in emergency situations being a significant predictor of subsequent altruistic and emotional behaviors. prosocial behavior meaning in life network analysis master's degree students Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction With the rapid development of the medical field, the requirements for medical talent have become increasingly stringent. As the future medical backbone of medical school graduate students, their professionalism and moral character have become the focus of extensive social attention [ 1 ] . Prosocial behavior is a key indicator of an individual's social responsibility and moral level, which is particularly important for graduate students in medical schools [ 2 ] . As a noble cause to serve society and benefit mankind, the prosocial behavior of postgraduate students has a direct effect on their attitudes and behaviors in future medical work, which is profoundly important for improving the quality of medical services, constructing harmonious doctor‒patient relationships and developing healthy and harmonious societies[3]. Moreover, prosocial behavior is crucial to the psychological development of students and is closely related to positive developments such as increased self-esteem, improved quality of interpersonal relationships, and academic achievement [ 4 ] . Studies have shown that there is a positive feedback loop between prosocial behavior and well-being that promotes each other [ 5 ] . Therefore, exploring the prosocial behaviors of graduate students in medical schools has important personal and social value. Prosocial behavior, which covers a wide range of forms, such as helping, cooperating, sharing, and comforting, is all behavior that benefits others and society and often occurs in specific social situations. Carlo and Randall (2002) classified them into six types, namely, altruistic, emotional, dire, compliant, anonymous, and public, where the first five are driven mainly by internal motives, whereas public behavior is more influenced by external motives [6]. Prosocial behavior not only promotes social harmony and progress but also enhances the meaning of an individual's life and becomes the cornerstone of social and personal development. In the field of medicine, such behaviors are characteristic of the profession. For example, anonymity may be manifested in anonymous donations to needy patients, whereas helpfulness in emergency situations is common in volunteer support in public health emergencies [ 7 ] . In life, i.e., an individual's perception of themselves, the world around them, and their place in the world, as well as their understanding of life's purpose goals and mission, has a motivational role and influences the way an individual chooses to behave in the present [ 8 – 9 ] . Baumeister et al. [ 10 ] suggested that people who value the meaning of life are more inclined to care for and help others as givers, to contribute to society, and to see helping people and serving society as a process of pursuing and achieving happiness. In other words, people who possess and seek meaning in life more often engage in prosocial behaviors, and prosocial behaviors significantly enhance an individual's meaning in life. Research has shown that prosocial behaviors enhance meaning in life by increasing an individual's sense of worth and self-esteem [ 11 ][ 12 ] . A longitudinal study by He et al. [ 13 ] further confirmed the bidirectional facilitative relationship between prosocial behaviors and meaning in life. However, the current study disagrees on the direction of the two influences, and most of the evidence is based on cross-sectional questionnaires and experiments and lacks longitudinal data support, which needs to be further explored. As the backbone of the future medical team, the professionalism and ethical quality of medical school graduate students are directly related to the quality of medical services and the doctor–patient relationship. The essence of medical practice is altruism and humanistic care [ 14 ] . Studies have shown that medical students' altruistic values are significantly and positively correlated with their professional identity [ 15 ] . Graduate students in medical schools generally have a high sense of professional mission, and the meaning in their lives often stems from the recognition of the value of "saving lives and helping people" [ 16 ] . When individuals regard medical practice as a way to realize meaning in life, they more actively participate in prosocial behaviors such as volunteering and unpaid clinics [ 17 ] . Therefore, exploring the dynamic relationship between their prosocial behaviors and meaning in life is crucial to cultivating socially responsible medical professionals. Although studies have revealed the correlation between prosocial behaviors and meaning in life through cross-sectional data [ 11 – 12 ] , few longitudinal data have been validated. In addition, prosocial behaviors in medical education are context dependent (e.g., public clinic vs. anonymous assistance), and traditional analytical methods have difficulty capturing such behavior through dimensionally structured interactions. In view of the current findings and theoretical support, this study concludes that there is a mutually reinforcing relationship between prosocial behaviors and having meaning in life. Graduate students' self-awareness develops rapidly, their outlook on life and values is gradually established [ 18 ] , and individuals think more deeply about the world around them and their own goals and values in graduate school than in secondary school [ 19 ] , which is a critical period for the establishment of life pursuits. Therefore, this study combines a cross-sectional study with a tracer study aimed at determining the interrelationship between meaning in life and prosocial behaviors. Network analysis is a new approach to describing multivariate dependency structures that extends traditional regression methods by being able to quantify and visualize the extent to which these correlated factors are interrelated [ 20 ] . Most previous network analyses have used cross-sectional data to construct undirected networks, but undirected cross-sectional networks provide only limited insights into the temporal and causal relationships estimated by the network model because of their inability to capture temporal variations as well as to discern the direction of the relationships [ 21 ] . Therefore, it is essential to use cohort data to construct temporal and causal relationships between variables. By quantifying the conditional dependencies between variables [ 22 ] , network analysis can directly resolve the independent associations of the dimensions of prosocial behaviors (e.g., altruistic, dire) and avoid the assumption bias of latent variable models. In addition, the cross-lagged network analysis method was developed by using network analysis in cohort data to simultaneously examine all the complex relationships between the hypothesized variables in the network model, taking into account the temporal nature of the correlation effects [ 23 ] . Network analysis is characterized by the fact that the structure of high-dimensional data can still be effectively explored when there is no a priori theory about how the variables are related [ 24 ] . Cross-lagged network analysis can simultaneously test the temporal effect between variables and the predictive path between clusters [ 25 ] , providing a new perspective on the dynamic interaction of "context-behavior-sense of meaning" in medical education. In empirical psychology research, network analysis has the following significant advantages: first, network analysis does not rely on the definition of latent variables but builds a model based on observed variables, thus realizing the direct analysis of the relationship between the observed variables; second, the network analysis technique can analyse the "independent" relationship between the two variables in the complete system, reducing the confusion caused by the "false correlation" in traditional analysis. Second, the network analysis technique can resolve the "independent" relationship between two variables in the complete system, reducing the confusion caused by the "false correlation" in traditional analysis. Third, network analysis integrates all the observed variables into a unified network framework, which can be used to examine the occurrence and development of a certain psychological or behavioral system from the perspective of the overall change in the network [ 26 ] . 2 Methodology 2.1 Participants Study 1 used convenience sampling and a combination of online and offline sampling methods to extract medical school-enrolled graduate students from 2007, following the principle of voluntarism. The subjects seriously answered the questions after review and approval of the subject fee, which excluded those who did not provide serious answers to the regular responses of 64 people, the effective subjects of 1963 people, and the questionnaire recovery rate of 97.81%. In Study 2, the whole group sampling method was used to select 2 classes in a medical school in Shanxi Province and administer the test twice at an interval of 3 months. In the process of response, the subjects followed the voluntary principle. The first measurement (T1) was carried out at the end of October 2024 and was administered to a total of 726 postgraduate medical students in two classes by group administration. Because 15 questionnaires had omissions and chaotic answers, the number of valid questionnaires for the first measurement was 711. There were 202 male and 509 female students, with a mean age of 23.54 ± 1.94 years. The second questionnaire was administered to the same group of people at the end of January of the following year, and to avoid practice effects, all the questions were randomly assigned in this administration. Finally, 711 valid questionnaires of the first time and 697 valid questionnaires of the second time were matched one by one, and finally, 671 questionnaires valid for both measurements were collected. Among them, 193 were male and 482 were female, with a mean age of 23.47 ± 1.45 years. A total of 37 subjects were lost in the two measurements, for an attrition rate of 5.20%. The gender of the attrition subjects and those who took the test on both occasions were tested for differences: the difference in the gender distribution was not significant, χ = 0.67, p = 0.415. The results of the characterization of the attrition subjects, the results of the sensitivity analysis, and other results revealed (p > 0.415) that the difference was not significant. 2.2 Measurement tools 2.2.1 Measurement of prosocial behaviors The Prosocial Tendencies Scale developed by Carlo and revised by Kou Yu et al. [27] was used. The scale has 26 items divided into six dimensions: public, anonymous, altruistic, compliant, emotional, and dire. The scale is scored on a 5-point Likert scale, with higher total scores indicating more prosocial behaviors. The Cronbach's alpha coefficients of the scale were 0.937 and 0.932 for the pre- and postintervention measurements, respectively, in this study, indicating high internal consistency reliability. 2.2.2 Meaning-in-life scale The Chinese version of the Meaning in Life Questionnaire (MLQ) was revised twice [ 28 ][ 29 ] to obtain the same questionnaire structure of 10 entries with a 2-dimensional structure (presence of meaning in life and searching for meaning in life) as Steger. The Cronbach's alpha coefficients for this scale were 0.840 and 0.863 for the pre- and postintervention measurements, respectively, in this study, indicating high internal consistency reliability. 2.3 Data analysis 2.3.1 Network analysis All descriptive statistical analyses in this study were performed via SPSS (version 22.0 for Windows) and reported as the number of cases, means, and standard deviations. Network estimation was performed via R (version 4.2.1), and a T1→T2 cross-lagged network was constructed to explore the predictive path of prosocial behaviors after 3 months. Regularization of the partial correlation network was applied to the R software to identify indicators of node centrality and predictability, which helped to identify key intervention targets [ 30 ] . For the first analysis, data fitting and item network construction were performed via the Gaussian graphical model (GGM) [ 31 ] . The GGM is an undirected network, where nodes represent observed variables and the line between two nodes represents their partial correlation [ 32 ] . Nonsupplementary transformation of the data was performed via a large software package [ 33 ] to account for the assumption of a multivariate normal distribution in the GGM. The use of the least absolute shrinkage and selection operator (LASSO) and the extended Bayesian information criterion (EBIC) have been used to refine the network edges and tune the parameters to enhance interpretability [ 22 ] by normalizing the network by shrinking very small correlations down to zero, through which biased correlations between variables are quantified and the core pivot of the multidimensional results is revealed. Following the recommendations of Epskamp et al. [ 22 ] , we evaluated edge accuracy via the bootstrapped method. Centrality indices indicate how well a node is connected to the rest of the network to determine the importance of each variable [ 34 ] and may indicate influential initial treatment targets [ 35 ] . Next, to quantify the importance of each node in the transected network, we used strength and expected impact as centrality indices [ 36 ] . Strength is calculated by summing the absolute value of the weights of all edges connected to a node, with higher values of strength indicating greater influence in the network [ 37 ] . The expected influence, on the other hand, takes into account both positive and negative relationships within the network on the basis of strength and can provide a more comprehensive assessment of influence on the network as a whole [ 38 ] . To determine confidence intervals (CIs) for the stability of the strength and centrality measures for each edge, we computed 10,000 bootstrapped networks. The bridge expected influence for each node was calculated via the mgm package. 2.3.2 Network Comparison The purpose of network comparison is to uncover the structural differences between different networks via a permutation test, which is implemented via the NCT function within the Network Comparison Test package in R software. In this study, the consistency of the network structure at different points in time is systematically examined via tests that include global and local invariance [ 39 ] . These tests are related to each other and assess network stability and change at different levels. First, the network invariance test serves as an assessment tool for the overall structure and aims to compare the global topological characteristics of networks at different points in time, with the null hypothesis that all corresponding edges are equal in both networks. In contrast, the global intensity invariance test focuses more on the network overall level of connectivity and explores the stability of the network's overall connectivity strength by evaluating the sum of all edge weights or the average of node strengths in the network. By integrating these 2 tests, it is possible to assess changes in prosocial behaviors across time, both holistically and locally. 