Bridging the connections between Big Five personality traits and mental well-being among medical staff: A network analysis

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While the Big Five personality traits (Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness) have been linked to mental well-being, most studies have focus on the overall construct level. Research examining the connections between these traits and mental-being at a component level is currently lacking. Method The Big Five personality traits and mental well-being components of 420 medical staff were assessed using the Chinese Big-Five Personality Inventory-15 and the 14-item Warwick-Edinburgh Mental Well-being Scale. Through network analysis, we examined the distinct connections between different Big Five personality traits and mental well-being components. Additionally, we used bridge centrality indexes to pinpoint the bridging effects of each Big Five personality traits on the mental well-being components community. Result There are distinct positive connections (e.g., Conscientiousness - Feeling Useful, Openness - Feeling interested in new things) and negative connection (e.g., Neuroticism - Feeling optimistic about future) between different dimensions of the Big Five personality traits and mental well-being components. Conscientiousness exhibited the highest positive bridging effects on the mental well-being components community, while Neuroticism showed the highest negative bridging effects. Conclusion These findings enhance current knowledge by elucidating the potential pathways between the Big Five personality traits and mental well-being components, providing novel insights for reassessing targets and developing intervention strategies to improve the mental health of medical staff in challenging medical settings. Big Five personality traits medical staff mental well-being network analysis psychological health Figures Figure 1 1. Introduction Mental well-being is a global public health concern, as it is closely linked to improved health-related quality of life and increased life expectancy [ 1 ]. Recently, there has been a growing focus on the mental well-being of employees across different professions, particularly in the medical field [ 2 , 3 ]. Medical staff, including medical professionals, are known to face various job-related stressors such as long and irregular work hours, night shifts, high work load, emotional exhaustion, continuity of care, and ethical dilemmas, all of which can have detrimental effects on their mental well-being, leading to conditions including depression, anxiety and insomnia [ 4 ]. Studies have indicated that the mental wellbeing status of Chinese medical professionals is generally unfavorable [ 5 , 6 ], with a meta-analysis highlighting a prevalence of psychological challenges among medical staff [ 7 ]. The compromised mental well-being of healthcare providers could jeopardize their professionalism, ability to empathize with patients, and commitment to the rigorous standards of medical practice [ 8 , 9 ], underscoring the importance of maintaining mental well-being of medical staff for public health [ 10 , 11 ]. Personality psychologists widely acknowledge the Big Five model as a framework encompassing five fundamental dimensions for categorizing a broad spectrum of personality traits. These dimensions, namely Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness, features the key aspects of individual differences [ 12 ]. Extraversion reflects variations in friendliness, sociability, enthusiasm, and assertiveness. Agreeableness pertains to variances in compassion, forgiveness, and friendliness. Conscientiousness involves impulse control, task focus, and self-discipline. Neuroticism relates to the susceptibility to negative emotions, such as irritability, moodiness, and vulnerability. Lastly, Openness reflects the differences in creativity, innovation, and receptiveness to novel ideas. The widespread adoption of the Big Five personality framework provides a robust and systematic approach to understanding nuanced personality distinctions at a fundamental level [ 13 ]. Personality has been proposed as a robust predictor of overall psychological health, encompassing positive mental health and well-being [ 14 ]. The healthy personality contributes significantly to various aspects of well-being, highlighting the importance of integrating personality factors into current mental health interventions [ 15 ]. Extraversion is characterized by a social, energetic, and proactive engagement with the environment; agreeableness entails a cooperative, trusting, and interpersonal approach; conscientiousness reflects an organized and industrious attitude towards tasks and goal attainment; neuroticism involves a sensitive, analytical, and anxious disposition; openness to experience represents a creative and exploratory mindset towards intellectual pursuits and real-life experiences [ 16 ]. Most studies suggest that individuals with higher Neuroticism levels may experience poorer mental well-being outcomes, while higher levels of Extraversion, Conscientiousness, and Openness may lead to better mental well-being [ 17 , 18 ]. However, research on the relationship between the Big Five personality traits and mental well-being has yielded inconsistent results. For example, most studies have reported strong positive correlations between Openness and mental well-being [ 19 , 20 ], while one study found a positive association between Openness and poorer mental well-being, noting that individuals with depression tended to have higher Openness scores than non-depressed individuals [ 21 ]. Nevertheless, a longitudinal study discovered that changes in Openness scores were not linked to the onset or recovery from depressive or anxiety disorders [ 22 ]. Additionally, a study examining the comprehensive relationship between Big Five personality traits and health indicators revealed that the effects were more pronounced for