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Predicting nursing students’ psychological well-being: network analysis based on a model of thriving through relationships



Psychological well-being plays a vital role in nursing students’ mental health and affects their decisions to stay in the nursing profession, particularly during the COVID-19 outbreak. Close relationships are undeniably linked to psychological well-being, but it is unknown how the specific pathways through which close relationships are related to each other and which are most strongly linked to nursing students’ psychological well-being.


To explore the network structure, central and bridge factors among well-being characteristics, and predictors based on a model of thriving through relationships.


A cross-sectional research design was used with a sample of undergraduate nursing students (531 participants from the Southwest part of China). We used a network model to analyze the network structure of perceived social support, mindfulness, self-integrity, self-compassion, professional self-concept, savoring, intentional self-regulation, non-relational self-expansion, relational self-expansion, attachment insecurity, and psychological well-being.


 A highly interconnected network of psychological well-being featured predictors and traits were formed. Node 8 (self-kindness), node 9 (self-judgment), and node 23 (non-relational self-expansion) were the predictors with the highest centrality in the network. Perceived social support and professional self-concept were most central in linking predictors to psychological well-being traits. Attachment insecurity was a non-supportive factor for predicting psychological well-being among female nursing students.


Interventions based on these supportive/non-supportive predictors, which operate on different psychological levels, hold promise to achieve positive effects on psychological well-being among nursing students.

Peer Review reports


Psychological well-being(PWB)—is broadly defined as the “positive perception of engagement with different challenges of life” [1, 2], and it refers to one’s sense of growth and self‐realization, to how people efforts to facilitate experiences of purposeful engagement [3]. Research indicated that psychological well-being plays a vital role in influencing nursing students’ decision to enter and remain in the nursing field and successfully adapt to college/university life [4]. Unfortunately, their psychological well-being has been struck by COVID-19 badly [4, 5]. Moreover, nursing students as a group, globally, may be inherently more vulnerable to psychological distress [6]. In China, the prevalence of anxiety, depression, or comorbid anxiety and depression was 55.0%, 56.4%, and 31.6%, respectively [7]. Numerous calls for paying attention to social support would be an effective way to improve nursing students’ well-being [8,9,10].

The years of early adulthood, with key tasks of self-identity and developing close relationships, engender both opportunities and risks for thriving. Despite a wealth of evidence linking perceived social support to psychological well-being in clinical and non-clinical populations, how to promote psychological well-being via factors based on close relationships remains unknown, and most works in this area lack theoretical support. Feeney and Collins [11] proposed a theoretical model of thriving through relationships, and there are eight categories of pathways linking social support to well-being: emotional state, self-perceptions, appraisals of events, situation-relevant behaviors and resources, relational outcomes, motivational state, neural activation, and physiological functioning, and lifestyle behaviors. This model of thriving through relationships provided a theoretical foundation for identifying the specific interpersonal processes that underlie the effects of close relationships on thriving [11], extending attachment theory in which relational attitudes influenced by social support contribute to well-being outcomes.

The first six pathways (i.e., emotional state, self-perceptions, appraisals of events, situation relevant behaviors and resources, relational outcomes, and motivational state) in the Feeney and Collins [11] model exactly map on to mindfulness, self-integrity, self-compassion, professional self-concept, savoring, intentional self-regulation, relational self-expansion, and non-relational self-expansion. Attachment insecurity was added to the study. Additionally, there is a substantial amount of evidence linking each of these nine factors to psychological well-being.

The first pathway of the model demonstrated that social support allows for one's objective emotional states and promotes self-monitoring capacity and objective observability, which has positive effects on promoting intrinsic motivation and thriving due to focus on the present moment [12]. Mindfulness is a self-regulatory approach in which an individual awareness of the present moment rather than the experience without judgments [13, 14]. The mindful capability allows for more self-consistent strategies to deal with undesirable stimuli and distress [14]. Several types of research linked mindfulness to better psychological well-being [15].

According to the second pathway (i.e. self-perception/self-acceptance), we chose three predictors (i.e. self-compassion, self-integrity, and professional self-concept). Self-compassion, i.e., benevolence and concern for oneself. [16]. According to cognitive neuroscience, self-compassion also associates with the soothing system of the emotion regulation system and therefore promotes well-being [17]. In addition, differences due to nursing and non-medical backgrounds may alter the perception or experience of self-compassion. There is a greater emphasis on compassion in nursing cultures [18]. Self-Integrity is defined as a process and tendency that individuals build connections between differentiating and multiplying self components, increase the level of conceptualization, and eventually form a sustainable and stable core self [19]. Individuals with self-affirmative resources are less reactive to and defensive about stressors because their overall sense of self-integrity relies less on the outcome of that particular stressor [20,21,22,23]. Research studies on self-concept differentiation, in contrast to self-integrity, have found that self-concept differentiation is associated with negative mental health outcomes, such as anxiety [24], loneliness [25] and depression [25, 26]. As with self-compassion, professional self-concept is also a type of self-representation that encompasses self-perception and self-evaluation through nursing activities, knowledge, and interpersonal relationships in the surrounding environment [27]. Limited research has shown that negative self-concept of university nursing students is associated with depression and anxiety [28].

Compared to non-medical students, nursing students may encounter more challenges in their life, such as death, negative professional image, workplace violence, etc. [8]. Greater ability to focus on positive experiences is the key to overcoming obstacles and challenges and predicted greater Psychological Well-Being [29, 30]. According to Bryant and Veroff (2007), savoring is the capacity to regulate your positive feelings by directing your attention to positive experiences, appreciating these experiences, and elaborating or enhancing your experience of these positive moments in one’s life [29, 31, 32].