2.3.3 Cross-Lagged Network Analysis The cross-lagged network model estimates the autoregressive and cross-lagged coefficients over time through a series of regularization regressions. In particular, the autoregressive coefficients reflect the predictive effect of a variable's state in the previous measurement on its state in the next measurement, i.e., the continuity of the variable itself over time, whereas the cross-lagged coefficients indicate the predictive effect of a variable's state in the previous measurement on the state of the other variable in the next measurement, i.e., the interaction of the variables [ 25 ] . This study uses the glmnet package in R [ 40 ] to estimate the cross-lagged network model of prosocial behaviors at two time points and the cross-lagged network model of prosocial behaviors and meaning in life. To enhance the interpretability of the results and create a more intuitive network structure, this study determined the optimal value of the tuning parameter γ through 10-fold cross-validation and applied Graphical Lasso (Glasso) to the estimated regression coefficients to shrink the insignificant paths to zero [ 25 ] . There are more negative edges in the cross-lagged network model, so this study used the expected influence and out-of-expected influence as the centrality indices for the cross-lagged network model. In cross-lagged networks, In-Predictivity is the percentage of the degree to which the variation of a node at a given measurement time point is explained by all nodes at the previous time point; Out-Predictivity is the percentage of the degree to which the variation of all nodes at a given measurement time point is explained by a node at the previous measurement time point. The in-predictability and out-predictability indicate the extent to which each node is predicted by other nodes and the extent to which it predicts other nodes in the network, respectively [ 25 ] . On this basis, considering the binary properties of prosocial behaviors and meaning in life networks, this study calculates cross-cluster metrics, including cross-cluster in-predictability and cross-cluster out-predictability (i.e., the extent to which the variance of a node on T2 is explained by all the nodes of T1 in another cluster and the extent to which a given T1 node accounts for the variance of all the T2 nodes in the other clusters) to the extent to which nodes in different clusters are predictive of each other [ 24 ] . degree of differentiation [ 24 ] . Higher cross-cluster in-prediction indicates that a node is more influenced by all nodes in the out-cluster at the previous point in time: higher cross-cluster out-prediction indicates that a node is more influenced by all nodes in the out-cluster at the later point in time [ 25 ] . 2.3.4 Visualization and stability assessment Visualization of both the cross-sectional network and cross-lagged network is implemented through the qgraph package (version 1.9.5) [ 41 ] . The positions of all nodes in the network are determined by the Fruchterman-Reingold algorithm, which places more strongly connected nodes closer to each other [ 42 ] . The edges in the network represent the partial correlation coefficients between two nodes after controlling for the effects of other variables; the nodes represent the variables, whereas the thickness and color of the edges represent the degree of correlation and the potency, respectively [ 43 ] . In the visualization network, blue edges indicate positive correlations, and red edges indicate negative correlations. Thicker edges indicate stronger correlations between nodes. A circle around the outside of a node indicates the predictability of that node, with the closer to complete filling representing a higher rate of explanation of the node to neighboring nodes [ 44 ] . Autoregressive paths in the cross-lagged network are omitted for a clear presentation of the network graph. The accuracy of the margin estimates was tested via the bootstrap method with 1000 iterations of the network to plot 95% nonparametric bootstrap confidence intervals for each margin [ 37 ] . The robustness of all centrality indices (node strength, node expected impact, out expected impact, and person expected impact) was tested via the case-droping method with correlation stability coefficients (CS coefficients) as the results [24]. The CS coefficient (cor = 0.7) indicates the percentage of samples for which the correlation between the centrality indicator of the bootstrap sample and the centrality indices of the original sample can be maintained, with a correlation of at least 0.7 within a 95% confidence interval obtained via the case-drop method. A CS coefficient between 0.25 and 0.50 indicates that the centrality indices are robust, and a CS coefficient greater than 0.5 indicates strong robustness [ 22 ] . The above network construction and visualization were implemented through the qgraph package and bootnet, which were used to calculate the correlation stability coefficient (CS). 3 Results of the study 3.1 Common method bias test In this study, common method bias was reduced mainly through procedural control and statistical control. First, the purpose of the study was stated before administering the test, and all the subjects completed the code to reduce their likelihood of bias; second, the common method bias test was conducted via Harman's one-factor method to conduct exploratory factor analysis of the items for all the variables [45]. The first measurement had 14 factors with eigenvalues greater than 1 in the unrotated case, and the variance explained by the first factor was 23.98%, which is less than the 40% criterion for the critical value. The second measurement had 13 factors with eigenvalues greater than 1 in the unrotated case, and the variance explained by the first factor was 23.92%, which is less than the 40% criterion for the critical value, suggesting that the two measurements of the present study do not have serious common methodological bias. 3.2 Descriptive statistics The means and standard deviations of the dimensions of the Meaning in Life Scale and the Prosocial Behavior Scale are shown in Table 1 . The skewness (-0.59 to 1.31) and kurtosis (-0.63 to 0.90) of all the items indicate that the data are basically normally distributed [ 46 ] , which satisfies the conditions for the network analysis conducted in Study II. Table 1 Descriptive statistics of the dimensions of meaning in life and prosocial behaviors Dimension N = 1943 N = 671(T1) N = 671(T2) M ± SD Skewness Kurtosis M ± SD Skewness Kurtosis M ± SD Skewness Kurtosis 1 — — — 26.48 ± 4.75 -0.53 0.35 26.73 ± 4.79 -0.63 0.5 2 — — — 23.6 ± 5.78 -0.15 -0.51 23.87 ± 5.94 -0.37 -0.32 3 13.76 ± 2.98 -0.31 0.25 13.86 ± 2.53 0.19 0.17 13.38 ± 2.70 0.21 0.15 4 18.32 ± 3.80 -0.5 0.55 17.69 ± 3.28 -0.32 1.09 17.71 ± 3.42 -0.24 0.43 5 15.45 ± 2.65 -0.59 0.75 15.28 ± 2.39 -0.45 1.31 15.48 ± 2.51 -0.33 0.47 6 18.62 ± 3.18 -0.34 0.23 18.56 ± 2.81 -0.11 0.35 18.28 ± 2.96 0.04 -0.04 7 18.75 ± 3.16 -0.41 0.55 18.6 ± 2.93 -0.17 0.54 18.5 ± 3.10 -0.05 0.02 8 12.06 ± 1.77 -0.56 0.90 11.89 ± 1.68 -0.33 1.12 11.93 ± 1.74 -0.26 0.56 Note: 1 = The search for meaning, 2 = The presence of meaning, 3 = Public, 4 = Anonymous, 5 = Altruistic, 6 = Compliant, 7 = Emotional, 8 = Dire. 3.3 Correlation analysis of variables at each time point Correlation analysis was conducted for each variable under the two time points of T1 and T2. The results revealed that the dimensions of the Meaning in Life Scale at T1 were significantly correlated with the dimensions of the Prosocial Behavior Scale at T1; the dimensions of the Meaning in Life Scale at T2 were significantly correlated with the dimensions of the Prosocial Behavior Scale at T2. For specific details, please refer to Table 2 . Table 2 Correlation analysis of meaning in life and prosocial behaviors (N = 671) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 — 2 0.39 — 3 0.24 0.26 — 4 0.17 0.26 0.38 — 5 0.28 0.34 0.38 0.71 — 6 0.24 0.27 0.55 0.59 0.64 — 7 0.26 0.33 0.55 0.53 0.59 0.70 — 8 0.26 0.32 0.45 0.53 0.65 0.60 0.58 — 9 0.67 0.30 0.20 0.17 0.24 0.22 0.25 0.25 — 10 0.28 0.55 0.23 0.22 0.26 0.21 0.25 0.27 0.41 — 11 0.14 0.22 0.53 0.19 0.19 0.32 0.30 0.28 0.15 0.27 — 12 0.16 0.23 0.17 0.50 0.39 0.37 0.31 0.36 0.23 0.29 0.25 — 13 0.22 0.27 0.25 0.41 0.50 0.42 0.39 0.45 0.31 0.34 0.33 0.68 — 14 0.19 0.18 0.37 0.35 0.36 0.63 0.46 0.39 0.21 0.25 0.45 0.56 0.59 — 15 0.23 0.28 0.38 0.30 0.33 0.45 0.52 0.39 0.26 0.37 0.54 0.51 0.58 0.65 — 16 0.21 0.28 0.28 0.32 0.37 0.40 0.40 0.54 0.28 0.39 0.46 0.53 0.62 0.58 0.61 Note: **, indicates p < 0.05, and all of the above variables are at less than 0.05.1 = the presence of meaning T1, 2 = The search for meaning T1, 3 = Public T1, 4 = Anonymous T1, 5 = Altruistic T1, 6 = Compliant T1, 7 = Emotional T1, 8 = Dire T1, 9 = The presence of meaning T2, 10 = The search for meaning T2, 11 = Public T2, 12 = Anonymous T2, 13 = Altruistic T2, 14 = Compliant T2, 15 = Emotional T2, 16 = Dire T2. 3.4 Cross-sectional network of the prosocial behaviors of medical school graduate students The transversal network of the dimensions of postgraduate students’ prosocial behaviors in medical schools is shown in Fig. 1 . In this network, nodes represent dimensions of prosocial behaviors, and edges represent Regularization biocorrelations between dimensions. Six nodes connected by 15 edges form a network of dimensions of postgraduate students of medical schools’ prosocial behavior, with a network density of 1. The weights of the edges range from − 0.05–0.53 (from Table 3 ), with an average weight of 0.17. The coefficients of the edges for the transverse network of prosocial behaviors are presented in the network version of Table 3 . The coefficients of the edges of the transverse network of prosocial behaviors are shown in the network version of Table 3 . In the network of dimensions of prosocial behaviors, "altruistic" (intensity = 2.8) is the highest-intensity node, and its intensity is significantly higher than that of the other nodes, indicating that "altruistic" behavior has the strongest influence on the other nodes in the network.. "altruistic" (expected influence = 0.64) and "emotional" (expected influence = 0.60) are the two nodes with the highest expected influence, and their expected influence is significantly greater than that of the other nodes, indicating that they are the most influential nodes on prosocial behavior as a whole. These findings suggest that they are the two nodes with the strongest influence on the overall structure of prosocial behaviors (see Fig. 2 , Fig. 3 and Fig. 4 ). The CS coefficient for both the intensity and the expected influence is 0.75, indicating good stability of the indicator (see Fig. 5 for details). The most highly weighted edges in the network are "anonymous-altruistic" (r = 0.53) and "public-emotional" (r = 0.29), which are significantly stronger than the remaining edges in the network (see Fig. 6 ). Table 3 Table of Marginal Coefficients of Cross-Sectional Network for prosocial behaviors From To Weight Pub. --- Anon. -0.04 Pub. --- Altru. -0.05 Anon. --- Altru. 0.53 Pub. --- Comp. 0.22 Anon. --- Comp. 0.14 Altru. --- Comp. 0.16 Pub. --- Emo. 0.29 Anon. --- Emo. 0.04 Altru. --- Emo. 0.16 Comp. --- Emo. 0.28 Pub. --- Dire. 0.09 Anon. --- Dire. 0.15 Altru. --- Dire. 0.21 Comp. --- Dire. 0.12 Emo. --- Dire. 0.22 3.4 Cross-lagged networks of postgraduate students’ prosocial behaviors in medical schools The results of the network comparisons revealed that the difference in overall strength between the T1 network (overall strength = 2.5) and the T2 network (overall strength = 2.5) was not significant ( p = 0.937), and network invariance testing passed ( p > 0.001) (see Fig. 7 , and the borderline coefficients are shown in Tables 4 and 5 ). The networks at the two time points did not show significant differences in overall strength, indicating that the overall strength of network connections remained relatively stable. The results of network invariance testing indicated that the overall topology of the prosocial behavior network also did not change significantly between the two time points. Table 4 Coefficients for each side of the prosocial behaviors cross-sectional network at time point T1 From To Weight Anon. --- Altru. 0.42 Pub. --- Comp. 0.2 Anon. --- Comp. 0.11 Altru. --- Comp. 0.19 Pub. --- Emo. 0.24 Anon. --- Emo. 0.09 Altru. --- Emo. 0.14 Comp. --- Emo. 0.32 Pub. --- Dire. 0.09 Anon. --- Dire. 0.12 Altru. --- Dire. 0.23 Comp. --- Dire. 0.2 Emo. --- Dire. 0.14 Table 5 Coefficients for each borderline of the prosocial behaviors cross-sectional network at time point T2 From To Weight Pub. --- Anon. -0.1 Anon. --- Altru. 0.42 Pub. --- Comp. 0.1 Anon. --- Comp. 0.18 Altru. --- Comp. 0.14 Pub. --- Emo. 0.32 Anon. --- Emo. 0.11 Altru. --- Emo. 0.14 Comp. --- Emo. 0.3 Pub. --- Dire. 0.14 Anon. --- Dire. 0.08 Altru. --- Dire. 0.26 Comp. --- Dire. 0.15 Emo. --- Dire. 0.21 The cross-lagged network of the prosocial behaviors of medical school graduate students is shown in Fig. 8 , in which individual nodes represent a dimension of prosocial behaviors and edges are regularized regression coefficients between dimensions. Six nodes are connected by 30 directed edges to form an intertemporal cross-lagged network model of individual prosocial behaviors. The weights of the edges ranged from − 0.11–0.24, with an average weight of 0.06 (see Table 6 in the web version for details of the edge coefficients). Table 6 Table of borderline coefficients for the Cross-Lagged Network of graduate students of medical schools prosocial behaviors (autoregressions omitted) Pub. Anon. Altru. Comp. Emo. Dire. Pub. 0.00 -0.11 0.00 0.02 0.10 0.00 Anon. 0.00 0.00 0.05 0.00 0.00 0.00 Altru. 0.00 0.00 0.00 -0.09 -0.06 0.00 Comp. 0.01 0.11 0.05 0.00 0.13 0.03 Emo. 0.00 0.00 0.04 0.05 0.00 0.05 Dire. 0.04 0.22 0.24 0.06 0.16 0.00 3.5 Cross-lagged networks of prosocial behaviors and meaning in life among medical school graduate students The cross-lagged network of postgraduate students’ prosocial behaviors and meaning in life is shown in Fig. 9 , where 8 nodes connected by 56 directed edges form the cross-lagged network model of postgraduate students’ prosocial behaviors across time. The weights of the edges ranged from − 0.18–0.34, with an average weight of 0.22 (see Table 7 for details of the edge coefficients). Table 7 Table of borderline coefficients for the Cross-Lagged Networks of prosocial behaviors and meaning in life Variables MLQS MLQP Pub. Anon. Altru. Comp. Emo. Dire. MLQS 0.00 0.01 0.03 0.05 0.04 0.00 0.06 0.03 MLQP 0.04 0.00 0.00 0.02 0.02 0.02 0.02 0.01 Pub. 0.08 0.00 0.00 -0.12 -0.05 0.02 0.09 0.00 Anon. 0.04 0.01 0.00 0.00 0.06 0.00 0.00 0.00 Altru. 0.00 0.00 0.00 0.00 0.00 -0.09 -0.09 0.00 Comp. 0.00 0.00 0.00 0.10 0.07 0.00 0.14 0.03 Emo. 0.00 0.07 0.00 0.00 0.04 0.05 0.00 0.04 Dire. 0.15 0.16 0.02 0.17 0.24 0.05 0.13 0.00 Considering that the present study is concerned with the predictive role of graduate students’ meaning in life and prosocial behaviors, cross-cluster in-predictivity and cross-cluster out-predictivity were specifically computed with the bridge borderline. Cross-cluster in-predictability and cross-cluster out-predictability can reveal the extent to which individual nodes are predicted by out-cluster nodes as well as the extent to which they are predicted by a node in the out-cluster. Bridge edges are edges formed by connections between nodes located in two different clusters. The cross-lagged network analysis revealed that "the search for meaning" (cross-cluster predictability = 0.06) was the most predictive node in the meaning-of-life cluster, indicating that "the search for meaning" was the most predictive node in the prosocial behavior cluster. This indicates that "The search for meaning in life" is the most predictive node in the meaning in life cluster for nodes within the prosocial behaviors cluster. The performance of the variables also varied in terms of cross-cluster in-predictability. Among them, urgency likewise showed high predictability" (cross-cluster in-predictability = 0.06). The cross-cluster inpredictability of the other variables was generally low, with no particularly prominent nodes, so we did not analyse them in depth (see Fig. 10 ). 