agreeableness, conscientiousness, and neuroticism compared to extraversion or openness [ 15 ]. Generally, the five-factor model of personality suggests that Neuroticism and Extraversion exhibit the strongest correlations with mental health outcomes [ 23 ]. Given these inconsistencies, further elucidation of the connections between Big Five personality traits and mental well-being is warranted. Analyzing the studies solely based on dimensions and total item scores may be contentious, as it overlooks the heterogeneity at the component/symptom level of mental well-being, which could obscure the intricate relationships between different mental well-being components and Big Five personality traits. Therefore, it is imperative to explore potential pathways between mental well-being and Big Five personality traits from a more granular, component-level perspective. Network analysis is a statistical method that models relationships between psychological constructs at the component level. In this framework, nodes represent the variables, while edges illustrate the relationships between them [ 24 , 25 ]. Network analysis offers several advantages, making it a suitable analytical approach for this study. First, previous research has used network analysis to explore the internal structures of mental well-being and the interplay between the big five personality traits and burnout among medical professionals [ 26 , 27 ]. Second, existing research predominantly focused on examining the relationship between Big Five personality traits and mental well-being at a broader construct level (via sum scores) [ 28 ]. This may ignore the unique relationships among different components of Big Five personality traits and mental well-being at a component level. Third, by employing partial correlation and regularization processes, network analysis can elucidate the connections between Big Five personality traits and mental well-being components, mitigating the challenges of over-interpretation and result reproducibility. Additionally, network analysis introduces novel indices such as "bridge expected influence (BEI)," which measures the potential influence between nodes in different communities. A node with higher positive BEI indicates greater extent for activating other communities. Conversely, a node with higher negative BEI indicates greater extent for deactivating other communities [ 29 , 30 ]. The BEI provides valuable insights for developing evidence-based interventions that target personality factors in clinical settings. For example, Liu et al. highlighted the bridging effect of Big Five personality traits on problematic smartphone use symptoms, emphasizing the significant roles of Neuroticism and Conscientiousness in prevention and intervention strategies for addressing this issue [ 31 ]. This study aimed to: (a) identify potential pathways connecting Big Five personality traits and mental well-being at the trait-to-component level; and (b) determine the ability of different traits of the Big Five personality traits to activate/deactivate mental well-being by estimating node bridge expected influence. Our goal was to enhance the understanding of the relationship between Big Five personality traits and mental well-being from a network perspective. 2. Material and Methods 2.1. Participants The survey was conducted onsite at Xijing Hospital between April 16 and 18, 2021, utilizing printed questionnaires for data acquisition. A total of 458 healthcare providers working at the participating hospital constituted the initial study cohort (Ethics No. KY20202063-F-2). All respondents provided written informed consent before completing the research instruments. Data collection commenced with basic demographic assessment. Subsequent quality checks led to the elimination of 38 responses due to either validity concerns or incomplete demographic reporting. The final dataset comprised 420 participants, yielding a sampling error of 4.8% with a 95% confidence interval. 2.2. Measures 2.2.1. Big Five personality traits The Big Five personality traits were evaluated using the Chinese Big-Five Personality Inventory-15 (CBF-PI-15), featuring five distinct subscales that measure neuroticism, conscientiousness, agreeableness, openness, and extraversion [ 27 ]. Each subscale contains three items evaluated on a six-point Likert scale, with response options spanning from "strongly disagree" (1) to "strongly agree" (6). Previous psychometric evaluations have established satisfactory measurement properties for this instrument, demonstrating adequate convergent, discriminant, and criterion-related validity. Its application in contemporary psychological research, particularly in network analytic studies, has been well documented (e.g., [ 31 , 32 ]). The reliability estimates for each personality dimension in our sample were acceptable: Neuroticism (α = 0.83), Conscientiousness (α = 0.75), Agreeableness (α = 0.70), Openness (α = 0.88), and Extraversion (α = 0.70). 2.2.2. Warwick-Edinburgh Mental Well-being Scale The 14-item Warwick-Edinburgh Mental Well-being Scale (WEMWBS) is a concise psychological assessment tool utilized for measuring mental well-being [ 33 ]. Each item is rated on a five-point Likert scale, ranging from 1 ("none of the time") to 5 ("all of the time"), with the total score calculated as the simple sum of the responses. The Chinese version of WEMWBS, employed in this study, demonstrates strong reliability and validity [ 34 ], enabling the evaluation of various aspects of mental well-being [ 35 ]. The Cronbach’s α coefficient for WEMWBS in the present investigation was 0.96. 