Intentional self-regulation(ISR)-conceptualized by the SOC model of Selection, Optimization, and Compensation-is an essential skill in promoting long-term thriving and adaptive development across the life span [33]. This kind of ISR can deal with goal conflict and reach goals [34]. Those higher in goal conflict report lower well-being (e.g. [35, 36]). There are studies linking SOC strategies to better subjective well-being [37], as well as psychological well-being [38]. According to Feeney and Collins [11], social support also leads to better self-regulation and coping strategies that the ability to control one’s behavior, emotions, and thoughts [39].

Relational self-expansion(RSE)-refers to a relational attitude that which a person adopts the attributes of a partner into one’s self-concept in a close relationship [40]. Relational self-expansion will be independent of negative life events and emotional distress [41], and will enhance relationship satisfaction and one's potential [42]. Thus, this process can promote one’s self-development and positive perceptions. Several kinds of research indicated that self-expansion has an overall positive effect on well-being [43,44,45].

Non-relational self-expansion (NRSE)-is defined as an intrinsic motivation to enhance one’s self-efficacy, which occurs by engaging in novel, challenging, and exciting activities, and incorporating others perspectives and experiences into their self-concept [46]. Self-expansion is positively associated with a variety of well-being outcomes (e.g. life satisfaction, positive affect, and psychological well-being) for both individuals [47] and their relationships [48], as well as decreased depression and perceived stress [46]. It has been suggested that social support may promote motivation (self-expansion) by allowing boldness and willingness to pull up stakes [11].

Attachment is typically conceptualized based on two dimensions: anxiety and avoidance [49]. Researches that link attachment to well-being are relatively recent and have focused more on SWB than on PWB. Limited literature has explored the relation between attachment insecurity and psychological distress [50].

Even though the supportive and non-supportive factors listed above are well supported by the theory of thriving through relationships and previous studies, interactions between different predictors and well-being traits remain unclear. Network analysis is well-suited to model these relations because of the complexity that may be involved. Network analysis is a statistical and theoretical framework that has been utilized increasingly to explore the relations between a wide variety of disorders and symptoms in psychiatry [51]. By putting all factors into a network, the network analysis can estimate which factor is most “central” or “bridged”. A drawback of previous studies is that for the most part, they have only focused solely on single pathways and not on the interactions of a line of factors [52]. Limited study has explored how factors based on close relationships are related to nursing students’ traits of psychological well-being.

In the present study, we used network analytic techniques to better understand the interactive relations among traits and predictors of psychological well-being in nursing students, as well as to identify the central and bridged predictors.


Setting and participants

This study took place in southwest China, and 531 undergraduate nursing students 18–25 years of age (female: 71.8%, 2.9% freshmen, 3.5% sophomores, 6.6% juniors, and 86.8% seniors) from a medical in China (All full-time) took part.

Study design and procedures

This is a cross-sectional study conducted between May 1, 2021, and September 1, 2021. A non-probabilistic sample of undergraduate nursing students was recruited. To increase response rates, the majority of the surveys were conducted during class time. Further, snowball sampling and posters and social media posts on a university campus were used to recruit undergraduate nursing students. Prospective participants were sent an email containing a link to a description of the study and a 30-min survey. Participants provided informed consent and completed an online pre-screening questionnaire for the real study, thereby confirming their identities. The researcher conducted an audit trail to ensure participants' mental health. In addition, the research team provided psychological counseling on a pro bono basis when needed by the participants to avoid potential stress caused by participation in the research. Participants received an extra reward for their time.


The Multidimensional Perceived Social Support Scale (MSPSS)

Perceived social support was assessed using the 12-item Chinese version of the MSPSS [53]. Participants rated each item on a scale from 1 (very strongly disagree) to 7 (very strongly agree). We used a sum score of the full scale as an indicator. Higher overall scores indicated greater perceived social support. The scale has strong internal reliability in the samples used in this study (Cronbach’s αTotal = 0.84).

The Cognitive and Affective Mindfulness Scale-Revised (CAMS-R)

Mindfulness was assessed using the 12-item Chinese version of the CAMS-R composed of four primary scales measuring attention, present focus, awareness, and acceptance [54, 55]. Participants rated each item on a scale from 1 (rarely/not at all) to 4 (almost always). Higher scores indicated greater mindfulness. Cronbach’s alpha for the present sample was adequate (Cronbach’s a = 0.79 ~ 0.88).

The Self-Integrity Scale (SIS)

Self-Integrity was assessed using the 8-item Chinese version of the SIS composed of two primary scales measuring adaptive adequacy, and feelings of moral [56]. Participants indicated their agreement with each item on a scale from 1 (strongly disagree) to 7 (strongly agree). Higher scores indicated greater self-integrity. Cronbach's alphas were 0.84 ~ 0.91.

The Self-Compassion Scale (SCS)

Self-Compassion was assessed using the 26-item Chinese version of the SCS composed of six primary scales measuring self-kindness, self-judgment, common humanity, isolation, mindfulness, and over-identification [16]. Response option ranges from 1 (never do that) to 5 (always do that). The negative items were reversed scored, and the sum across all items was calculated. Higher scores indicated greater self-compassion. Cronbach's alphas were 0.77 ~ 0.89. Response option ranges from 1 (never do that) to 5 (always do that). The negative items were reversed scored, and the sum across all items was calculated. Higher scores indicated greater self-compassion [57]. Cronbach's alphas were 0.77 ~ 0.89. Note that: given that nursing students are in a high-stress environment, we followed Neff's [16] 6-component model, but recent studies suggested that omitting the 3 “negative” dimensions to measure self-compassion may be closer to its definition [58,59,60]

The Professional Self-Concept Scale (NSCI)

Professional self-concept was assessed using the 14-item Chinese version of the NSCI [27]. Response option ranges from 1 (definitely false) to 8 (definitely true). For the aim of our study, we used a sum score of the full scale as an indicator. Higher overall scores indicated a more positive professional self-concept. Cronbach's alpha was 0.92.