4 Discussion This study examines the interrelationship between meaning of life and prosocial behaviors by combining cross-lagged network analysis of cross-sectional measurements and tracking data and analyses the specific relationship between meaning in life and six different types of prosocial behaviors. The content of this study is organized on the following three levels. First, at the "one-dimensional" level, cross-sectional network analysis is used to explore the core characteristics of the prosocial behaviors of postgraduate medical students and to analyse the key components of their prosocial behaviors to provide a more comprehensive and in-depth perspective for understanding their performance in prosocial behaviors. Second, at the "two-dimensional" level, the evolution of the internal structure of the prosocial behaviors of graduate students in medical schools over time is deeply investigated through network comparisons to better reveal the dynamics of the prosocial behaviors of graduate students in medical schools. Finally, at the "three-dimensional" level, a cross-lagged binary network model is constructed to explore the core nodes and key pathways that shape the influence of prosocial behaviors on external variables. The main findings of the study are summarized and discussed below. 4.1 Core components of the prosocial behaviors of graduate students in medical schools The results of the cross-sectional network analysis indicated that "altruistic" nodes had the highest intensity. As a key component of prosocial behaviors, altruistic behavior occupies a central position in the prosocial behavior of postgraduate students in medical schools, and its high-intensity node status highlights their positive performance and significance in terms of values, behavioral orientation, educational effectiveness, future career development, and social adaptability, which is in line with the results of a series of previous studies; that is, altruistic behavior is closely related to prosocial behavior [ 47 ] . An altruistic disposition essentially aims to focus on and promote the well-being of others on the basis of transcending self-interest [ 48 ] , and an altruistic disposition, as a core dimension of the prosocial behaviors of postgraduate students in medical schools, aims to cultivate medical professionals with a high degree of social responsibility, professional ethics, and the ability to achieve interpersonal harmony in line with the social norms of the collectivist culture in China. As can be visualized from the cross-sectional network diagram, altruism is strongly linked to anonymity, emotion, and the public. This suggests that altruism is not only an intrinsic motivator for prosocial behaviors but also a key factor in shaping an individual's behavioral performance in a given social situation [ 49 ] . However, the negative connection between the "altruistic" and the "public" reveals a complex and important relationship: altruism, as an important driver of prosocial behaviors among graduate students in medical schools, plays a positive role in facilitating their ability to help others and care for the community. The difference in the performance of anonymous versus public prosocial behaviors may be closely related to the collectivist values of traditional Chinese culture. According to Hofstede's theory of cultural dimensions, Chinese culture is characterized by a high degree of collectivism, emphasizing group harmony and social norms rather than individual performance [ 50 ] . This cultural context may reinforce the behavioral tendency of "doing good without leaving a name": anonymous prosocial behaviors (e.g., anonymous donations) are more likely to be adopted by individuals because they conform to the social expectation of collectivism of "doing good in a low profile" [ 51 ] . In contrast, public behaviors (e.g., recognition of volunteering) may be limited by triggering social pressure in face culture. Facial culture emphasizes the need for individuals to avoid showing off or incurring jealousy in public [ 52 ] , which may lead medical students to weigh the social evaluation of their behaviors (e.g., whether they are perceived as "putting on a show") more cautiously in public situations, thereby inhibiting the outwards display of prosocial behaviors [ 53 ] . In addition, the Confucian norm of "restoring one's self to propriety" [ 54 ] further encourages individuals to practice morality in an introverted manner rather than pursuing social praise. Such behavioral differences in cultural contexts reflect the complexity of altruistic prosocial behaviors in different contexts and suggest that we need to consider cultural factors and contextual characteristics when understanding and encouraging prosocial behaviors. Future research could incorporate cross-cultural comparisons (e.g., comparing medical students in individualistic cultures) to test the cultural generalizability of the difference in anonymous versus public behavior. 4.2 Key internal mechanisms of the prosocial behaviors of graduate students in medical schools In the prosocial behavior network, the most highly weighted edges are "anonymous–altruistic" and "public–emotional", and these two edges are significantly stronger than the remaining edges in the network. This not only indicates the strong correlation between these dimensions but also reflects their key role in the overall prosocial behavior network framework. The stronger connection of "anonymous–altruistic" suggests that individuals' altruistic tendencies are more likely to be triggered in anonymous situations and thus more willing to engage in prosocial behaviors. At the same time, anonymity also generates interconnections through dire and altruistic actions (as shown in Fig. 1 ). Prosocial behavior theory provides an explanation of this process. Specifically, prosocial behaviors refer to behaviors in which individuals help others without expecting a reward. This behavior can be direct, such as donating money or goods, or indirect, such as working for a social cause or participating in volunteer activities [ 55 ] . Anonymity may be more reflective of the selfless qualities of graduate students in medical schools while also reducing social pressures and expectations for individuals to express altruistic behaviors more freely. Anonymous individuals play an important facilitating role in prosocial behaviors by providing an environment free from external interference for graduate students in medical schools, which makes them more able to freely display altruistically and thus more revealing of their noble spirit of selflessness and dedication. On the other hand, the public may increase an individual's social visibility, thereby affecting the expression of emotional and prosocial behaviors. The strength of the "public–emotional" dimension suggests that both dimensions play a pivotal role in the pro-social behavior system, possibly shaping behavioral patterns through the interaction of emotional drive and social reinforcement. The strong association between the public and emotions in prosocial behaviors reflects the "social self-efficacy" epiphenomenon of social cognitive theory. According to self-presentation theory, in the public, an individual's prosocial behaviors are reinforced by social evaluations (e.g., receiving praise or recognition) [ 56 ] . This external feedback can increase the positive value of emotional arousal, forming a cycle of "emotion-driven behavior→social reinforcement→emotional satisfaction". For example, public donations not only satisfy an individual's need for empathy (emotional) but also reinforce his or her self-concept through social recognition (e.g., being perceived as a "generous person") [ 57 ] , thus reinforcing the emotion‒behavior link. Emotion motivates helpers to help when they are emotionally aroused, whereas public helpers amplify emotional efficacy through social reinforcement, forming a cycle of emotion-driven behavior→social reinforcement→ emotional satisfaction. Thus, the prominent strength of the "public-emotional" border in prosocial behaviors reflects the dynamic synergy between emotion and social context, both as a result of emotional responses and as a product of social context shaping. 4.3 Stability of the prosocial behaviors of graduate students in medical schools over time Comparative analyses of the cross-sectional networks revealed that the overall strength of prosocial behaviors was stable in the time dimension, but the centrality of its internal components as well as the strength of some of its margins changed over time. This result is consistent with the process, stage, and complexity of the prosocial behaviors of medical school graduate students mentioned in the literature [ 58 ] , which emphasizes that individuals' prosocial behaviors in their living environments are not static but rather are influenced by a variety of factors and may change dynamically over time. In addition, the dynamic changes within the prosocial behavior network reveal the plasticity of prosocial behaviors. This plasticity is reflected not only in the individual differences among medical school graduate students but also in the multidimensional and multilevel characteristics of the prosocial behavior process. In other words, the centrality of the components of prosocial behaviors and the links between them may play different roles at different time stages. Therefore, the dynamic changes in prosocial behaviors in the time dimension also provide a theoretical basis for further exploration of the intertemporal directed network relationships among its internal components. The cross-lagged network revealed that "altruistic" (T1→T2 autoregressive coefficient = 0.24) and "emotional" (T1→T2 autoregressive coefficient = 0.16) exhibited strong continuity in the time dimension (Table 6 ). According to self-determination theory (SDT) [ 59 ] , the stability of altruism as intrinsically motivation-driven behavior (e.g., uncompensated consultations based on a sense of professional mission) stems from the deep internalization of the values of the medical profession by graduate students in medical schools. Emotion (e.g., empathy-driven helping behavior) is maintained through the "social reinforcement cycle" of emotion-as-social information theory (EASI) [ 60 ] : emotional arousal at T1 (e.g., empathy triggered by the patient's distress) motivates individuals to engage in prosocial behaviors, whereas social feedback at T2 (e.g., patient gratitude or peer recognition) further reinforces the emotional‒behavioral link [ 57 ] . Although the overall network structure was stable, the strength of some edges (e.g., "public-emotional") increased slightly between T1 (r = 0.29) and T2 (r = 0.32) (Tables 4 and 5 ), suggesting that this connection may be influenced by contextual reinforcement effects. According to self-presentation theory [56], public prosocial behaviors (e.g., clinical activities) amplify the positive value of emotional experiences through social evaluations (e.g., public praise), resulting in a cycle of "emotion-driven→behavioral outwards appearance→social reinforcement" [ 61 ] . For example, public helping behaviors at T1 (e.g., community health outreach) may enhance an individual's emotional efficacy through social recognition at T2 (e.g., being labelled a "healer"), which in turn promotes subsequent behaviors [ 11 ] . The cross-lagged network analyses further revealed that "dire" (e.g., helping behaviors in medical emergencies) was a significant predictor of altruism (β = 0.24) and emotionality (β = 0.16) at T1 and T2 (Table 6 ). This result is consistent with the contingency model of prosocial behaviors [ 7 ] , in which emergency situations promote immediate helping behaviors by activating individuals' perceptions of responsibility and empathic responses and may enhance long-term altruistic tendencies through the accumulation of "feelings of competence" (e.g., successful handling of an emergency) [ 62 ] . For example, emergency rescue experiences at T1 may increase medical students' self-efficacy to be more proactive in engaging in high-risk volunteerism (e.g., disaster medical support) at T2. The findings of this study provide an important basis for medical education, such as contextual design strategies. The reinforcement of the "public-emotional" connection suggests that creating socially visible practice scenarios (e.g., public clinics, honor recognition systems) can be an effective way to use social feedback mechanisms to promote the persistence of prosocial behaviors [ 57] . 4.4 Relationships between prosocial behaviors and meaning in life among graduate students in medical schools The present study revealed a significant bidirectional facilitative relationship between meaning in life and prosocial behaviors among medical school graduate students, a result that is highly compatible with the theoretical framework and established research. Individuals' pursuit of meaning in life promotes prosocial behaviors; specifically, individuals may increase their prosocial behaviors to receive psychological approval rather than extrinsic rewards. From the perspective of motivational mechanisms, meaning in life serves as an intrinsic driving force that prompts medical students to internalize their sense of professional mission as a core motivation for helping behaviors through autonomy need satisfaction in self-determination theory [ 59 ] . Specifically, individuals with greater meaning in life are more inclined to regard medical practice as an important way to realize the value of life [ 16 ] and thus actively practice altruistic prosocial behaviors (e.g., unpaid volunteer clinics, psychological support for patients) in clinical diagnosis and treatment, volunteer service, and other scenarios. This intrinsically driven behavioral pattern is consistent with the "giver" role theory proposed by Baumeister et al. [ 10 ] , in which the individual gains a sense of existential value through serving others and society, which in turn reinforces his or her experience of meaning in life. This study validates the basic psychological needs model of meaning in life [63] in terms of the feedback of prosocial behaviors on meaning in life. Graduate students in medical schools engage in prosocial behaviors that reinforce meaning in life through three pathways: (1) Competence fulfilment: successful interventions in clinical scenarios (e.g., first aid operations) directly increase self-efficacy [64] , creating a positive perception that "I can create value. (2) Strengthening of social connections: Trusting relationships with patients and peers alleviates feelings of isolation under academic pressure [65] and enhances professional identity through social recognition (e.g., the "healer's heart" assessment) [ 14 ] . (3) Value externalization: primary care practice enables individuals to intuitively perceive the social impact of their actions [66] , thus deepening their understanding of the medical mission [ 11 ] . For example, graduate students who participate in volunteer clinics in remote areas experience the unity of professional values and life goals by improving the health of local residents [ 12 ] . Notably, altruistic prosocial behaviors played a central pivotal role in this study. The cross-lagged network showed that altruism (e.g., empathy-based unpaid medical treatment) not only had a stabilizing predictive effect on meaning in life (β = 0.24) but also activated individuals' sense of responsibility and empathy through the "emergency situation-altruistic behavior" pathway (e.g., volunteer support in public health emergencies) [ 6 ] . This dynamic interaction reveals a special attribute of medical education: the cultivation of a sense of professional mission not only relies on knowledge transfer but also requires contextualized practices (e.g., simulated first aid, community service) to guide students to integrate altruistic values into their self-concept [ 59 ] . Findings also suggest that anonymous prosocial behaviors (e.g., anonymous donations) reflect medical students' dedication more purely by avoiding social evaluation pressure, whereas public behaviors (e.g., recognition volunteering) promote the persistence of emotional helping tendencies through social reinforcement mechanisms [67] . 