2.3. Network analysis Before constructing the network, the goldbricker function in the R-package networktools was used to detect redundant nodes, and the results showed that there were no redundant nodes in the network. We employed the graphical Least Absolute Shrinkage and Selection Operator (LASSO) regularization technique in conjunction with the Extended Bayesian Information Criterion (EBIC) to estimate The trait-to-component network, and the hyperparameter gamma was maintained at 0.5 to balance network specificity and sensitivity [ 36 ]. In the resulting network configuration, edges represent regularized partial correlations between node pairs, indicating the strength of association between variables after controlling for all other nodes in the network [ 37 ]. Network visualization was accomplished using the Fruchterman-Reingold algorithm through the qgraph package in R, which optimizes node placement for interpretability while maintaining the underlying statistical relationships [ 38 , 39 ]. Bridge expected influence were derived using the network tools package, with bridge expected influence quantifying each node's propensity to activate or deactivate neighboring communities[ 40 ]. Positive values indicate activating potential, while negative values suggest inhibitory effects [ 40 ]. The network was partitioned into two predefined communities: the first contained five nodes representing Big Five personality traits, while the second included fourteen nodes corresponding to mental well-being components. To evaluate the stability of the estimated network, a bootstrap resampling procedure (1,000 samples) was implemented via the R package bootnet [ 41 ]. This analysis assessed the accuracy of edge weights by computing 95% confidence intervals and performing difference tests between edges. Furthermore, the stability of bridge expected influence indices was quantified using the correlation stability coefficient (CS-coefficient), derived from a case-dropping bootstrap approach, with subsequent difference tests. In line with established guidelines, a CS-coefficient above 0.50 was deemed to indicate acceptable stability, while a value below 0.25 was considered unsatisfactory [ 41 ]. 3. Results 3.1. Descriptive data analysis The analytical sample consisted of 420 healthcare professionals, including 221 nursing staff and 199 physicians. Participants ranged in age from 22 to 50 years (M = 32.74, SD = 5.37), with female respondents comprising 81.7% of the sample (n = 343). 304 people are married, 106 people are divorced or unmarried; In terms of work experience, among the participants, 135 had worked for less than or equal to 5 years, 150 had worked between 6-10 years, and 135 had worked for more than 10 years. Table 1 presents the abbreviations, mean scores, and standard deviations for the variables included in the network analysis. Table 1. Abbreviations, mean scores, standard deviations and bridge expected influence for each variable selected in the current network analysis Variables Abbr M SD BEI Traits of Big Five Personality Agreeableness Agr 15.20 2.26 0.24 Conscientiousness Con 14.47 2.46 0.37 Extraversion Ext 11.73 2.99 0.27 Neuroticism Neu 7.96 3.40 -0.38 Openness Ope 10.94 3.30 0.35 Components of Mental Well-Being I’ve been feeling optimistic about the future MW1 4.19 0.86 -0.10 I’ve been feeling useful MW2 4.18 0.83 0.09 I’ve been feeling relaxed MW3 3.71 0.97 0.05 I’ve been feeling interested in other people MW4 4.22 0.83 0.16 I’ve had energy to spare MW5 3.85 0.86 0.002 I’ve been dealing with problems well MW6 4.08 0.75 0.001 I’ve been thinking clearly MW7 4.14 0.74 0.11 I’ve been feeling good about myself MW8 3.83 0.87 0.06 I’ve been feeling close to other people MW9 3.88 0.85 0.08 I’ve been feeling confident MW10 3.78 0.91 0.05 I’ve been able to make up my own mind about things MW11 4.15 0.76 0.10 I’ve been feeling loved MW12 3.96 0.84 0.007 I’ve been interested in new things MW13 4.09 0.87 0.21 I’ve been feeling cheerful MW14 3.99 0.89 0.05 Abbreviations: Abbr, Abbreviation; M, mean; SD, standard deviation; BEI, bridge expected influence 3.2. Network analysis of Big Five personality traits and mental well-being The estimated network depicted in Figure 1 retained 32 between-community edges (45.71% of the total) with non-zero edge weights (ranging from -0.11 to 0.16) out of a possible 70 between-community edges. Detailed edge weights within the final network are provided in Table S1 (in supplementary materials). Among the 14 mental well-being components, seven exhibited negative correlations with Neuroticism (weights ranging from -0.11 to -0.004). The most pronounced negative correlation was observed between Neuroticism and Feeling optimistic about the future (MW1; edge weight = -0.11). The second-strongest negative edge connected Neuroticism to Feeling confident (MW10; edge weight = -0.10). Conscientiousness demonstrated connections with six mental well-being components (out of 14), with all the six edges exhibiting positive weights (weights ranged from 0.0005 to 0.14). The mental well-being component Feeling useful (MW2; edge weight = 0.14) exhibited the strongest positive relationship with Conscientiousness. The second strongest positive edge was observed between Conscientiousness and Thinking clearly (MW7; edge weight = 0.11). Among the fourteen mental well-being components, six edges showed positive correlations and one edge showed negative correlation with Agreeableness (weights ranging from -0.008 to 0.10). The mental well-being component Feeling cheerful (MW14; edge weight = 0.10) exhibited the strongest positive relationship with Agreeableness. Five mental well-being components (out of 14) demonstrated positive links with Openness (weights ranging from 0.02 to 0.16). The mental well-being component Interested in new things (MW13; edge weight = 0.16) exhibited the strongest positive relationship with Openness. Similarly, seven mental well-being components (out of 14) were positively associated with Extraversion, with weights ranging from 0.001 to 0.10. The edge between Extraversion and Interested in other people exhibited the strongest positive correlation (MW4; edge weight = 0.103). The second-strongest positive edge connected Extraversion and Feeling confident (MW10; edge weight = 0.102). Figure S1 (Supplementary Material) showed the bootstrapped 95% confidence intervals for edge weights. The bootstrapped difference test for edge weights was displayed in Figure S2 (Supplementary Material). Table 1 and Figure 1b illustrate the raw bridge expected influence values. Conscientiousness and Openness exhibited the highest positive bridge expected influences among all nodes (value = 0.37 and 0.35), while Neuroticism showed the highest negative bridge expected influence (value = -0.38). Figure S3 (in Supplementary Material) demonstrated the adequate stability of the bridge expected influence, with a CS-coefficient value of 0.75 exceeding 0.50. The bootstrapped difference test (Figure S4 in the Supplementary Material) revealed differences in the bridge expected influence among nodes. 