The Savoring Beliefs Inventory (SBI)

Savoring was assessed using the 24-item Chinese version of the SBI composed of three primary scales measuring anticipating, savoring the moment, and reminiscing [31], ranging from 1 (strongly disagree) to 7 (strongly agree). The 12 odd-numbered items were reverse-scored, and the mean of all items was calculated. Higher scores reflected a greater perceived ability to savor. Cronbach’s α alphas were 0.85 ~ 0.90.

The Measurement of Selection, Optimization, and Compensation (SOC)

The intentional self-regulation was assessed using the 12-item Chinese version of SOC composed of four primary scales measuring elective selection, loss-based selection, optimization, and compensation [33]. Each response was scored as 1 (SOC-related strategy) or 0 (non-SOC-related strategy). Higher scores indicated that the students’ intentional self-regulation can promote the maximization of the adaptive integration of changes in the self and context. Cronbach’s alphas were 0.69 ~ 0.82.

The Self-Expansion Scale (SES)

Relational self-expansion was assessed using the 14-item Chinese version of the SES [61]. Participants rated each item on a scale from 1 (not very much) to 7 (very much). Higher scores correspond to greater evaluations of relational self-expansion. Cronbach’s α was 0.88.

The Self-Expansion Preference Scale (SEPS)

Non-relational self-expansion was assessed using the 24-item Chinese version of the SEPS [46]. Participants rated each item on a scale from 1 (strongly disagree) to 7 (strongly agree). The 12 Self-Conservation items were reverse-scored. Higher scores correspond to greater evaluations of self-expansion. We used a sum score of the full scale as an indicator. Cronbach’s α was 0.89.

The Experiences in Close Relationships–Relationship Structures Scale (ECR-RS)

Attachment insecurity was assessed using the 9-item Chinese version of the ECR-RS composed of two primary scales measuring anxiety and avoidance [62], ranging from 1 (strongly disagree) to 7 (strongly agree). Item-2, 4, 6 & 9 were reverse coded. Higher overall scores indicated a more serious extent to participants feeling anxious about being abandoned and uncomfortable with intimacy and avoiding others. Cronbach’s α alphas were 0.82 and 0.73.

The Ryff’s Scale of Psychological Well-Being (RPWBS)

Psychological Well-being was assessed using the 18-item validated version of the RPWBS composed of six primary scales measuring positive relations with others, autonomy environmental mastery, personal growth, purpose in life, and self-acceptance [63, 64], ranging from 1 (strongly disagree) to 6 (strongly agree). Higher scores reflected better psychological well-being. Cronbach’s α alphas were 0.70 ~ 0.93.

Data analyses

A psychology network was constructed conducting in R (Version 3.3.0) to address the relations among the study variables. The package we used included qgraph [65], glasso [66], mgm [67], igraph [68], Exploratory Graph Analysis [69], and bootnet [70].

The score of 31 variables was used to estimate a Gaussian graphical model (GGM). variables (i.e. nodes) are bound together by conditional dependence relations (i.e. edges) in a network [70]. Thresholded EBICglasso utilizes the “least absolute shrinkage and selection operator”, which was used to reduce the edge estimates (i.e. avoid spurious edges) to estimate GGM [70]. The Fruchterman-Reingold algorithm was used to determine the network, according to the connections between nodes [71].

Betweenness, closeness, and strength were used to estimate the centrality of the network. The node strength was the priority to identify the central nodes due to the reliability, presenting the estimates of betweenness and closeness in Supplementary Fig. S1 [70]. Predictability is shown by the degree of filling to which the circle around each node [72], reflecting the proportion of variance in that node that can be explained by variance in the nodes to which it is connected [70, 73]

We used the bridge function of the Networktools package to identify bridge variables between predictors and psychological well-being in the network. By calculating bridge strength, we identified which predictor was most strongly connected to nursing students’ psychological well-being [74].

The NetworkComparisonTest(NCT) was used to test for differences in centrality for each node, overall connectivity, and network structure in order to identify the network differences between males and females [75]. The correlation stability (CS) coefficient reflects the stability of the parameters. A CS coefficient of 0.25 or above is recommended [70].


The psychological well-being network

The psychological network structure is detailed in Fig. 1. Almost nodes within the network are related to each other positively except for attachment insecurity. The mean predictability was 0.562, which means that on average items from the model share 56.2% of the variance with surrounding items, demonstrating that there is 56.2% variation of nodes explained via variation among other nodes. We can know how well the given node can be predicted by the other nodes surrounding it assuming that all edges go to the node under investigation from its neighbors.

Fig. 1
figure 1

Network structure for predictors and traits of psychological well-being. Notes. Each variable is represented by a node (1 to 31), and it belongs to different variables, indicated by a code in the column on the right. Edges are represented by lines between nodes. Blue edges depict a positive association; red edges a negative association. Wider and more saturated edges reflect stronger associations. Predictability (i.e. the proportion of variance explained for a specific node by variance in nodes to which it is connected) is depicted as a filled part of a circle surrounding each node. The thickness of the line represents the connection strength. According to [73]

Central nodes

Strength centrality for each node, that is, an estimate reflecting what degree each node was connected to other nodes in the network, is presented in Fig. 2. Centrality indices suggested that node 8 (self-kindness), node 9 (self-judgment), and node 23 (non-relational self-expansion) were central nodes in these specific pathways, and node 30 (purpose in life) was the central node in the psychometric measurement of psychological well-being. Node 22 and node 17 emerged as the least central nodes.