4.5 Insufficient research The following limitations remain in this study. First, although this study combined cross-sectional and longitudinal research, the Meaning of Life and Prosocial Behavior measures were self-reported paper-and-pencil tests, which could be replicated in the future via experimental methods for validation. Second, despite the relatively large sample size of this study, the structure of the subjects was homogeneous, and all of the Study 2 subjects were from a particular medical school in Shanxi, which may affect the generalizability of the results; future studies could conduct multicenter follow-up studies in medical schools in multiple regions and at different levels to improve external validity. Third, longitudinal designs can effectively capture dynamic changes and provide insights into prosocial behaviors and meaning in life over time. However, it is not possible to completely rule out confounding variables, which may affect the accuracy of causal inferences. To this end, we suggest methodological improvements for future studies: on the one hand, experimental designs, such as intervention experiments, could be incorporated to explore causality more directly; on the other hand, more control variables, such as Big Five personality traits and social support, could be introduced to further purify the effects and enhance the robustness of causal inferences. This research design contributes to a more comprehensive and accurate understanding of the relationships among the study variables. Despite these limitations, the present study provides longitudinal evidence to support the interrelationship between meaning in life and prosocial behaviors, promotes an understanding of the function of meaning in life, and provides new ideas for promoting prosocial behaviors. 5 Conclusions and insights from the study The present study revealed a bidirectional facilitative relationship between prosocial behaviors and meaning in life among graduate students in medical schools. Individuals with high meaning in life are more likely to engage in prosocial behaviors, which in turn enhance individuals' meaning in life. Altruistic behavior is a core dimension of prosocial behavior and is closely related to anonymity, emotion, and the public. Prosocial behaviors showed dynamic changes over time, with helping behavior in emergency situations significantly predicting subsequent altruistic and emotional behavior. The study suggests that medical educators should focus on meaningful guidance in clinical practice and institutionalize the design of volunteer programs (e.g., linking emergency department rotations to volunteer credits and simulating emergency scenarios to strengthen the sense of responsibility in emergency situations). Combined with research findings (e.g., "emergency helping behaviour is predictive of altruistic"), targeted training modules (e.g., disaster medical rescue simulation courses) were designed to enhance medical students' sense of responsibility and meaning in life. Future studies could include multicenter follow-up designs, behavioral experiments combined with physiological indicators, and cross-cultural comparisons to further uncover the mechanisms underlying this relationship. Declarations 1.Ethics approval and consent to participate （ 1 ） Ethics Approval: This study was approved by the Institutional Review Board (IRB) of Shanxi Medical University, with approval number 2023SJL71 (Date of Approval: 2020.03.13). The research was conducted in strict accordance with the approved protocol. （ 2 ） Consent to Participate: All participants were fully informed about the study's purpose, methods, potential risks, and benefits, and they all signed written informed consent forms before participating in the study. （ 3 ） Privacy and Confidentiality: This study strictly protects the privacy and anonymity of participants. All collected data are stored and processed in an encrypted or anonymized format. Any information that could identify participants has been removed or securely stored. 2 . Consent for publication Consent to Publish Statement: All participants signed an electronic informed consent form, agreeing to the publication of data or related results from this study in open-access journals. 3 . Availability of data and materials Data availability statement: The data are available upon reasonable request. Researchers requesting data access should contact [Guo Li] via [ [email protected] ]. 4 . Competing Interests Competing Interests Statement: All the authors declare that there are no competing interests that could affect the objectivity and reliability of the results of this study. 5. Funding : The content of this article does not represent the views of any organization or institution, nor has it received any form of financial support. 6. Authors' contributions ： Guo Li was responsible for the conceptualization, data curation, and writing of the original draft, as well as software development. Niu Yangtong contributed to the software development, data curation, formal analysis, conceptualization, and visualization. Li Xinying handled the conceptualization, methodology, project administration, data curation, and resources. Li Yuting was involved in visualization, project administration, resources, conceptualization, and methodology. Tong Shiyu contributed to the conceptualization, visualization, funding acquisition, validation, and project administration. Cai Wenwei was responsible for writing the original draft, reviewing and editing, project administration, supervision, and formal analysis. Xue Zhaoxia was involved in reviewing and editing, project administration, resources, conceptualization, writing the original draft, and supervision. 7. Acknowledgements： Firstly, I would like to express my gratitude to the teachers and colleagues who provided me with help and guidance during the research process. 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Advances in Psychological Science , 26(4), p.744. doi:https://doi.org/10.3724/sp.j.1042.2018.00744. JIANG, P., HAN, L., YANG, X., DENG, G. and ZHANG, W. (2012). Research progress on strategies to improve clinical nurses’ self-efficacy. Modern Nurse , (12), pp.8–10.（doi） LIU, N., LIU, Y., MA, S. and WANG, L. (2018). Analysis on the status quo and influencing factors of loneliness among junior medical students. Journal of Lanzhou Vocational Technical College , 34(3), pp.160–162. doi:https://doi.org/10.3969/j.issn.1008-5823.2018.03.063. PENG, K., YANG, J., TANG, M. and SHANG, M. (2022). Study on the influence of social practice on the employment concept of medical students at grassroots level. China Higher Medical Educatio n, (4), pp.32–33. doi:https://doi.org/10.3969/j.issn.1002-1701.2022.04.016. WANG, W., WU, X., TIAN, Y. and ZHOU, X. (2018). The Relationship between Attachment, PTSD and PTG Among adolescents after the Wenchuan earthquake: the mediating role of perceived social support and coping. Psychological Development and Education , 34(1), pp.112–119. doi:https://doi.org/10.16187/j.cnki.issn1001-4918.2018.01.14. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7164933","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487861601,"identity":"051d0f62-2cca-4924-9f0b-239b13be7fbe","order_by":0,"name":"Niu Yangtong","email":"","orcid":"","institution":"Shanxi Medical University-Zhongdu Campus","correspondingAuthor":false,"prefix":"","firstName":"Niu","middleName":"","lastName":"Yangtong","suffix":""},{"id":487861602,"identity":"7e93c5ed-00ef-4ff4-a4a1-9f79262a33c5","order_by":1,"name":"Guo Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACNmbmgw8/VEjw8DMcPkCcFj72tmRjiTM2cpKNxxKI0yLHc0ZNgLctzdjg8BkDIh0mkcPGIHHmcGLDsTMfb7xhsJPTbSCoJffYg4KKw4mNPWc3W85hSDY2O0BQS166AciWZomz26R5GA4kbiOsJcdMgrftcGKb/JtnRGrhOQPSkmbMw3CGjUgtsECWYDhmbDnHgAi/yDdDo9L+wOGHN95U2MkR1IICJHiIjBpkLaTqGAWjYBSMghEBAEhpRRoYnEF7AAAAAElFTkSuQmCC","orcid":"","institution":"Shanxi Medical University-Zhongdu Campus","correspondingAuthor":true,"prefix":"","firstName":"Guo","middleName":"","lastName":"Li","suffix":""},{"id":487861603,"identity":"aba309ba-6410-4ea9-bcd9-c9d3c9c9ecbd","order_by":2,"name":"Li Yuting","email":"","orcid":"","institution":"Shanxi Medical University-Zhongdu Campus","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Yuting","suffix":""},{"id":487861604,"identity":"aabe8a2a-072e-418b-bcac-5146a84b38d3","order_by":3,"name":"Li Xinying","email":"","orcid":"","institution":"Shanxi Medical University-Zhongdu Campus","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Xinying","suffix":""},{"id":487861605,"identity":"5d688c66-2622-4195-89bd-01933a605406","order_by":4,"name":"Cai Wenwei","email":"","orcid":"","institution":"Shanxi Medical University-Zhongdu Campus","correspondingAuthor":false,"prefix":"","firstName":"Cai","middleName":"","lastName":"Wenwei","suffix":""},{"id":487861606,"identity":"b096bf47-4120-4796-85f9-a5ec00ec4160","order_by":5,"name":"Tong Shiyu","email":"","orcid":"","institution":"Shanxi Medical University-Zhongdu Campus","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Shiyu","suffix":""},{"id":487861607,"identity":"10d0642d-a86b-4b87-870d-f7639ef97bac","order_by":6,"name":"Xue zhaoxia","email":"","orcid":"","institution":"Shanxi Medical University-Zhongdu Campus","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"zhaoxia","suffix":""},{"id":487861608,"identity":"a1ae24ff-aae9-4326-bc35-19e99f633ff0","order_by":7,"name":"yang guane","email":"","orcid":"","institution":"Shanxi Medical University-Zhongdu Campus","correspondingAuthor":false,"prefix":"","firstName":"yang","middleName":"","lastName":"guane","suffix":""}],"badges":[],"createdAt":"2025-07-19 14:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7164933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7164933/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87478165,"identity":"66357929-6892-4762-ac8e-f4df5a12c0b8","added_by":"auto","created_at":"2025-07-24 09:24:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23524,"visible":true,"origin":"","legend":"\u003cp\u003eRegularization bias correlation network for the internal dimensions of prosocial behaviors\u003c/p\u003e\n\u003cp\u003eNote: The wider the edges are, the stronger the conditional correlation between the nodes.The blue edges indicate positive correlations,and the red edges indicate negative correlations. Pub.=Public, Anon.=Anonymous, Altru.=Altruistic, Comp.=Compliant, Emo.=Emotional, Dire.=Dire, the same applies below.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/255c03430d80be810c603950.jpg"},{"id":87478169,"identity":"0c1dde0e-b902-4b1d-8018-8b723db3df15","added_by":"auto","created_at":"2025-07-24 09:24:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33706,"visible":true,"origin":"","legend":"\u003cp\u003eProsocial behavior node strength and expected impacts\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/83a4fc5065f6e8ab79e3e5f2.jpg"},{"id":87478166,"identity":"66cb057a-5dc5-41e0-8dd7-85a7c1be16f7","added_by":"auto","created_at":"2025-07-24 09:24:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27411,"visible":true,"origin":"","legend":"\u003cp\u003eNonparametricbootstrap method to test the differences in the strength of individual nodes of the prosocial behaviorcross-sectional network\u003c/p\u003e\n\u003cp\u003eNote: The black squares in the figure indicate that the 95% confidence interval corresponding to the difference in the intensities of the two nodes derived via the bootstrap method does not contain zero, and the gray squares indicate that the 95% confidence interval corresponding to the difference in the intensities of the two nodes contains zero. The diagonal lines indicate the magnitude of the individual node intensities. (the same below)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/8506e823ad5bc2887a57aa6b.jpg"},{"id":87478829,"identity":"edde30fd-631c-4dbc-ae38-0de0f3b57b7c","added_by":"auto","created_at":"2025-07-24 09:32:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29629,"visible":true,"origin":"","legend":"\u003cp\u003eNonparametric bootstrap test for differences in expected impacts across nodes of the prosocial behaviorcross-sectional network\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/d61d22f7bedcf83fc037dcc0.jpg"},{"id":87479731,"identity":"095e3221-f57d-4c57-833d-3e4e756be007","added_by":"auto","created_at":"2025-07-24 09:40:20","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60365,"visible":true,"origin":"","legend":"\u003cp\u003eCase-drop method to test the stability test results of the intensity and expected impact in the cross-sectional network of prosocial behaviors\u003c/p\u003e\n\u003cp\u003eNote: The X-axis indicates the percentage of cases remaining after randomly discarding samples from the original sample, and the Y-axis indicates the correlation of the centrality indices between the remaining samples and the original sample. The points on the lines in the figure indicate the average correlation (from 90% to 10%) between the intensity estimated in the full sample and the intensity estimated on a random subsample that retains only a certain percentage of cases. The shaded areas indicate 95% bootstrap confidence intervals for the correlation estimates. Higher values indicate better stability of the centrality estimates. (the same below)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/83008241fffe440dea68d2c4.jpg"},{"id":87478834,"identity":"594fb725-3832-466b-a48b-a87ec2504ec6","added_by":"auto","created_at":"2025-07-24 09:32:20","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":91106,"visible":true,"origin":"","legend":"\u003cp\u003eNonparametric bootstrap method for testing borderline differences in the prosocial behavior cross-sectional network\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/0f99cad1c82e08e55d4ef5c1.jpg"},{"id":87478180,"identity":"5d8df01b-7d1f-4f56-905a-ed70f4bc37b4","added_by":"auto","created_at":"2025-07-24 09:24:20","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":51591,"visible":true,"origin":"","legend":"\u003cp\u003eCross-sectional network of prosocial behaviors at T1 and T2\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/a15aab6da16d36ee142213f9.jpg"},{"id":87478832,"identity":"707c4f37-f799-4dd4-9903-8d165c33a61a","added_by":"auto","created_at":"2025-07-24 09:32:20","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":19885,"visible":true,"origin":"","legend":"\u003cp\u003eIndividual prosocial behavior cross-lagged network(autoregressions omitted)\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/413b28e84fa5dd8f1df7047b.jpg"},{"id":87478174,"identity":"d70b07d6-31be-4f47-b6a2-d4b9a605f4a0","added_by":"auto","created_at":"2025-07-24 09:24:20","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":37667,"visible":true,"origin":"","legend":"\u003cp\u003eCross-lagged network model of prosocial behaviors and meaning of life\u003c/p\u003e\n\u003cp\u003eNote: MLQ-P=The presence of meaning, MLQ-S=The search for meaning, Pub.=Public, Annon.=Anonymous,\u003c/p\u003e\n\u003cp\u003eAltru.=Altruistic, Comp.=Compliant, Emo.=Emotional, Dire.=Dire, the same applies below.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/5cfded708f088585dad9fcc9.jpg"},{"id":87478190,"identity":"fcb49b2e-9163-44e1-8d25-7be2fc528bf6","added_by":"auto","created_at":"2025-07-24 09:24:20","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":27291,"visible":true,"origin":"","legend":"\u003cp\u003eCross-lagged networks of prosocial behaviors and meaning of life cross-cluster out predictability (left) and cross-cluster in predictability (right)\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/c78208579b4dff2949d96559.jpg"},{"id":93567831,"identity":"45fb9df5-3baa-4e3c-9e49-2314b8b28c63","added_by":"auto","created_at":"2025-10-15 08:40:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2076640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7164933/v1/9775a9ee-7415-45d8-a3e6-03dfc7267527.