4. Discussion The present study investigated network models depicting interactions among the Big Five personality traits and components of mental well-being. Our analysis revealed distinct inter-community connections, encompassing both positive and negative correlations. The results on the relationship between the Big Five personality traits and components of mental well-being has brought a more granular perspective, which is conducive to further deepening the theoretical framework. Our findings support our objective through bridge expected influence analysis, indicating that Conscientiousness, Openness, Extraversion, and Agreeableness contribute positively to the mental well-being component community, whereas Neuroticism has a detrimental effect. Understanding the relationship between the personality traits of medical staff and their mental well-being components could enable the development of more targeted psychological interventions, such as exploring more personalized intervention methods that could stimulate medical staff's optimism about the future and confidence with high neuroticism. 4.1. Relations between Big Five personality traits and mental well-being components Our analysis revealed specific associations between Big Five personality traits and mental well-being components, each potentially reflecting distinct psychosocial mechanisms. Notably, a significant negative correlation was observed between Neuroticism and Optimistic about the future (MW1). Neuroticism is defined as a dimension of maladjustment or negative emotionality in contrast to adjustment and emotional stability. In the late 1990s, researchers reached a consensus that Neuroticism fundamentally involves a predisposition to experience negative emotions [42, 43]. Previous studies have linked Neuroticism to various phenomena associated with psychological distress, such as persistent low subjective well-being and physical health issues [44, 45]. Moreover, Neuroticism has been found to impact outcomes like occupational success [46]. Consequently, it is plausible that medical staff with high levels of Neuroticism tend to experience negative emotions, exhibit lower work performance, maintain a pessimistic outlook, and lack optimism for the future. The association between Conscientiousness and the six components of mental wellbeing was consistently positive. Numerous cross-sectional studies have demonstrated that individuals with higher levels of Conscientiousness tend to experience more frequent positive affect, greater life satisfaction, and lower levels of negative affect compared to those with lower Conscientiousness [47-49]. High levels of conscientiousness are linked to better regulation of negative affect [50] and a reduced likelihood of experiencing depression and anxiety disorders [51]. A recent longitudinal study found that individuals initially high in Conscientiousness reported increased subjective well-being over time, while those with high initial levels of subjective well-being tended to become more conscientious [52]. Furthermore, the mental wellbeing component Feeling useful (MW2) showed the strongest positive correlation with Conscientiousness, highlighting the connection between self-efficacy and Conscientiousness. This finding aligns with a previous study involving nurse anesthetists, which indicated that those with high levels of Conscientiousness also exhibited high scores in self-efficacy. Regression analysis further revealed that Conscientiousness was most strongly associated with self-efficacy [53]. Most associations between Agreeableness and mental well-being, particularly with components such as Feeling cheerful (MW14), were predominantly positive. Agreeableness typically encompasses traits like cooperativeness, sympathy, tolerance, and forgiveness towards others, while avoiding competition, conflict, coercion, and aggression [54]. Notably, an intriguing inverse relationship was observed between Agreeableness and Confidence. This outcome could be linked to the unique dynamics within the medical field. A gradual erosion of the doctor-patient relationship appears to be an overarching global trend in healthcare systems. Pun et al. highlighted a deficiency in trust between doctors and patients in East Asia, including Mainland China, Japan, and South Korea [55]. Recent reports indicate a significant deterioration in doctor-patient interactions in China [56]. The 2018 White Paper on the Practice of Chinese Doctors, published by the Chinese Medical Doctor Association, revealed that 66% of doctors encountered conflicts with patients, with over 30% experiencing violence [57]. Faced with escalating tensions in doctor-patient relationships, healthcare professionals may feel compelled to exhibit increased kindness and politeness in delivering medical care to patients, which could potentially impact their confidence over time. However, further research is warranted to offer a more nuanced elucidation of this discovery. The final network analysis revealed predominantly positive connections among components within the mental well-being community. Specifically, three edges with the highest weights in the mental well-being community were identified: Dealing with problems well (MW6) - Thinking clearly (MW7), Feeling good about myself (MW8) - Feeling confident (MW10), and Feeling optimistic about the future (MW1) - Feeling useful (MW2). These findings regarding the strongest edges are consistent with our earlier research on the network structure of mental well-being among medical staff during the COVID-19 pandemic [58]. Additionally, a study investigating mental well-being in four UK cohorts using network analysis also reported significant associations between Feeling good about myself (MW8) - Feeling confident (MW10), as well as between Dealing with problems well (MW6) - Thinking clearly (MW7) [59]. 