Fig. 2
figure 2

Strength centrality estimates for the 31 nodes. The Y-axis represents the centrality indices as standardized z-scores (the greater the estimate, the more central the item is), and the X-axis represents the 31 predictors and traits. According to [73]

Bridge nodes

To examine which nodes were most important in linking nursing students’ psychological well-being to predictors (i.e. the specific pathways through close relationships), we estimated bridge strength for each node. Results indicated that perceived social support and professional self-concept were most central in linking predictors to psychological well-being characteristics. All bridge strength centrality estimates are detailed in Fig. 3.

Fig. 3
figure 3

Bridge strength for each node. Notes. Bridge strength is a measure that reflects the degree to which each node links risk/protective factors and psychopathology symptoms. Z-standardised estimates are presented. Higher values indicate that a node is more important in linking predictors and traits of psychological well-being. According to [73]

Network accuracy and stability

The result of 95% confidence intervals of all edge weights indicated a good accuracy (detailed in Supplementary Fig. S2), and the network was identified accurately via the edge-weight difference test (detailed in Supplementary Fig. S3). CS coefficient values of strength, betweenness, and closeness are 0.75, 0.60, and 0.67, respectively. That is, the psychological well-being network was stable in the differences in node centrality (detailed in Supplementary Fig. S4). The centrality difference test showed that the highest centrality estimates are statistically different from the lowest centrality estimates (detailed in Supplementary Fig. S5).

There were no differences between males NS and females NS on the degree of overall interconnectedness of the network and the centrality of each node (p > 0.05). However, there were differences in network structure. Specifically, attachment insecurity was more strongly linked to relational self-expansion, savoring and positive relations with others among females than males (p < 0.01), that is, these three nodes were significantly demonstrated more negatively associated among females than males.


By applying a network analytical approach, the study explored how the predictors based on the model of thriving through relationships (i.e. perceived social support, mindfulness, self-integrity, self-compassion, professional self-concept, savoring, intentional self-regulation, non-relational self-expansion, relational self-expansion, and attachment insecurity) were interrelated, and how these factors were related to psychological well-being in a sample of nursing students. The centrality of the network is stable and identified by a bootstrap approach. Furthermore, network comparison between gender provided evidence that the network has relatively high stability and generalizability. Overall, the results revealed a closely related network between predictors and characteristics of psychological well-being. A detailed description of the breakdown of each predictor is omitted here, due to restrictions on paper length, but each central node and bridge node is discussed individually.

In addition to the supportive predictors, attachment insecurity was significantly and negatively associated with both savoring beliefs and the self-acceptance dimension of psychological well-being among nursing students, suggesting that attachment insecurity was a non-supportive predictor for nursing students’ psychological well-being. Echoing previous theories suggesting that attachment insecurity may interfere with the improvement of the savoring ability and psychological well-being [76, 77]. According to attachment theory [78], individuals higher in attachment anxiety exhibit hyper-activation of the attachment system. Nursing students whose higher scores in both anxiety and avoidance may be consumed by insecurity and pay more attention to their emotions and feelings, thus weakening their strategies for savoring and self-acceptance [79,80,81].

Critically, the predictors that demonstrated the highest centrality were identified, which might be the measures and targets that can maximize the effectiveness of future interventions [82]. The node strength was used as the main centrality index because of its stability. Among all of the predictors, the nodes with the highest centrality are node 8 (self-kindness), node 9 (self-judgment), and node 23 (non-relational self-expansion). That is, the self-related attitudes play a vital role in influencing the other predictors, as well as psychological well-being traits among nursing students [57]. These results confirmed and extended previous research. First, in terms of self-compassion, these results are consistent with the previous study, indicating that lack of self-kindness may be an essential reason for nurses’ psychological distress in a large sample of nurses/nursing students in New Zealand [18]. It is true that self-compassion also avoids comparison with others or focuses on self-kindness/judgment, thus minimizing the distortion of self-concepts [83]. Self-compassion strengthens or facilitates the association between positive psychological constructs and both mental (e.g. [52, 84]), and physical (e.g., [85]) health outcomes. It suggests that the self-kindness and self-judgment dimensions of self-compassion are central ties of well-being traits and predictors, and this result that self-compassion has more positive effects on nurses group than doctors or medical students [18] seems to be cross-culture stable. Second, in terms of non-relational self-expansion, consistent with previous research, NRSE was associated with better psychological well-being, despite limited research that has explored the relationship between NRSE and other mental health outcomes [46]. This result was also validated in nursing students. By self-expansion, nursing students are more likely to increase self-efficacy and self-concept clarity to enhance nursing students' professional self-concept and professional identity due to the personal potential enhancement [46], thereby improving self-acceptance and self-growth to promote psychological well-being [11]. The relatively high stability (centrality) of node 8 (self-kindness), and node 9 (self-judgment) in our results suggest the importance of self-compassion in Chinese nursing students’ psychological well-being.

Interestingly, bridge strength indices suggested that perceived social support was most central in linking predictors to psychological well-being traits. These findings are generally consistent with a study by Feeney and Collins' theoretical model [12] that identified close relationships(e.g. social support) as the strongest protective factor for long-term thriving(e.g. Psychological well-being, social well-being, and physical well-being) in the general population, and even though in patients, and with the empirical study in the sample of college students in U.S.A [52], identifying that perceived social support predict well-being via the mediators of mindfulness, savoring and self-compassion. However, classical regression analysis was unable to reveal the complex variable network achieved in this study which may imply that perceived social support not only influences the rest of the predictors but can also be influenced inversely [86]. In addition, nursing students' professional self-concept was the central bridge node second only to perceived social support. the result may imply that the other predictors were partly linked to psychological well-being traits through professional self-concept, that is, the link between the rest of the predictors and well-being traits seems to be mediated by professional self-concept [57, 87]. The protective factor related close relationships seems to be promoted self-acceptance and self-identify, which are complementary to the structure of psychological well-being.