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Complementing each other: a topological analysis of prosocial behaviors and meaning in life among master's degree students in medical schools: based on longitudinal tracking data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the rapid development of the medical field, the requirements for medical talent have become increasingly stringent. As the future medical backbone of medical school graduate students, their professionalism and moral character have become the focus of extensive social attention \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Prosocial behavior is a key indicator of an individual's social responsibility and moral level, which is particularly important for graduate students in medical schools \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. As a noble cause to serve society and benefit mankind, the prosocial behavior of postgraduate students has a direct effect on their attitudes and behaviors in future medical work, which is profoundly important for improving the quality of medical services, constructing harmonious doctor‒patient relationships and developing healthy and harmonious societies[3]. Moreover, prosocial behavior is crucial to the psychological development of students and is closely related to positive developments such as increased self-esteem, improved quality of interpersonal relationships, and academic achievement\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that there is a positive feedback loop between prosocial behavior and well-being that promotes each other \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Therefore, exploring the prosocial behaviors of graduate students in medical schools has important personal and social value.\u003c/p\u003e\u003cp\u003eProsocial behavior, which covers a wide range of forms, such as helping, cooperating, sharing, and comforting, is all behavior that benefits others and society and often occurs in specific social situations. Carlo and Randall (2002) classified them into six types, namely, altruistic, emotional, dire, compliant, anonymous, and public, where the first five are driven mainly by internal motives, whereas public behavior is more influenced by external motives [6]. Prosocial behavior not only promotes social harmony and progress but also enhances the meaning of an individual's life and becomes the cornerstone of social and personal development. In the field of medicine, such behaviors are characteristic of the profession. For example, anonymity may be manifested in anonymous donations to needy patients, whereas helpfulness in emergency situations is common in volunteer support in public health emergencies \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In life, i.e., an individual's perception of themselves, the world around them, and their place in the world, as well as their understanding of life's purpose goals and mission, has a motivational role and influences the way an individual chooses to behave in the present \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Baumeister et al. \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e suggested that people who value the meaning of life are more inclined to care for and help others as givers, to contribute to society, and to see helping people and serving society as a process of pursuing and achieving happiness. In other words, people who possess and seek meaning in life more often engage in prosocial behaviors, and prosocial behaviors significantly enhance an individual's meaning in life. Research has shown that prosocial behaviors enhance meaning in life by increasing an individual's sense of worth and self-esteem \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. A longitudinal study by He et al. \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e further confirmed the bidirectional facilitative relationship between prosocial behaviors and meaning in life. However, the current study disagrees on the direction of the two influences, and most of the evidence is based on cross-sectional questionnaires and experiments and lacks longitudinal data support, which needs to be further explored. As the backbone of the future medical team, the professionalism and ethical quality of medical school graduate students are directly related to the quality of medical services and the doctor\u0026ndash;patient relationship. The essence of medical practice is altruism and humanistic care \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that medical students' altruistic values are significantly and positively correlated with their professional identity \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Graduate students in medical schools generally have a high sense of professional mission, and the meaning in their lives often stems from the recognition of the value of \"saving lives and helping people\" \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. When individuals regard medical practice as a way to realize meaning in life, they more actively participate in prosocial behaviors such as volunteering and unpaid clinics\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Therefore, exploring the dynamic relationship between their prosocial behaviors and meaning in life is crucial to cultivating socially responsible medical professionals. Although studies have revealed the correlation between prosocial behaviors and meaning in life through cross-sectional data \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, few longitudinal data have been validated. In addition, prosocial behaviors in medical education are context dependent (e.g., public clinic vs. anonymous assistance), and traditional analytical methods have difficulty capturing such behavior through dimensionally structured interactions. In view of the current findings and theoretical support, this study concludes that there is a mutually reinforcing relationship between prosocial behaviors and having meaning in life. Graduate students' self-awareness develops rapidly, their outlook on life and values is gradually established \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, and individuals think more deeply about the world around them and their own goals and values in graduate school than in secondary school \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, which is a critical period for the establishment of life pursuits. Therefore, this study combines a cross-sectional study with a tracer study aimed at determining the interrelationship between meaning in life and prosocial behaviors.\u003c/p\u003e\u003cp\u003eNetwork analysis is a new approach to describing multivariate dependency structures that extends traditional regression methods by being able to quantify and visualize the extent to which these correlated factors are interrelated \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Most previous network analyses have used cross-sectional data to construct undirected networks, but undirected cross-sectional networks provide only limited insights into the temporal and causal relationships estimated by the network model because of their inability to capture temporal variations as well as to discern the direction of the relationships \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is essential to use cohort data to construct temporal and causal relationships between variables. By quantifying the conditional dependencies between variables \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, network analysis can directly resolve the independent associations of the dimensions of prosocial behaviors (e.g., altruistic, dire) and avoid the assumption bias of latent variable models. In addition, the cross-lagged network analysis method was developed by using network analysis in cohort data to simultaneously examine all the complex relationships between the hypothesized variables in the network model, taking into account the temporal nature of the correlation effects \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Network analysis is characterized by the fact that the structure of high-dimensional data can still be effectively explored when there is no a priori theory about how the variables are related \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Cross-lagged network analysis can simultaneously test the temporal effect between variables and the predictive path between clusters \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, providing a new perspective on the dynamic interaction of \"context-behavior-sense of meaning\" in medical education. In empirical psychology research, network analysis has the following significant advantages: first, network analysis does not rely on the definition of latent variables but builds a model based on observed variables, thus realizing the direct analysis of the relationship between the observed variables; second, the network analysis technique can analyse the \"independent\" relationship between the two variables in the complete system, reducing the confusion caused by the \"false correlation\" in traditional analysis. Second, the network analysis technique can resolve the \"independent\" relationship between two variables in the complete system, reducing the confusion caused by the \"false correlation\" in traditional analysis. Third, network analysis integrates all the observed variables into a unified network framework, which can be used to examine the occurrence and development of a certain psychological or behavioral system from the perspective of the overall change in the network \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eStudy 1 used convenience sampling and a combination of online and offline sampling methods to extract medical school-enrolled graduate students from 2007, following the principle of voluntarism. The subjects seriously answered the questions after review and approval of the subject fee, which excluded those who did not provide serious answers to the regular responses of 64 people, the effective subjects of 1963 people, and the questionnaire recovery rate of 97.81%.\u003c/p\u003e\u003cp\u003eIn Study 2, the whole group sampling method was used to select 2 classes in a medical school in Shanxi Province and administer the test twice at an interval of 3 months. In the process of response, the subjects followed the voluntary principle. The first measurement (T1) was carried out at the end of October 2024 and was administered to a total of 726 postgraduate medical students in two classes by group administration. Because 15 questionnaires had omissions and chaotic answers, the number of valid questionnaires for the first measurement was 711. There were 202 male and 509 female students, with a mean age of 23.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94 years. The second questionnaire was administered to the same group of people at the end of January of the following year, and to avoid practice effects, all the questions were randomly assigned in this administration. Finally, 711 valid questionnaires of the first time and 697 valid questionnaires of the second time were matched one by one, and finally, 671 questionnaires valid for both measurements were collected. Among them, 193 were male and 482 were female, with a mean age of 23.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45 years. A total of 37 subjects were lost in the two measurements, for an attrition rate of 5.20%. The gender of the attrition subjects and those who took the test on both occasions were tested for differences: the difference in the gender distribution was not significant, χ\u0026thinsp;=\u0026thinsp;0.67, p\u0026thinsp;=\u0026thinsp;0.415. The results of the characterization of the attrition subjects, the results of the sensitivity analysis, and other results revealed (p\u0026thinsp;\u0026gt;\u0026thinsp;0.415) that the difference was not significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measurement tools\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Measurement of prosocial behaviors\u003c/h2\u003e\u003cp\u003eThe Prosocial Tendencies Scale developed by Carlo and revised by Kou Yu et al. [27] was used. The scale has 26 items divided into six dimensions: public, anonymous, altruistic, compliant, emotional, and dire. The scale is scored on a 5-point Likert scale, with higher total scores indicating more prosocial behaviors. The Cronbach's alpha coefficients of the scale were 0.937 and 0.932 for the pre- and postintervention measurements, respectively, in this study, indicating high internal consistency reliability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Meaning-in-life scale\u003c/h2\u003e\u003cp\u003eThe Chinese version of the Meaning in Life Questionnaire (MLQ) was revised twice \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e to obtain the same questionnaire structure of 10 entries with a 2-dimensional structure (presence of meaning in life and searching for meaning in life) as Steger. The Cronbach's alpha coefficients for this scale were 0.840 and 0.863 for the pre- and postintervention measurements, respectively, in this study, indicating high internal consistency reliability.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data analysis\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Network analysis\u003c/h2\u003e\u003cp\u003eAll descriptive statistical analyses in this study were performed via SPSS (version 22.0 for Windows) and reported as the number of cases, means, and standard deviations. Network estimation was performed via R (version 4.2.1), and a T1\u0026rarr;T2 cross-lagged network was constructed to explore the predictive path of prosocial behaviors after 3 months. Regularization of the partial correlation network was applied to the R software to identify indicators of node centrality and predictability, which helped to identify key intervention targets \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. For the first analysis, data fitting and item network construction were performed via the Gaussian graphical model (GGM) \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The GGM is an undirected network, where nodes represent observed variables and the line between two nodes represents their partial correlation \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Nonsupplementary transformation of the data was performed via a large software package \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e to account for the assumption of a multivariate normal distribution in the GGM. The use of the least absolute shrinkage and selection operator (LASSO) and the extended Bayesian information criterion (EBIC) have been used to refine the network edges and tune the parameters to enhance interpretability \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e by normalizing the network by shrinking very small correlations down to zero, through which biased correlations between variables are quantified and the core pivot of the multidimensional results is revealed. Following the recommendations of Epskamp et al. \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, we evaluated edge accuracy via the bootstrapped method. Centrality indices indicate how well a node is connected to the rest of the network to determine the importance of each variable \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e and may indicate influential initial treatment targets \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Next, to quantify the importance of each node in the transected network, we used strength and expected impact as centrality indices \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Strength is calculated by summing the absolute value of the weights of all edges connected to a node, with higher values of strength indicating greater influence in the network \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. The expected influence, on the other hand, takes into account both positive and negative relationships within the network on the basis of strength and can provide a more comprehensive assessment of influence on the network as a whole \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. To determine confidence intervals (CIs) for the stability of the strength and centrality measures for each edge, we computed 10,000 bootstrapped networks. The bridge expected influence for each node was calculated via the mgm package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Network Comparison\u003c/h2\u003e\u003cp\u003eThe purpose of network comparison is to uncover the structural differences between different networks via a permutation test, which is implemented via the NCT function within the Network Comparison Test package in R software. In this study, the consistency of the network structure at different points in time is systematically examined via tests that include global and local invariance \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. These tests are related to each other and assess network stability and change at different levels. First, the network invariance test serves as an assessment tool for the overall structure and aims to compare the global topological characteristics of networks at different points in time, with the null hypothesis that all corresponding edges are equal in both networks. In contrast, the global intensity invariance test focuses more on the network overall level of connectivity and explores the stability of the network's overall connectivity strength by evaluating the sum of all edge weights or the average of node strengths in the network. By integrating these 2 tests, it is possible to assess changes in prosocial behaviors across time, both holistically and locally.