4.2. Bridge expected influence The bridge centrality analysis revealed the differential roles of personality dimensions in influencing mental well-being components. Nodes exhibiting higher bridge expected influence values demonstrated greater potential for transmitting effects across communities, thereby serving as potentially influential intervention targets. This empirical evidence supports the enhancement of medical staff mental well-being through the lens of the Big Five personality traits. Consequently, promoting medical staff mental well-being may entail diminishing the Neuroticism levels while bolstering Conscientiousness and Openness. Conscientiousness and Openness demonstrated significantly positive bridge expected influence values in the study, indicating their effective activation of the mental well-being components. The finding partly contrasted with prior study employing network analysis to explore the bridging effects of each Big Five personality trait on the symptom community of problematic smartphone use and burnout, which reported Conscientiousness as having the highest negative bridge centrality [27, 31]. Strengthening the training of medical personnel is one of the key measures to enhance their sense of responsibility. Through training, not only the medical theoretical level and clinical operational skills of medical staff could be improved, but their awareness of ethical and professional norms may also be strengthened, and their sense of social responsibility and professional ethics could be enhanced. Some innovative intervention measures are also worth paying attention to. For example, the use of psychological interventions such as cognitive-behavioral therapy and metacognitive training might have a positive impact on enhancing the conscientiousness of healthcare workers. People with high openness usually have higher creativity and willingness to learn new things, could adapt to society, understand others [60], and effectively manage conflicts, obtain good social support [61], and maintain high-quality interpersonal relationships [62]. Managers could regularly organize brainstorming meetings to encourage medical staff to propose innovative ideas, support their participation in innovative projects or research, and stimulate their creative thinking in diverse ways to enhance individual openness. Conversely, Neuroticism displays the highest negative value of bridge expected influence, suggesting its potential to deactivate the well-being components community. Individuals with elevated Neuroticism levels often endure heightened stress, tend to exaggerate the severity of threat, and compromise their overall happiness. The identification of bridge nodes provides valuable insights for clinical applications, as these nodes represent optimal targets for interventions aimed at disrupting maladaptive network dynamics and promoting psychological well-being. Encouraging healthcare professionals to choose between guided or silent meditation, or between different types of practices such as meditation or mindfulness yoga, has a positive effect on the adjustment and transformation of neuroticism. When practicing mindfulness, gradually introducing mindfulness exercises, starting with a short course and increasing the duration as participants become more comfortable, could make mindfulness-based interventions more effective [63]. 4.3. Limitations The study utilizes a novel component-based approach, specifically network analysis, to investigate the relationship between the Big Five personality traits and mental well-being components in medical staff. However, there are several notable limitations to consider. Firstly, while our theoretical framework presupposes directional influences from personality traits to mental well-being, the cross-sectional nature of our design prevents definitive causal inferences. The potential for bidirectional relationships between study variables cannot be ruled out. Secondly, the generalizability of our network structure may be constrained by our specific assessment instruments. Future investigations utilizing alternative measurement approaches would help establish the robustness of our findings. Thirdly, the exclusive reliance on self-report measures introduces the possibility of common method variance. Incorporating multi-method assessment strategies in future longitudinal designs would strengthen the validity of the observed relationships. 5. Conclusions Our study examines the association between the Big Five personality traits and components of mental well-being among medical professionals from a trait-to-component perspective. By exploring the connections between the Big Five personality traits and components of mental well-being, we can deepen our understanding of the potential mechanisms underlying the relationship between these traits and mental well-being. This investigation provides a nuanced insight into how medical staff with diverse personalities may impact mental well-being through various pathways, in which a more detailed and in-depth supplement could be made to the theoretical framework of the relationship between these two concepts. Notably, our findings suggest that addressing the mental well-being needs of medical professionals, particularly concerning Neuroticism (exhibiting the most substantial positive bridging effect) and Conscientiousness (demonstrating the most pronounced negative bridging effect), could have profound