Finally, considering network differences between males and females, attachment insecurity has negative effects on relational self-expansion, savoring, and positive relations with others among females than males [88]. Research has found gender differences in attachment, showing that females tend to be significantly more attached to their peers than males [74, 89]. Similarly, in an Asian context, male college students showed higher trust toward close relationships when compared to female college students, whereas females showed higher levels of alienation [74]. Our results suggest that attachment insecurity was a non-supportive factor in predicting psychological well-being among female nursing students.

Limitations and future research

Despite this being the first empirical study to reveal the complex variable network based on the model of thriving through relationships, it is also important to acknowledge the limitations of the current study. First, the data in the present study were obtained from self-reports, which can produce self-report response bias [90]. Second, it prevents inferences about how variables are causally related as all data were collected at a single time point. Future researchers are advised to explore the causal relationship among these examined variables in a longitudinal design [91]. Third, the generalisability of the findings is limited due to professional differences and sample characteristics. This means that network properties such as central nodes and bridge nodes may be unlikely to replicate in the same way in other medical groups [92]. Finally, despite our data providing evidence of predictors, there are undoubtedly additional supportive/ non-supportive predictors that contribute to and may also account for promoting better psychological well-being. Future research should replicate these findings, extending our results by exploring other pathways and research methods. Moreover, the findings revealed some core predictors, which may contribute to strategies for exploring intervention measures for nursing students' psychological well-being.


The current study reveals that three predictors have the highest degree of centrality in nursing students’ psychological well-being network: self-kindness, self-judgment, and non-relational self-expansion, respectively. Perceived social support and professional self-concept were most central in linking predictors to psychological well-being traits. These predictors might serve as targets of interventions to promote nursing students' psychological well-being.

Availability of data and material

The datasets analyzed in this manuscript are not publicly available. Requests to access the datasets should be directed to:


  1. Ryff CD, Singer BH. Know thyself and become what you are: a eudaimonic approach to psychological well-being. J Happiness Stud. 2008;9(1):13–39.

    Article  Google Scholar 

  2. Miyamoto Y, Ma X. Dampening or savoring positive emotions: a dialectical cultural script guides emotion regulation. Emotion. 2011;11(6):1346.

    Article  Google Scholar 

  3. Ruini C, Ryff CD. Using eudaimonic well-being to improve lives. 2016.

    Book  Google Scholar 

  4. Morales-Rodríguez FM, Espigares-López I, Brown T, Pérez-Mármol JM. The relationship between psychological well-being and psychosocial factors in university students. Int J Environ Res Public Health. 2020;17(13):4778.

    Article  Google Scholar 

  5. Melnyk BM, Gallagher-Ford L, Zellefrow C, Tucker S, Thomas B, Sinnott LT, Tan A. The first US study on nurses’ evidence-based practice competencies indicates major deficits that threaten healthcare quality, safety, and patient outcomes. Worldviews Evid Based Nurs. 2018;15(1):16–25.

    Article  Google Scholar 

  6. Brouwer K, Walmsley L, Parrish E, McCubbin A, Welsh J, Braido C, Okoli C. Examining the associations between self-care practices and psychological distress among nursing students during the COVID-19 pandemic. Nurse Educ Today. 2021;100:104864.

    Article  Google Scholar 

  7. Zhu Y, Wang H, Wang A. An evaluation of mental health and emotion regulation experienced by undergraduate nursing students in China during the COVID-19 pandemic: a cross-sectional study. Int J Ment Health Nurs. 2021;30(5):1160–116.

    Article  Google Scholar 

  8. Li Y, Wang Y, Jiang J, Valdimarsdóttir UA, Fall K, Fang F, Song H, Lu D, Zhang W. Psychological distress among health professional students during the COVID-19 outbreak. Psychological Medicine. 2021;51:1952–4.

  9. Labrague LJ, De los Santos JAA, Falguera C. Social and emotional loneliness among college students during the COVID-19 pandemic: the predictive role of coping behaviours, social support, and personal resilience. 2021.

    Google Scholar 

  10. Labrague LJ, De los Santos JAA. COVID-19 anxiety among front-line nurses: predictive role of organisational support, personal resilience and social support. J Nurs Manag. 2020;28(7):1653–61.

    Article  Google Scholar 

  11. Feeney BC, Collins NL. A new look at social support: a theoretical perspective on thriving through relationships. Pers Soc Psychol Rev. 2015;19(2):113–47.

    Article  Google Scholar 

  12. Feeney BC, Collins NL. Thriving through relationships. Curr Opin Psychol. 2015;1:22–8.

    Article  Google Scholar 

  13. Bishop SR, Lau M, Shapiro S, Carlson L, Anderson ND, Carmody J, Segal ZV, Abbey S, Speca M, Velting D. Mindfulness: a proposed operational definition. Clin Psychol Sci Pract. 2004;11(3):230.

    Article  Google Scholar 

  14. Brown KW, Ryan RM. The benefits of being present: mindfulness and its role in psychological well-being. J Pers Soc Psychol. 2003;84(4):822.

    Article  Google Scholar 

  15. Querstret D, Morison L, Dickinson S, Cropley M, John M. Mindfulness-based stress reduction and mindfulness-based cognitive therapy for psychological health and well-being in nonclinical samples: a systematic review and meta-analysis. Int J Stress Manage. 2020;27(4):394.