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Cross-Lagged Network Analysis\u003c/h2\u003e\u003cp\u003eThe cross-lagged network model estimates the autoregressive and cross-lagged coefficients over time through a series of regularization regressions. In particular, the autoregressive coefficients reflect the predictive effect of a variable's state in the previous measurement on its state in the next measurement, i.e., the continuity of the variable itself over time, whereas the cross-lagged coefficients indicate the predictive effect of a variable's state in the previous measurement on the state of the other variable in the next measurement, i.e., the interaction of the variables \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. This study uses the glmnet package in R \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e to estimate the cross-lagged network model of prosocial behaviors at two time points and the cross-lagged network model of prosocial behaviors and meaning in life. To enhance the interpretability of the results and create a more intuitive network structure, this study determined the optimal value of the tuning parameter γ through 10-fold cross-validation and applied Graphical Lasso (Glasso) to the estimated regression coefficients to shrink the insignificant paths to zero \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. There are more negative edges in the cross-lagged network model, so this study used the expected influence and out-of-expected influence as the centrality indices for the cross-lagged network model. In cross-lagged networks, In-Predictivity is the percentage of the degree to which the variation of a node at a given measurement time point is explained by all nodes at the previous time point; Out-Predictivity is the percentage of the degree to which the variation of all nodes at a given measurement time point is explained by a node at the previous measurement time point. The in-predictability and out-predictability indicate the extent to which each node is predicted by other nodes and the extent to which it predicts other nodes in the network, respectively \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. On this basis, considering the binary properties of prosocial behaviors and meaning in life networks, this study calculates cross-cluster metrics, including cross-cluster in-predictability and cross-cluster out-predictability (i.e., the extent to which the variance of a node on T2 is explained by all the nodes of T1 in another cluster and the extent to which a given T1 node accounts for the variance of all the T2 nodes in the other clusters) to the extent to which nodes in different clusters are predictive of each other \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. degree of differentiation \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Higher cross-cluster in-prediction indicates that a node is more influenced by all nodes in the out-cluster at the previous point in time: higher cross-cluster out-prediction indicates that a node is more influenced by all nodes in the out-cluster at the later point in time \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4 Visualization and stability assessment\u003c/h2\u003e\u003cp\u003eVisualization of both the cross-sectional network and cross-lagged network is implemented through the qgraph package (version 1.9.5) \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The positions of all nodes in the network are determined by the Fruchterman-Reingold algorithm, which places more strongly connected nodes closer to each other \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. The edges in the network represent the partial correlation coefficients between two nodes after controlling for the effects of other variables; the nodes represent the variables, whereas the thickness and color of the edges represent the degree of correlation and the potency, respectively \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. In the visualization network, blue edges indicate positive correlations, and red edges indicate negative correlations. Thicker edges indicate stronger correlations between nodes. A circle around the outside of a node indicates the predictability of that node, with the closer to complete filling representing a higher rate of explanation of the node to neighboring nodes \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Autoregressive paths in the cross-lagged network are omitted for a clear presentation of the network graph. The accuracy of the margin estimates was tested via the bootstrap method with 1000 iterations of the network to plot 95% nonparametric bootstrap confidence intervals for each margin \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. The robustness of all centrality indices (node strength, node expected impact, out expected impact, and person expected impact) was tested via the case-droping method with correlation stability coefficients (CS coefficients) as the results [24]. The CS coefficient (cor\u0026thinsp;=\u0026thinsp;0.7) indicates the percentage of samples for which the correlation between the centrality indicator of the bootstrap sample and the centrality indices of the original sample can be maintained, with a correlation of at least 0.7 within a 95% confidence interval obtained via the case-drop method. A CS coefficient between 0.25 and 0.50 indicates that the centrality indices are robust, and a CS coefficient greater than 0.5 indicates strong robustness \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The above network construction and visualization were implemented through the qgraph package and bootnet, which were used to calculate the correlation stability coefficient (CS).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3 Results of the study","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Common method bias test\u003c/h2\u003e\n \u003cp\u003eIn this study, common method bias was reduced mainly through procedural control and statistical control. First, the purpose of the study was stated before administering the test, and all the subjects completed the code to reduce their likelihood of bias; second, the common method bias test was conducted via Harman\u0026apos;s one-factor method to conduct exploratory factor analysis of the items for all the variables [45]. The first measurement had 14 factors with eigenvalues greater than 1 in the unrotated case, and the variance explained by the first factor was 23.98%, which is less than the 40% criterion for the critical value. The second measurement had 13 factors with eigenvalues greater than 1 in the unrotated case, and the variance explained by the first factor was 23.92%, which is less than the 40% criterion for the critical value, suggesting that the two measurements of the present study do not have serious common methodological bias.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Descriptive statistics\u003c/h2\u003e\n \u003cp\u003eThe means and standard deviations of the dimensions of the Meaning in Life Scale and the Prosocial Behavior Scale are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The skewness (-0.59 to 1.31) and kurtosis (-0.63 to 0.90) of all the items indicate that the data are basically normally distributed\u0026nbsp;\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, which satisfies the conditions for the network analysis conducted in Study II.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of the dimensions of meaning in life and prosocial behaviors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;1943\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;671(T1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;671(T2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.87\u0026thinsp;\u0026plusmn;\u0026thinsp;5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.76\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eNote: 1\u0026thinsp;=\u0026thinsp;The search for meaning, 2\u0026thinsp;=\u0026thinsp;The presence of meaning, 3\u0026thinsp;=\u0026thinsp;Public, 4\u0026thinsp;=\u0026thinsp;Anonymous, 5\u0026thinsp;=\u0026thinsp;Altruistic, 6\u0026thinsp;=\u0026thinsp;Compliant, 7\u0026thinsp;=\u0026thinsp;Emotional, 8\u0026thinsp;=\u0026thinsp;Dire.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Correlation analysis of variables at each time point\u003c/h2\u003e\n \u003cp\u003eCorrelation analysis was conducted for each variable under the two time points of T1 and T2. The results revealed that the dimensions of the Meaning in Life Scale at T1 were significantly correlated with the dimensions of the Prosocial Behavior Scale at T1; the dimensions of the Meaning in Life Scale at T2 were significantly correlated with the dimensions of the Prosocial Behavior Scale at T2. For specific details, please refer to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation analysis of meaning in life and prosocial behaviors (N\u0026thinsp;=\u0026thinsp;671)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNote: **, indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and all of the above variables are at less than 0.05.1\u0026thinsp;=\u0026thinsp;the presence of meaning T1, 2\u0026thinsp;=\u0026thinsp;The search for meaning T1, 3\u0026thinsp;=\u0026thinsp;Public T1, 4\u0026thinsp;=\u0026thinsp;Anonymous T1, 5\u0026thinsp;=\u0026thinsp;Altruistic T1, 6\u0026thinsp;=\u0026thinsp;Compliant T1, 7\u0026thinsp;=\u0026thinsp;Emotional T1, 8\u0026thinsp;=\u0026thinsp;Dire T1, 9\u0026thinsp;=\u0026thinsp;The presence of meaning T2, 10\u0026thinsp;=\u0026thinsp;The search for meaning T2, 11\u0026thinsp;=\u0026thinsp;Public T2, 12\u0026thinsp;=\u0026thinsp;Anonymous T2, 13\u0026thinsp;=\u0026thinsp;Altruistic T2, 14\u0026thinsp;=\u0026thinsp;Compliant T2, 15\u0026thinsp;=\u0026thinsp;Emotional T2, 16\u0026thinsp;=\u0026thinsp;Dire T2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Cross-sectional network of the prosocial behaviors of medical school graduate students\u003c/h2\u003e\n \u003cp\u003eThe transversal network of the dimensions of postgraduate students\u0026rsquo; prosocial behaviors in medical schools is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In this network, nodes represent dimensions of prosocial behaviors, and edges represent Regularization biocorrelations between dimensions. Six nodes connected by 15 edges form a network of dimensions of postgraduate students of medical schools\u0026rsquo; prosocial behavior, with a network density of 1. The weights of the edges range from \u0026minus;\u0026thinsp;0.05\u0026ndash;0.53 (from Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), with an average weight of 0.17. The coefficients of the edges for the transverse network of prosocial behaviors are presented in the network version of Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The coefficients of the edges of the transverse network of prosocial behaviors are shown in the network version of Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. In the network of dimensions of prosocial behaviors, \u0026quot;altruistic\u0026quot; (intensity\u0026thinsp;=\u0026thinsp;2.8) is the highest-intensity node, and its intensity is significantly higher than that of the other nodes, indicating that \u0026quot;altruistic\u0026quot; behavior has the strongest influence on the other nodes in the network.. \u0026quot;altruistic\u0026quot; (expected influence\u0026thinsp;=\u0026thinsp;0.64) and \u0026quot;emotional\u0026quot; (expected influence\u0026thinsp;=\u0026thinsp;0.60) are the two nodes with the highest expected influence, and their expected influence is significantly greater than that of the other nodes, indicating that they are the most influential nodes on prosocial behavior as a whole. These findings suggest that they are the two nodes with the strongest influence on the overall structure of prosocial behaviors (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The CS coefficient for both the intensity and the expected influence is 0.75, indicating good stability of the indicator (see Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e for details). The most highly weighted edges in the network are \u0026quot;anonymous-altruistic\u0026quot; (r\u0026thinsp;=\u0026thinsp;0.53) and \u0026quot;public-emotional\u0026quot; (r\u0026thinsp;=\u0026thinsp;0.29), which are significantly stronger than the remaining edges in the network (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTable of Marginal Coefficients of Cross-Sectional Network for prosocial behaviors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrom\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Cross-lagged networks of postgraduate students\u0026rsquo; prosocial behaviors in medical schools\u003c/h2\u003e\n \u003cp\u003eThe results of the network comparisons revealed that the difference in overall strength between the T1 network (overall strength\u0026thinsp;=\u0026thinsp;2.5) and the T2 network (overall strength\u0026thinsp;=\u0026thinsp;2.5) was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.937), and network invariance testing passed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.001) (see Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, and the borderline coefficients are shown in Tables \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The networks at the two time points did not show significant differences in overall strength, indicating that the overall strength of network connections remained relatively stable. The results of network invariance testing indicated that the overall topology of the prosocial behavior network also did not change significantly between the two time points.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCoefficients for each side of the prosocial behaviors cross-sectional network at time point T1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrom\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCoefficients for each borderline of the prosocial behaviors cross-sectional network at time point T2\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrom\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe cross-lagged network of the prosocial behaviors of medical school graduate students is shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, in which individual nodes represent a dimension of prosocial behaviors and edges are regularized regression coefficients between dimensions. Six nodes are connected by 30 directed edges to form an intertemporal cross-lagged network model of individual prosocial behaviors. The weights of the edges ranged from \u0026minus;\u0026thinsp;0.11\u0026ndash;0.24, with an average weight of 0.06 (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e in the web version for details of the edge coefficients).