implications for clinical practice. Declarations Author Contributions The initial draft of the publication was prepared by ZRK and WYF. ZRK and RL collected all the data and prepared all the figures. WQY and CBH revised the grammar and expression of the article. CBH and RL designed the research and reviewed the manuscript. Funding This work was supported by the General projects Clinical Medicine Plus X Research Center - Research Project from CBH, Grant number LHJJ24HL03. Clinical trial number Not applicable Data availability The datasets and R-codes used in this study are not publicly available but are available from the corresponding author. Acknowledgments We would like to thank all the individuals who participated in the study. We also thank all the administrative staff and doctors in the hospital who help us with the recruitment. Ethical approval In accordance with the Declaration of Helsinki, the data collection process for Project No. KY20202063-F-2 was approved by the Ethics Committee of the First Affiliated Hospital of the Fourth Military Medical University. Consent to participate Prior to participants’ involvement, all participants gave informed consent. Consent to publish The work described has not been published previously, and is not under consideration for publication elsewhere. All authors have contributed significantly to the research and the preparation of the manuscript. The authors declare that they have no conflict of interest. References Diener E, Oishi S, Tay L. Advances in subjective well-being research. Nat Hum Behav. 2018;2(4):253–60. 10.1038/s41562-018-0307-6 . Ariza-Montes A, Mahmood F, Han H, et al. The Mental Well-Being of Health Care Professionals During the COVID-19 Pandemic. J Occup Environ Med. 2022;64(5):429–42. 10.1097/JOM.0000000000002470 . Niewiadomska E, Łabuz-Roszak B, Pawłowski P, et al. The Physical and Mental Well-Being of Medical Doctors in the Silesian Voivodeship. Int J Environ Res Public Health. 2022;19(20):13410. 10.3390/ijerph192013410 . Berlanda S, de Cordova F, Fraizzoli M, et al. Risk and protective factors of well-being among healthcare staff. A thematic analysis. Int J Environ Res Public Health. 2020;17(18):6651. 10.3390/ijerph17186651 . van Agteren J, Iasiello M, Lo L. Improving the wellbeing and resilience of health services staff via psychological skills training. BMC Res Notes. 2018;11(1):924. 10.1186/s13104-018-4034-x . Dong A, Huang J, Lin S, et al. Psychometric Properties of the Chinese Warwick-Edinburgh Mental Well-being Scale in Medical Staff: Cross-sectional Study. J Med Internet Res. 2022;24(11):e38108. 10.2196/38108 . Salazar de Pablo G, Vaquerizo-Serrano J, Catalan A, et al. Impact of coronavirus syndromes on physical and mental health of health care workers: systematic review and meta-analysis. J Affect Disord. 2020;275:48–57. 10.1016/j.jad.2020.06.022 . Ruiz-Fernández MD, Pérez-García E, Ortega-Galán ÁM. Quality of Life in Nursing Professionals: Burnout, Fatigue, and Compassion Satisfaction. Int J Environ Res Public Health. 2020;17(4):1253. 10.3390/ijerph17041253 . Dunn LB, Iglewicz A, Moutier C. A conceptual model of medical student well-being: promoting resilience and preventing burnout. Acad Psychiatry 2008 Jan-Feb;32(1):44–53. 10.1176/appi.ap.32.1.44 Ledikwe JH, Kleinman NJ, Mpho M, et al. Associations between healthcare worker participation in workplace wellness activities and job satisfaction, occupational stress and burnout: a cross-sectional study in Botswana. BMJ Open. 2018;8(3):e018492. 10.1136/bmjopen-2017-018492 . Yakes EA, Dean S, Labadie RF, et al. Factors Associated With Physician Empowerment and Well-being at an Academic Medical Center. J Occup Environ Med. 2020;62(7):478–83. 10.1097/JOM.0000000000001874 . Costa PT Jr., McCrae RR. Revised NEO personality inventory (NEO-PI-R) and the five factor inventory (NEO-FFI): Professional manual. Odessa, Florida: Psychological Assessment Resources, Inc.; 1992. Kang W, Steffens F, Pineda S, et al. Personality traits and dimensions of mental health. Sci Rep. 2023;13(1):7091. 10.1038/s41598-023-33996-1 . Conway CC, Simms LJ. Maximizing the applied value of structural models of psychopathology: Introduction to a special issue of Personality and Mental Health. Personal Ment Health. 2020;14(1):3–8. 10.1002/pmh.1474 . Strickhouser JE, Zell E, Krizan Z. Does personality predict health and well-being? A Metasynthesis. Health Psychol. 2017;36(8):797–810. 10.1037/hea0000475 . McAdams DP. The five-factor model in personality: a critical appraisal. J Pers. 1992;60(2):329–61. 10.1111/j.1467-6494.1992.tb00976.x . Anglim J, Horwood S, Smillie LD, et al. Predicting psychological and subjective well-being from personality: A meta-analysis. Psychol Bull. 2020;146(4):279–323. 10.1037/bul0000226 . Joshanloo M. Reciprocal relationships between personality traits and psychological well-being. Br J Psychol. 2023;114(1):54–69. 10.1111/bjop.12596 . Lewis EG, Cardwell JM. The big five personality traits, perfectionism and their association with mental health among UK students on professional degree programmes. BMC Psychol. 2020;8(1):1–10. 10.1186/s40359-020-00423-3 . Kang W. The associations between personality traits and mental health in people with and without asthma. J Affect Disord. 2023;333:102–6. 10.1016/j.jad.2023.04.022 . Wolfenstein M, Trull TJ. Depression and openness to experience. J Pers Assess. 1997;69:614–32. 10.1207/s15327752jpa6903_14 . Karsten J, Penninx BW, Riese H, et al. The state effect of depressive and anxiety disorders on big five personality traits. J Psychiatr Res. 2012;46(5):644–50. 10.1016/j.jpsychires.2012.01.024 . Brooks SK, Webster RK, Smith LE, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet. 2020;395(10227):912–20. 10.1016/S0140-6736(20)30460-8 . Fried EI, van Borkulo CD, Cramer AO, et al. Mental disorders as networks of problems: a review of recent insights. Soc Psychiatry Psychiatr Epidemiol. 2017;52(1):1–10. 10.1007/s00127-016-1319-z . Fried EI, Cramer AOJ. Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology. Perspect Psychol Sci. 2017;12(6):999–1020. 10.1177/1745691617705892 . Wu L, Ren L, Wang Y, et al. The item network and domain network of burnout in Chinese nurses. BMC Nurs. 2021;20(1):147. 10.1186/s12912-021-00670-8 . Wang Y, Wu L, Liu C, et al. A network analysis bridging the gap between the big five personality traits and burnout among medical staff. BMC Nurs. 2024;23(1):92. 10.1186/s12912-024-01751-0 . Hongmei Z, Jingwei Y. Investigating the Role of Personality Traits on the Mental Health of the Residents of China: How Effective is the BIG Five? Am J Health Behav. 2022;46(6):706–715. 10.5993/AJHB.46.6.13 . PMID: 36721288. Li J, Liu C, Wulandari T, et al. The relationship between dimensions of empathy and symptoms of depression among university students during the COVID-19 pandemic: A network analysis. Front Public Health. 2022;10:1034119. 10.3389/fpubh.2022.1034119 . Liang S, Liu C, Rotaru K, et al. The relations between emotion regulation, depression and anxiety among medical staff during the late stage of COVID-19 pandemic: a network analysis. Psychiatry Res. 2022;317:114863. 10.1016/j.psychres.2022.114863 . Liu C, Ren L, Rotaru K, et al. Bridging the links between Big Five personality traits and problematic smartphone use: A network analysis. J Behav Addict. 2023;12(1):128–36. 10.1556/2006.2022.00093 . Liang G, Cheng Y, Barnhart WR, et al. A network analysis of disordered eating symptoms, big-five personality traits, and psychological distress in Chinese adults. Int J Eat Disord. 2023;56(10):1842–53. 10.1002/eat.24012 . Tennant R, Hiller L, Fishwick R, et al. The Warwick-Edinburgh mental well-being scale (Wemwbs): development and UK validation. Health Qual Life Outcom. 2007;5:63. 10.1186/1477-7525-5-63 . Koushede V, Lasgaard M, Hinrichsen C, et al. Measuring mental well-being in Denmark: Validation of the original and short version of the Warwick-Edinburgh mental well-being scale (WEMWBS and SWEMWBS) and cross-cultural comparison across four European settings. Psychiatry Res. 2019;271:502–9. 10.1016/j.psychres.2018.12.003 . Fung SF. Psychometric evaluation of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) with Chinese University Students. Health Qual Life Outcomes. 2019;17(1):46. 10.1186/s12955-019-1113-1 . Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23(4):617–34. 10.1037/met0000167 . Isvoranu AM, Epskamp S. Which estimation method to choose in network psychometrics? Deriving guidelines for applied researchers. Psychol Methods. 2023;28(4):925–46. 10.1037/met0000439 . Fruchterman TMJ, Reingold EM. Graph drawing by force-directed placement. Softw Pract Experience. 2010;21(11):1129–64. 10.1002/spe.4380211102 . Epskamp S, Cramer AOJ, Waldorp LJ, et al. qgraph: Network Visualizations of Relationships in Psychometric Data. J Stat Softw. 2012;48(4):1–18. 10.18637/JSS.V048.I04 . Jones PJ, Ma R, McNally RJ. Bridge Centrality: A Network Approach to Understanding Comorbidity. Multivariate Behavioral Research. 2021 Mar-Apr;56(2):353 – 67. 10.1080/00273171.2019.1614898 Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195–212. 10.3758/s13428-017-0862-1 . Matthews G, Deary IJ, Whiteman MC. Personality traits. New York, NY: Cambridge University Press; 2003. Widiger TA. Neuroticism. In: Leary MR, Hoyle RH, editors. Handbook of individual differences in social behavior. New York; London: The Guilford Press; 2009. pp. 129–46. Heller D, Watson D, Ilies R. The role of person versus situation in life satisfaction: A critical examination. Psychol Bull. 2004;130(4):574–600. 10.1037/0033-2909.130.4.574 . Watson D. Mood and temperament. New York: Guilford Press; 2000. Lahey BB. Public health significance of neuroticism. Am Psychol. 2009;64(4):241–56. 10.1037/a0015309 . Kang W, Whelan E, Malvaso A. Understanding the Role of Cancer Diagnosis in the Associations between Personality and Life Satisfaction. Healthc (Basel). 2023;11(16):2359. 10.3390/healthcare11162359 . Smith J, Ryan LH, Röcke C. The day-to-day effects of conscientiousness on well-being. Res Hum Dev. 2013;10:9–25. 10.1080/15427609.2013.760257 . Steel P, Schmidt J, Shultz J. Refining the relationship between personality and subjective well-being. Psychol Bull. 2008;134:138–61. 10.1037/0033-2909.134.1.138 . Javaras KN, Schaefer SM, van Reekum CM, et al. Conscientiousness predicts greater recovery from negative emotion. Emotion. 2012;12(5):875–81. 10.1037/a0028105 . Kotov R, Gamez W, Schmidt F, et al. Linking big personality traits to anxiety, depressive, and substance use disorders: a meta-analysis. Psychol Bull. 2010;136(5):768–821. 10.1037/a0020327 . Soto CJ. Is happiness good for your personality? Concurrent and prospective relations of the big five with subjective well-being. J Pers. 2015;83:45–55. 10.1111/jopy.12081 . Kwiatosz-Muc M, Kotus M, Aftyka A. Personality Traits and the Sense of Self-Efficacy among Nurse Anaesthetists. Multi-Centre Questionnaire Survey. Int J Environ Res Public Health. 2021;18(17):9381. 10.3390/ijerph18179381 . McCrae RR, Costa PT Jr. Validation of the five-factor model of personality across instruments and observers. J Pers Soc Psychol. 1987;52(1):81–90. 10.1037//0022-3514.52.1.81 . Pun JKH, Chan EA, Wang S, et al. Health professional-patient communication practices in East Asia: An integrative review of an emerging field of research and practice in Hong Kong, South Korea, Japan, Taiwan, and Mainland China. Patient Educ Couns. 2018;101(7):1193–206. 10.1016/j.pec.2018.01.018 . Kaba R, Sooriakumaran P. The evolution of the doctor-patient relationship. Int J Surg. 2007;5(1):57–65. 10.1016/j.ijsu.2006.01.005 . Peng W, Ding G, Tang Q, et al. Continuing violence against medical personnel in China: A flagrant violation of Chinese law. Biosci Trends. 2016;10(3):240–3. 10.5582/bst.2016.01094 . Chen C, Li F, Liu C, et al. The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis. Front Public Health. 2022;10:919692. 