    Article  Google Scholar 

  16. Neff KD. Self-compassion: moving beyond the pitfalls of a separate self-concept. 2008.

    Google Scholar 

  17. Gilbert P. An introduction to compassion focused therapy in cognitive behavior therapy. Int J Cogn Ther. 2010;3(2):97–112.

    Article  Google Scholar 

  18. Dev V, Fernando AT, Consedine NS. Self-compassion as a stress moderator: a cross-sectional study of 1700 doctors, nurses, and medical students. Mindfulness. 2020;11(5):1170–81.

    Article  Google Scholar 

  19. Aronson J, Cohen G, Nail PR. Self-affirmation theory: an update and appraisal. 1999.

    Google Scholar 

  20. Creswell JD, Welch WT, Taylor SE, Sherman DK, Gruenewald TL, Mann T. Affirmation of personal values buffers neuroendocrine and psychological stress responses. Psychol Sci. 2005;16(11):846–51.

    Article  Google Scholar 

  21. Crocker J, Niiya Y, Mischkowski D. Why does writing about important values reduce defensiveness? Self-affirmation and the role of positive other-directed feelings. Psychol Sci. 2008;19(7):740–7.

    Article  Google Scholar 

  22. Murray SL, Bellavia G, Feeney B, Holmes JG, Rose P. The contingencies of interpersonal acceptance: when romantic relationships function as a self-affirmational resource. Motiv Emot. 2001;25(2):163–89.

    Article  Google Scholar 

  23. Sherman DK, Cohen GL. The psychology of self-defense: self-affirmation theory. Adv Exp Soc Psychol. 2006;38:183–242.

    Article  Google Scholar 

  24. Bigler M, Neimeyer GJ, Brown E. The divided self revisited: Effects of self-concept clarity and self-concept differentiation on psychological adjustment. J Soc Clin Psychol. 2001;20(3):396–415.

    Article  Google Scholar 

  25. Lutz CJ, Ross SR. Elaboration versus fragmentation: Distinguishing between self-complexity and self-concept differentiation. J Soc Clin Psychol. 2003;22(5):537–59.

    Article  Google Scholar 

  26. Diehl M, Hay EL. Self-concept differentiation and self-concept clarity across adulthood: associations with age and psychological well-being. Int J Aging Hum Dev. 2011;73(2):125–52.

    Article  Google Scholar 

  27. Angel E, Craven R, Denson N. The nurses’ self-concept instrument (NSCI): A comparison of domestic and international student nurses’ professional self-concepts from a large Australian University. Nurse Educ Today. 2012;32(6):636–40.

    Article  Google Scholar 

  28. Shang B, Wang S, Fan Y, Jiang E. Exploring the relationship between self-concept and mental health among undergraduate nursing students: a cross-sectional study. Mathews J Psychiatry Ment Health. 2017;2(2):1–6.

    Google Scholar 

  29. Smith JL, Hollinger-Smith L. Savoring, resilience, and psychological well-being in older adults. Aging Ment Health. 2015;19(3):192–200.

    Article  Google Scholar 

  30. Smith JL, Bryant FB. The benefits of savoring life: savoring as a moderator of the relationship between health and life satisfaction in older adults. Int J Aging Hum Dev. 2016;84(1):3–23.

    Article  Google Scholar 

  31. Bryant F. Savoring Beliefs Inventory (SBI): a scale for measuring beliefs about savouring. J Ment Health. 2003;12(2):175–96.

    Article  Google Scholar 

  32. Bryant FB, Chadwick ED, Kluwe K. Understandingthe processes that regulate positive emotional experience: Unsolved problems and future directions for theory and research on savoring. Int J Wellbeing. 2011;1(1):107–26.

  33. Baltes PB, Baltes MM, Freund AM, Lang FR. The measurement of selection, optimization, and compensation (SOC) by self report: Technical report 1999: Max-Planck-Institut für Bildungsforschung; 1999.

  34. Knecht M, Freund AM. The use of selection, optimization, and compensation (SOC) in goal pursuit in the daily lives of middle-aged adults. Eur J Dev Psychol. 2017;14(3):350–66.

    Article  Google Scholar 

  35. Emmons RA, King LA. Conflict among personal strivings: immediate and long-term implications for psychological and physical well-being. J Pers Soc Psychol. 1988;54(6):1040.

    Article  Google Scholar 

  36. Riediger M, Freund AM. Interference and facilitation among personal goals: differential associations with subjective well-being and persistent goal pursuit. Pers Soc Psychol Bull. 2004;30(12):1511–23.

    Article  Google Scholar 

  37. Jopp D, Smith J. Resources and life-management strategies as determinants of successful aging: on the protective effect of selection, optimization, and compensation. Psychol Aging. 2006;21(2):253.

    Article  Google Scholar 

  38. Freund AM, Baltes PB. Life-management strategies of selection, optimization and compensation: measurement by self-report and construct validity. J Pers Soc Psychol. 2002;82(4):642.

    Article  Google Scholar 

  39. Muraven M, Tice DM, Baumeister RF. Self-control as a limited resource: regulatory depletion patterns. J Pers Soc Psychol. 1998;74(3):774.

    Article  Google Scholar 

  40. Dobson K, Stanton SC, Veeravalli A. The power of potential: relational self-expansion potential and daily self-expansion behaviors uniquely contribute to relationship quality over time. 2021.

    Google Scholar 

  41. Stenseng F, Rise J, Kraft P. Activity engagement as escape from self: the role of self-suppression and self-expansion. Leis Sci. 2012;34(1):19–38.

    Article  Google Scholar 

  42. Schindler I, Paech J, Löwenbrück F. Linking admiration and adoration to self-expansion: different ways to enhance one’s potential. Cogn Emot. 2015;29(2):292–310.