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTable of borderline coefficients for the Cross-Lagged Network of graduate students of medical schools prosocial behaviors (autoregressions omitted)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Cross-lagged networks of prosocial behaviors and meaning in life among medical school graduate students\u003c/h2\u003e\n \u003cp\u003eThe cross-lagged network of postgraduate students\u0026rsquo; prosocial behaviors and meaning in life is shown in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, where 8 nodes connected by 56 directed edges form the cross-lagged network model of postgraduate students\u0026rsquo; prosocial behaviors across time. The weights of the edges ranged from \u0026minus;\u0026thinsp;0.18\u0026ndash;0.34, with an average weight of 0.22 (see Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e for details of the edge coefficients).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTable of borderline coefficients for the Cross-Lagged Networks of prosocial behaviors and meaning in life\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMLQS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMLQP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLQS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePub.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnon.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAltru.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDire.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eConsidering that the present study is concerned with the predictive role of graduate students\u0026rsquo; meaning in life and prosocial behaviors, cross-cluster in-predictivity and cross-cluster out-predictivity were specifically computed with the bridge borderline. Cross-cluster in-predictability and cross-cluster out-predictability can reveal the extent to which individual nodes are predicted by out-cluster nodes as well as the extent to which they are predicted by a node in the out-cluster. Bridge edges are edges formed by connections between nodes located in two different clusters. The cross-lagged network analysis revealed that \u0026quot;the search for meaning\u0026quot; (cross-cluster predictability\u0026thinsp;=\u0026thinsp;0.06) was the most predictive node in the meaning-of-life cluster, indicating that \u0026quot;the search for meaning\u0026quot; was the most predictive node in the prosocial behavior cluster. This indicates that \u0026quot;The search for meaning in life\u0026quot; is the most predictive node in the meaning in life cluster for nodes within the prosocial behaviors cluster. The performance of the variables also varied in terms of cross-cluster in-predictability. Among them, urgency likewise showed high predictability\u0026quot; (cross-cluster in-predictability\u0026thinsp;=\u0026thinsp;0.06). The cross-cluster inpredictability of the other variables was generally low, with no particularly prominent nodes, so we did not analyse them in depth (see Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study examines the interrelationship between meaning of life and prosocial behaviors by combining cross-lagged network analysis of cross-sectional measurements and tracking data and analyses the specific relationship between meaning in life and six different types of prosocial behaviors. The content of this study is organized on the following three levels. First, at the \"one-dimensional\" level, cross-sectional network analysis is used to explore the core characteristics of the prosocial behaviors of postgraduate medical students and to analyse the key components of their prosocial behaviors to provide a more comprehensive and in-depth perspective for understanding their performance in prosocial behaviors. Second, at the \"two-dimensional\" level, the evolution of the internal structure of the prosocial behaviors of graduate students in medical schools over time is deeply investigated through network comparisons to better reveal the dynamics of the prosocial behaviors of graduate students in medical schools. Finally, at the \"three-dimensional\" level, a cross-lagged binary network model is constructed to explore the core nodes and key pathways that shape the influence of prosocial behaviors on external variables. The main findings of the study are summarized and discussed below.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Core components of the prosocial behaviors of graduate students in medical schools\u003c/h2\u003e\u003cp\u003eThe results of the cross-sectional network analysis indicated that \"altruistic\" nodes had the highest intensity. As a key component of prosocial behaviors, altruistic behavior occupies a central position in the prosocial behavior of postgraduate students in medical schools, and its high-intensity node status highlights their positive performance and significance in terms of values, behavioral orientation, educational effectiveness, future career development, and social adaptability, which is in line with the results of a series of previous studies; that is, altruistic behavior is closely related to prosocial behavior \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. An altruistic disposition essentially aims to focus on and promote the well-being of others on the basis of transcending self-interest \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e, and an altruistic disposition, as a core dimension of the prosocial behaviors of postgraduate students in medical schools, aims to cultivate medical professionals with a high degree of social responsibility, professional ethics, and the ability to achieve interpersonal harmony in line with the social norms of the collectivist culture in China. As can be visualized from the cross-sectional network diagram, altruism is strongly linked to anonymity, emotion, and the public. This suggests that altruism is not only an intrinsic motivator for prosocial behaviors but also a key factor in shaping an individual's behavioral performance in a given social situation \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. However, the negative connection between the \"altruistic\" and the \"public\" reveals a complex and important relationship: altruism, as an important driver of prosocial behaviors among graduate students in medical schools, plays a positive role in facilitating their ability to help others and care for the community. The difference in the performance of anonymous versus public prosocial behaviors may be closely related to the collectivist values of traditional Chinese culture. According to Hofstede's theory of cultural dimensions, Chinese culture is characterized by a high degree of collectivism, emphasizing group harmony and social norms rather than individual performance \u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. This cultural context may reinforce the behavioral tendency of \"doing good without leaving a name\": anonymous prosocial behaviors (e.g., anonymous donations) are more likely to be adopted by individuals because they conform to the social expectation of collectivism of \"doing good in a low profile\" \u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. In contrast, public behaviors (e.g., recognition of volunteering) may be limited by triggering social pressure in face culture. Facial culture emphasizes the need for individuals to avoid showing off or incurring jealousy in public \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e, which may lead medical students to weigh the social evaluation of their behaviors (e.g., whether they are perceived as \"putting on a show\") more cautiously in public situations, thereby inhibiting the outwards display of prosocial behaviors \u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. In addition, the Confucian norm of \"restoring one's self to propriety\"\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e further encourages individuals to practice morality in an introverted manner rather than pursuing social praise. Such behavioral differences in cultural contexts reflect the complexity of altruistic prosocial behaviors in different contexts and suggest that we need to consider cultural factors and contextual characteristics when understanding and encouraging prosocial behaviors. Future research could incorporate cross-cultural comparisons (e.g., comparing medical students in individualistic cultures) to test the cultural generalizability of the difference in anonymous versus public behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Key internal mechanisms of the prosocial behaviors of graduate students in medical schools\u003c/h2\u003e\u003cp\u003eIn the prosocial behavior network, the most highly weighted edges are \"anonymous\u0026ndash;altruistic\" and \"public\u0026ndash;emotional\", and these two edges are significantly stronger than the remaining edges in the network. This not only indicates the strong correlation between these dimensions but also reflects their key role in the overall prosocial behavior network framework. The stronger connection of \"anonymous\u0026ndash;altruistic\" suggests that individuals' altruistic tendencies are more likely to be triggered in anonymous situations and thus more willing to engage in prosocial behaviors. At the same time, anonymity also generates interconnections through dire and altruistic actions (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Prosocial behavior theory provides an explanation of this process. Specifically, prosocial behaviors refer to behaviors in which individuals help others without expecting a reward. This behavior can be direct, such as donating money or goods, or indirect, such as working for a social cause or participating in volunteer activities \u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Anonymity may be more reflective of the selfless qualities of graduate students in medical schools while also reducing social pressures and expectations for individuals to express altruistic behaviors more freely. Anonymous individuals play an important facilitating role in prosocial behaviors by providing an environment free from external interference for graduate students in medical schools, which makes them more able to freely display altruistically and thus more revealing of their noble spirit of selflessness and dedication. On the other hand, the public may increase an individual's social visibility, thereby affecting the expression of emotional and prosocial behaviors. The strength of the \"public\u0026ndash;emotional\" dimension suggests that both dimensions play a pivotal role in the pro-social behavior system, possibly shaping behavioral patterns through the interaction of emotional drive and social reinforcement. The strong association between the public and emotions in prosocial behaviors reflects the \"social self-efficacy\" epiphenomenon of social cognitive theory. According to self-presentation theory, in the public, an individual's prosocial behaviors are reinforced by social evaluations (e.g., receiving praise or recognition) \u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. This external feedback can increase the positive value of emotional arousal, forming a cycle of \"emotion-driven behavior\u0026rarr;social reinforcement\u0026rarr;emotional satisfaction\". For example, public donations not only satisfy an individual's need for empathy (emotional) but also reinforce his or her self-concept through social recognition (e.g., being perceived as a \"generous person\") \u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e, thus reinforcing the emotion‒behavior link. Emotion motivates helpers to help when they are emotionally aroused, whereas public helpers amplify emotional efficacy through social reinforcement, forming a cycle of emotion-driven behavior\u0026rarr;social reinforcement\u0026rarr; emotional satisfaction. Thus, the prominent strength of the \"public-emotional\" border in prosocial behaviors reflects the dynamic synergy between emotion and social context, both as a result of emotional responses and as a product of social context shaping.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Stability of the prosocial behaviors of graduate students in medical schools over time\u003c/h2\u003e\u003cp\u003eComparative analyses of the cross-sectional networks revealed that the overall strength of prosocial behaviors was stable in the time dimension, but the centrality of its internal components as well as the strength of some of its margins changed over time. This result is consistent with the process, stage, and complexity of the prosocial behaviors of medical school graduate students mentioned in the literature \u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e, which emphasizes that individuals' prosocial behaviors in their living environments are not static but rather are influenced by a variety of factors and may change dynamically over time. In addition, the dynamic changes within the prosocial behavior network reveal the plasticity of prosocial behaviors. This plasticity is reflected not only in the individual differences among medical school graduate students but also in the multidimensional and multilevel characteristics of the prosocial behavior process. In other words, the centrality of the components of prosocial behaviors and the links between them may play different roles at different time stages. Therefore, the dynamic changes in prosocial behaviors in the time dimension also provide a theoretical basis for further exploration of the intertemporal directed network relationships among its internal components. The cross-lagged network revealed that \"altruistic\" (T1\u0026rarr;T2 autoregressive coefficient\u0026thinsp;=\u0026thinsp;0.24) and \"emotional\" (T1\u0026rarr;T2 autoregressive coefficient\u0026thinsp;=\u0026thinsp;0.16) exhibited strong continuity in the time dimension (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). According to self-determination theory (SDT) \u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e, the stability of altruism as intrinsically motivation-driven behavior (e.g., uncompensated consultations based on a sense of professional mission) stems from the deep internalization of the values of the medical profession by graduate students in medical schools. Emotion (e.g., empathy-driven helping behavior) is maintained through the \"social reinforcement cycle\" of emotion-as-social information theory (EASI) \u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e: emotional arousal at T1 (e.g., empathy triggered by the patient's distress) motivates individuals to engage in prosocial behaviors, whereas social feedback at T2 (e.g., patient gratitude or peer recognition) further reinforces the emotional‒behavioral link \u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. Although the overall network structure was stable, the strength of some edges (e.g., \"public-emotional\") increased slightly between T1 (r\u0026thinsp;=\u0026thinsp;0.29) and T2 (r\u0026thinsp;=\u0026thinsp;0.32) (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting that this connection may be influenced by contextual reinforcement effects. According to self-presentation theory [56], public prosocial behaviors (e.g., clinical activities) amplify the positive value of emotional experiences through social evaluations (e.g., public praise), resulting in a cycle of \"emotion-driven\u0026rarr;behavioral outwards appearance\u0026rarr;social