    Article  Google Scholar 

  43. Ketay S, Beck LA, Welker KM. Self-expansion: Intergroup and Sociocultural Factors. In: Mattingly B, McIntyre K, Lewandowski Jr. G, editors. Interpersonal Relationships and the Self-Concept. Cham: Springer; 2020.

  44. Liu S, Peng M, Lewandowski GW. Psychometric evaluation of a Chinese translation of the relational and individual self-expansion scales. Curr Psychol. 2022.

  45. Emery LF, Hughes EK, Gardner WL. Confusion or Clarity? Examining a Possible Tradeoff Between Self-Expansion and Self-Concept Clarity. Soc Psychol Personal Sci. 2022.

  46. Hughes EK, Slotter EB, Lewandowski GW Jr. Expanding who I am: validating the self-expansion preference scale. J Pers Assess. 2020;102(6):792–803.

    Article  Google Scholar 

  47. Mattingly BA, Lewandowski GW Jr. An expanded self is a more capable self: the association between self-concept size and self-efficacy. Self and Identity. 2013;12(6):621–34.

    Article  Google Scholar 

  48. Aron A, Lewandowski Jr GW, Mashek D, Aron EN. The self-expansion model of motivation and cognition in close relationships. 2013.

    Book  Google Scholar 

  49. Brennan KA, Clark CL, Shaver PR. Self-report measurement of adult attachment: an integrative overview. 1998.

    Google Scholar 

  50. Marrero-Quevedo RJ, Blanco-Hernández PJ, Hernández-Cabrera JA. Adult attachment and psychological well-being: the mediating role of personality. J Adult Dev. 2019;26(1):41–56.

    Article  Google Scholar 

  51. Sahlan RN, Williams BM, Forrest LN, Saunders JF, Fitzsimmons-Craft EE, Levinson CA. Disordered eating, self-esteem, and depression symptoms in Iranian adolescents and young adults: a network analysis. Int J Eat Disord. 2021;54(2):132–47.

    Article  Google Scholar 

  52. Wilson JM, Weiss A, Shook NJ. Mindfulness, self-compassion, and savoring: factors that explain the relation between perceived social support and well-being. Pers Individ Differ. 2020;152:109568.

    Article  Google Scholar 

  53. Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. J Pers Assess. 1988;52(1):30–41.

    Article  Google Scholar 

  54. Feldman G, Hayes A, Kumar S, Greeson J, Laurenceau J-P. Mindfulness and emotion regulation: The development and initial validation of the Cognitive and Affective Mindfulness Scale-Revised (CAMS-R). J Psychopathol Behav Assess. 2007;29(3):177–90.

    Article  Google Scholar 

  55. Chan H, Lo L, Lin M, Thompson N. Revalidation of the Cognitive and Affective Mindfulness Scale — Revised (CAMS-R) With Its Newly Developed Chinese Version (Ch-CAMS-R). J Pac Rim Psychol. 2016;10:E1.

  56. Cohen GL, Sherman DK. The psychology of change: Self-affirmation and social psychological intervention. Annu Rev Psychol. 2014;65:333–71.

    Article  Google Scholar 

  57. Zhou L, Sukpasjaroen K, Wu Y, Gao L, Chankoson T, Cai E. Perceived social support promotes nursing students’ psychological wellbeing: explained with self-compassion and professional self-concept. Front Psychol. 2022;13:835134.

    Article  Google Scholar 

  58. Montero-Marín J, Gaete J, Demarzo M, Rodero B, Lopez LCS, García-Campayo J. Self-criticism: a measure of uncompassionate behaviors toward the self, based on the negative components of the self-compassion scale. Front Psychol. 2016;7:1281.

    Article  Google Scholar 

  59. Muris P, Petrocchi N. Protection or vulnerability? A meta-analysis of the relations between the positive and negative components of self-compassion and psychopathology. Clin Psychol Psychother. 2017;24(2):373–83.

    Article  Google Scholar 

  60. Muris P, van den Broek M, Otgaar H, Oudenhoven I, Lennartz J. Good and bad sides of self-compassion: a face validity check of the Self-Compassion Scale and an investigation of its relations to coping and emotional symptoms in non-clinical adolescents. J Child Fam Stud. 2018;27(8):2411–21.

    Article  Google Scholar 

  61. Aron A, Norman CC, Aron EN, Lewandowski G. Shared participation in self-expanding activities: Positive effects on experienced marital quality. Understanding marriage: Developments in the study of couple interaction. 2002. p. 177–94.

    Google Scholar 

  62. Zhang Q, Hou ZJ, Fraley RC, Hu Y, Zhang X, Zhang J, Guo X. Validating the Experiences in Close Relationships–Relationship Structures Scale among Chinese Children and Adolescents. J Pers Assess. 2021.

  63. Ryff CD, Keyes CLM. The structure of psychological well-being revisited. J Pers Soc Psychol. 1995;69(4):719.

    Article  Google Scholar 

  64. Li R-H. Reliability and validity of a shorter Chinese version for Ryff’s psychological well-being scale. Health Educ J. 2014;73(4):446–52.

    Article  Google Scholar 

  65. Epskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: network visualizations of relationships in psychometric data. J Stat Softw. 2012;48:1–18.

    Article  Google Scholar 

  66. Friedman J, Hastie T, Tibshirani R, Tibshirani MR. Package ‘glasso.’ 2015.

    Google Scholar 

  67. Haslbeck J, Waldorp LJ. mgm: estimating time-varying mixed graphical models in high-dimensional data. arXiv preprint arXiv:151006871 2015.

  68. Csárdi G. igraph–a package for network analysis. Thomas Achia, Atinuke Adebanji, John Owino, Anne Wangombe: Spatial Durbin Model for Poverty Mapping and Analysis 8 Daniel Adler, Jens Oehlschlägel, Oleg Nenadic, Walter Zucchini: Large atomic data in R: package’ff’9 Claudio Agostinelli: Robust Inference in Generalized Linear Models 10 2014:43.

  69. Golino HF, Epskamp S. Exploratory graph analysis: a new approach for estimating the number of dimensions in psychological research. PLoS One. 2017;12(6):e0174035.

    Article  Google Scholar 

  70. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Methods. 2018;50(1):195–212.

    Article  Google Scholar 

  71. Fruchterman TM, Reingold EM. Graph drawing by force-directed placement. Software Pract Exper. 1991;21(11):1129–64.

    Article  Google Scholar 

  72. Haslbeck JM, Fried EI. How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets. Psychol Med. 2017;47(16):2767–76.

    Article  Google Scholar 

  73. Zeng G, Peng K, Hu C-P. The network structure of adolescent well-being traits: results from a large-scale Chinese sample. Front Psychol. 2019;10:2783.

    Article  Google Scholar 

  74. Barcaccia B, Cervin M, Pozza A, Medvedev ON, Baiocco R, Pallini S. Mindfulness, self-compassion and attachment: a network analysis of psychopathology symptoms in adolescents. Mindfulness. 2020;11(11):2531–41.

    Article  Google Scholar 

  75. Van Borkulo CD, Boschloo L, Kossakowski J, Tio P, Schoevers RA, Borsboom D, Waldorp LJ. Comparing network structures on three aspects: a permutation test. Manuscript submitted for publication 2017, 10.

  76. Borelli JL, Smiley PA, Kerr ML, Hong K, Hecht HK, Blackard MB, Falasiri E, Cervantes BR, Bond DK. Relational savoring: an attachment-based approach to promoting interpersonal flourishing. Psychotherapy. 2020;57(3):340.

    Article  Google Scholar 

  77. Gentzler AL, Palmer CA, Ramsey MA. Savoring with intent: Investigating types of and motives for responses to positive events. J Happiness Stud. 2016;17(3):937–58.

    Article  Google Scholar 

  78. Mikulincer M, Shaver PR. Attachment in adulthood: Structure, dynamics, and change. Guilford Publications. 2010.

  79. Laan AJ, Van Assen MA, Vingerhoets AJ. Individual differences in adult crying: the role of attachment styles. Soc Behav Personal Int J. 2012;40(3):453–71.

    Article  Google Scholar 

  80. Palmer CA, Gentzler AL. Adults’ self-reported attachment influences their savoring ability. J Posit Psychol. 2018;13(3):290–300.

    Article  Google Scholar 

  81. Karreman A, Vingerhoets AJ. Attachment and well-being: The mediating role of emotion regulation and resilience. Pers Individ Differ. 2012;53(7):821–6.

    Article  Google Scholar 

  82. Miguel AdQC, Tempski P, Kobayasi R, Mayer FB, Martins MA. Predictive factors of quality of life among medical students: results from a multicentric study. BMC Psychol. 2021;9(1):1–13.

    Article  Google Scholar 

  83. Játiva R, Cerezo MA. The mediating role of self-compassion in the relationship between victimization and psychological maladjustment in a sample of adolescents. Child Abuse Negl. 2014;38(7):1180–90.

    Article  Google Scholar 

  84. Körner M, Wirtz MA, Bengel J, Göritz AS. Relationship of organizational culture, teamwork and job satisfaction in interprofessional teams. BMC Health Serv Res. 2015;15(1):1–12.

    Article  Google Scholar 

  85. Neff K. Self-compassion: an alternative conceptualization of a healthy attitude toward oneself. Self Identity. 2003;2(2):85–101.

    Article  Google Scholar 

  86. Gunst RF, Mason RL. Regression Analysis and Its Application: A Data-Oriented Approach (1st ed.). CRC Press; 1980.

  87. Thagard P, Wood JV. Eighty phenomena about the self: representation, evaluation, regulation, and change. Front Psychol. 2015;6:334.

    Article  Google Scholar 

  88. Jahng KE, Kim D. College students’ adult attachment and career adaptability: mediation by maladaptive perfectionism and moderation by gender. J Career Dev. 2021;48(4):507–20.

    Article  Google Scholar 

  89. Gorrese A, Ruggieri R. Peer attachment: a meta-analytic review of gender and age differences and associations with parent attachment. J Youth Adolesc. 2012;41(5):650–72.

    Article  Google Scholar 

  90. Paulhus DL, Vazire S. The self-report method. Handbook of research methods in personality psychology. 2007;2007(1):224–39.

    Google Scholar 

  91. Doty DH, Glick WH. Common methods bias: does common methods variance really bias results? Organ Res Methods. 1998;1(4):374–406.

    Article  Google Scholar 

  92. Yilmaz K. Comparison of quantitative and qualitative research traditions: epistemological, theoretical, and methodological differences. Eur J Educ. 2013;48(2):311–25.

    Article  Google Scholar 

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We thank all participants and interviewers involved in our study.


The study was supported by the Yunnan University of Chinese Medicine Research Program (2022J0364).

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Authors and Affiliations



Conceptualization, T.C, EL.C, L.Z.; software, L.Z.; formal analysis, L.Z. L.W.; data curation, MY,W., and L.W.; writing—review and editing, K.S, L.Z.; supervision, EL.C.;T.C. All authors have approved the final version of the manuscript.

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Correspondence to EnLi Cai.

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Zhou, L., Sukpasjaroen, K., Wu, Y. et al. Predicting nursing students’ psychological well-being: network analysis based on a model of thriving through relationships. BMC Med Educ 22, 463 (2022).

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  • Psychological well-being
  • Relationships
  • Nursing students
  • Network analysis