reinforcement\" \u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. For example, public helping behaviors at T1 (e.g., community health outreach) may enhance an individual's emotional efficacy through social recognition at T2 (e.g., being labelled a \"healer\"), which in turn promotes subsequent behaviors\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The cross-lagged network analyses further revealed that \"dire\" (e.g., helping behaviors in medical emergencies) was a significant predictor of altruism (β\u0026thinsp;=\u0026thinsp;0.24) and emotionality (β\u0026thinsp;=\u0026thinsp;0.16) at T1 and T2 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This result is consistent with the contingency model of prosocial behaviors \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, in which emergency situations promote immediate helping behaviors by activating individuals' perceptions of responsibility and empathic responses and may enhance long-term altruistic tendencies through the accumulation of \"feelings of competence\" (e.g., successful handling of an emergency) \u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. For example, emergency rescue experiences at T1 may increase medical students' self-efficacy to be more proactive in engaging in high-risk volunteerism (e.g., disaster medical support) at T2. The findings of this study provide an important basis for medical education, such as contextual design strategies. The reinforcement of the \"public-emotional\" connection suggests that creating socially visible practice scenarios (e.g., public clinics, honor recognition systems) can be an effective way to use social feedback mechanisms to promote the persistence of prosocial behaviors \u003csup\u003e[ 57]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Relationships between prosocial behaviors and meaning in life among graduate students in medical schools\u003c/h2\u003e\u003cp\u003eThe present study revealed a significant bidirectional facilitative relationship between meaning in life and prosocial behaviors among medical school graduate students, a result that is highly compatible with the theoretical framework and established research. Individuals' pursuit of meaning in life promotes prosocial behaviors; specifically, individuals may increase their prosocial behaviors to receive psychological approval rather than extrinsic rewards. From the perspective of motivational mechanisms, meaning in life serves as an intrinsic driving force that prompts medical students to internalize their sense of professional mission as a core motivation for helping behaviors through autonomy need satisfaction in self-determination theory \u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. Specifically, individuals with greater meaning in life are more inclined to regard medical practice as an important way to realize the value of life \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e and thus actively practice altruistic prosocial behaviors (e.g., unpaid volunteer clinics, psychological support for patients) in clinical diagnosis and treatment, volunteer service, and other scenarios. This intrinsically driven behavioral pattern is consistent with the \"giver\" role theory proposed by Baumeister et al. \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, in which the individual gains a sense of existential value through serving others and society, which in turn reinforces his or her experience of meaning in life. This study validates the basic psychological needs model of meaning in life \u003csup\u003e[63]\u003c/sup\u003e in terms of the feedback of prosocial behaviors on meaning in life. Graduate students in medical schools engage in prosocial behaviors that reinforce meaning in life through three pathways: (1) Competence fulfilment: successful interventions in clinical scenarios (e.g., first aid operations) directly increase self-efficacy \u003csup\u003e[64]\u003c/sup\u003e, creating a positive perception that \"I can create value. (2) Strengthening of social connections: Trusting relationships with patients and peers alleviates feelings of isolation under academic pressure \u003csup\u003e[65]\u003c/sup\u003e and enhances professional identity through social recognition (e.g., the \"healer's heart\" assessment) \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. (3) Value externalization: primary care practice enables individuals to intuitively perceive the social impact of their actions \u003csup\u003e[66]\u003c/sup\u003e, thus deepening their understanding of the medical mission \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. For example, graduate students who participate in volunteer clinics in remote areas experience the unity of professional values and life goals by improving the health of local residents \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Notably, altruistic prosocial behaviors played a central pivotal role in this study. The cross-lagged network showed that altruism (e.g., empathy-based unpaid medical treatment) not only had a stabilizing predictive effect on meaning in life (β\u0026thinsp;=\u0026thinsp;0.24) but also activated individuals' sense of responsibility and empathy through the \"emergency situation-altruistic behavior\" pathway (e.g., volunteer support in public health emergencies) \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. This dynamic interaction reveals a special attribute of medical education: the cultivation of a sense of professional mission not only relies on knowledge transfer but also requires contextualized practices (e.g., simulated first aid, community service) to guide students to integrate altruistic values into their self-concept \u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. Findings also suggest that anonymous prosocial behaviors (e.g., anonymous donations) reflect medical students' dedication more purely by avoiding social evaluation pressure, whereas public behaviors (e.g., recognition volunteering) promote the persistence of emotional helping tendencies through social reinforcement mechanisms \u003csup\u003e[67]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Insufficient research\u003c/h2\u003e\u003cp\u003eThe following limitations remain in this study. First, although this study combined cross-sectional and longitudinal research, the Meaning of Life and Prosocial Behavior measures were self-reported paper-and-pencil tests, which could be replicated in the future via experimental methods for validation. Second, despite the relatively large sample size of this study, the structure of the subjects was homogeneous, and all of the Study 2 subjects were from a particular medical school in Shanxi, which may affect the generalizability of the results; future studies could conduct multicenter follow-up studies in medical schools in multiple regions and at different levels to improve external validity. Third, longitudinal designs can effectively capture dynamic changes and provide insights into prosocial behaviors and meaning in life over time. However, it is not possible to completely rule out confounding variables, which may affect the accuracy of causal inferences. To this end, we suggest methodological improvements for future studies: on the one hand, experimental designs, such as intervention experiments, could be incorporated to explore causality more directly; on the other hand, more control variables, such as Big Five personality traits and social support, could be introduced to further purify the effects and enhance the robustness of causal inferences. This research design contributes to a more comprehensive and accurate understanding of the relationships among the study variables. Despite these limitations, the present study provides longitudinal evidence to support the interrelationship between meaning in life and prosocial behaviors, promotes an understanding of the function of meaning in life, and provides new ideas for promoting prosocial behaviors.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusions and insights from the study","content":"\u003cp\u003eThe present study revealed a bidirectional facilitative relationship between prosocial behaviors and meaning in life among graduate students in medical schools. Individuals with high meaning in life are more likely to engage in prosocial behaviors, which in turn enhance individuals' meaning in life. Altruistic behavior is a core dimension of prosocial behavior and is closely related to anonymity, emotion, and the public. Prosocial behaviors showed dynamic changes over time, with helping behavior in emergency situations significantly predicting subsequent altruistic and emotional behavior. The study suggests that medical educators should focus on meaningful guidance in clinical practice and institutionalize the design of volunteer programs (e.g., linking emergency department rotations to volunteer credits and simulating emergency scenarios to strengthen the sense of responsibility in emergency situations). Combined with research findings (e.g., \"emergency helping behaviour is predictive of altruistic\"), targeted training modules (e.g., disaster medical rescue simulation courses) were designed to enhance medical students' sense of responsibility and meaning in life. Future studies could include multicenter follow-up designs, behavioral experiments combined with physiological indicators, and cross-cultural comparisons to further uncover the mechanisms underlying this relationship.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e1.Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003cstrong\u003eEthics Approval:\u003c/strong\u003e This study was approved by the Institutional Review Board (IRB) of Shanxi Medical University, with approval number 2023SJL71 (Date of Approval: 2020.03.13). The research was conducted in strict accordance with the approved protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e All participants were fully informed about the study\u0026apos;s purpose, methods, potential risks, and benefits, and they all signed written informed consent forms before participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003cstrong\u003ePrivacy and Confidentiality:\u003c/strong\u003e This study strictly protects the privacy and anonymity of participants. All collected data are stored and processed in an encrypted or anonymized format. Any information that could identify participants has been removed or securely stored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e. Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to Publish Statement: All participants signed an electronic informed consent form, agreeing to the publication of data or related results from this study in open-access journals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData availability statement: The data are available upon reasonable request. Researchers requesting data access should contact [Guo Li] via [[email protected]].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e. Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting Interests Statement: All the authors declare that there are no competing interests that could affect the objectivity and reliability of the results of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Funding\u003c/strong\u003e: The content of this article does not represent the views of any organization or institution, nor has it received any form of financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eGuo Li was responsible for the conceptualization, data curation, and writing of the original draft, as well as software development. Niu Yangtong contributed to the software development, data curation, formal analysis, conceptualization, and visualization. Li Xinying handled the conceptualization, methodology, project administration, data curation, and resources. Li Yuting was involved in visualization, project administration, resources, conceptualization, and methodology. Tong Shiyu contributed to the conceptualization, visualization, funding acquisition, validation, and project administration. Cai Wenwei was responsible for writing the original draft, reviewing and editing, project administration, supervision, and formal analysis. Xue Zhaoxia was involved in reviewing and editing, project administration, resources, conceptualization, writing the original draft, and supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Acknowledgements：\u003c/strong\u003eFirstly, I would like to express my gratitude to the teachers and colleagues who provided me with help and guidance during the research process. The discussions and exchanges with them were incredibly beneficial, offering new ideas and directions for my research.Furthermore, I would like to thank my family and friends. Their understanding, support, and encouragement have been a constant source of motivation for me, and they have provided me with great comfort and help when I encountered difficulties and setbacks.Here, I would like to extend my most sincere thanks once again to everyone who has helped and supported me!\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiang, F. (2019). The Contemporary Logic, Connotation, and Pathways of Moral Education for Medical Postgraduates in the New Era: A Perspective from Healthcare Public Opinion. \u003cem\u003eJournal of Hefei Normal university\u003c/em\u003e, 37(3), pp.100\u0026ndash;102. doi:https://doi.org/10.3969/j.issn.1674-2273%20.2019.03.027.\u003c/li\u003e\n\u003cli\u003eMA, Y., HU, Z., SUN, Y., QIN, B., YANG, X. and HE, Y. (2022). 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The Relationship between Attachment, PTSD and PTG Among adolescents after the Wenchuan earthquake: the mediating role of perceived social support and coping. \u003cem\u003ePsychological Development and Education\u003c/em\u003e, 34(1), pp.112\u0026ndash;119. doi:https://doi.org/10.16187/j.cnki.issn1001-4918.2018.01.14.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"prosocial behavior, meaning in life, network analysis, master's degree students","lastPublishedDoi":"10.21203/rs.3.rs-7164933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7164933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe complex and multidimensional internal attributes of prosocial behavior, as crucial aspects of the growth of contemporary graduate students in medical schools, have not yet been systematically explored. Using a network analysis approach to explore the core features, dynamic evolution, and external associations of graduate students in medical schools’network adaptation, this study explored the interrelationships between prosocial behaviors and meaning in life among graduate students inmedical schools. It was found that there is a bidirectional facilitative relationship between the two, with individuals with high meaning in life being more likely to engage in prosocial behaviors, which in turn enhances the individual's meaning in life. Among them, altruistic behavior is the core dimension of prosocial behavior, which is closely related to anonymity, emotions,and the public. In addition, prosocial behaviors showed dynamic changes over time, with helping behaviors in emergency situations being a significant predictor of subsequent altruistic and emotional behaviors.\u003c/p\u003e","manuscriptTitle":"Complementing each other: a topological analysis of prosocial behaviors and meaning in life among master's degree students in medical schools: based on longitudinal tracking data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 09:24:15","doi":"10.21203/rs.3.rs-7164933/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0fe68bb4-51fc-4288-a585-65afd05a648b","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-15T08:39:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-24 09:24:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7164933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7164933","identity":"rs-7164933","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}