10.3389/fpubh.2022.919692 . Stochl J, Soneson E, Wagner AP, et al. Identifying key targets for interventions to improve psychological wellbeing: replicable results from four UK cohorts. Psychol Med. 2019;49(14):2389–96. 10.1017/S0033291718003288 . McCrae RR. Social consequences of experiential openness. Psychol. Bull. 1996;120:323–37. 10.1037/0033-2909.120.3.323 . McCrae RR, Sutin AR. Openness to Experience. In: Leary MR, Hoyle RH, editors. Handbook of Individual Differences in Social Behavior. New York, NY, USA: Guilford; 2009. pp. 257–73. Nezlek JB, Schütz A, Schröder–Abé M, Smith VC. A cross-cultural study of relationships between daily social interaction and the five-factor model of personality. J Personal. 2011;79:811–40. 10.1111/j.1467-6494.2011.00706.x . Angarita-Osorio N, Escorihuela RM, Cañete T. The relationship between neuroticism as a personality trait and mindfulness skills: a scoping review. Front Psychol. 2024;15:1401969. 10.3++389/fpsyg.2024.1401969. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.pdf 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. 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University","correspondingAuthor":false,"prefix":"","firstName":"Yifei","middleName":"","lastName":"Wang","suffix":""},{"id":542132270,"identity":"6e194f00-4872-4cc5-9b8e-eda698377176","order_by":2,"name":"Qingyi Wang","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qingyi","middleName":"","lastName":"Wang","suffix":""},{"id":542132272,"identity":"2d91baf5-6e0a-43b7-b30b-a53aebdd26b8","order_by":3,"name":"Baohua Cao","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Baohua","middleName":"","lastName":"Cao","suffix":""},{"id":542132273,"identity":"2525fb78-4a5b-4033-ae18-2a898532d722","order_by":4,"name":"Lei 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08:18:34","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143651,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7767568/v1/83500666074dac7c089183b4.html"},{"id":95515150,"identity":"ebcc61a0-cd1e-45a5-8241-906eee8e172e","added_by":"auto","created_at":"2025-11-10 08:18:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182766,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Network structure of Big Five personality traits and mental well-being. (b) Bridge expected influence plot.\u003c/p\u003e\n\u003cp\u003eNote: Blue edges represent positive connections, red edges represent negative connections. A total description of nodes of Big Five personality traits and mental well-being components could be seen in Table 1.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7767568/v1/b97af2fc0e372d82db8c16dd.png"},{"id":98383675,"identity":"12755394-6791-465c-89da-8a9ebdda1c68","added_by":"auto","created_at":"2025-12-17 08:10:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":903456,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7767568/v1/1fbe8eb9-35f4-4ca8-9b95-9c294edebe5e.pdf"},{"id":95515148,"identity":"2709cbf5-10e8-4451-8b8e-f292340c5f8d","added_by":"auto","created_at":"2025-11-10 08:18:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2137008,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7767568/v1/6b16af5ed0ad1ac3e64165da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bridging the connections between Big Five personality traits and mental well-being among medical staff: A network analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMental well-being is a global public health concern, as it is closely linked to improved health-related quality of life and increased life expectancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recently, there has been a growing focus on the mental well-being of employees across different professions, particularly in the medical field [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Medical staff, including medical professionals, are known to face various job-related stressors such as long and irregular work hours, night shifts, high work load, emotional exhaustion, continuity of care, and ethical dilemmas, all of which can have detrimental effects on their mental well-being, leading to conditions including depression, anxiety and insomnia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies have indicated that the mental wellbeing status of Chinese medical professionals is generally unfavorable [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], with a meta-analysis highlighting a prevalence of psychological challenges among medical staff [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The compromised mental well-being of healthcare providers could jeopardize their professionalism, ability to empathize with patients, and commitment to the rigorous standards of medical practice [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], underscoring the importance of maintaining mental well-being of medical staff for public health [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePersonality psychologists widely acknowledge the Big Five model as a framework encompassing five fundamental dimensions for categorizing a broad spectrum of personality traits. These dimensions, namely Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness, features the key aspects of individual differences [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Extraversion reflects variations in friendliness, sociability, enthusiasm, and assertiveness. Agreeableness pertains to variances in compassion, forgiveness, and friendliness. Conscientiousness involves impulse control, task focus, and self-discipline. Neuroticism relates to the susceptibility to negative emotions, such as irritability, moodiness, and vulnerability. Lastly, Openness reflects the differences in creativity, innovation, and receptiveness to novel ideas. The widespread adoption of the Big Five personality framework provides a robust and systematic approach to understanding nuanced personality distinctions at a fundamental level [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePersonality has been proposed as a robust predictor of overall psychological health, encompassing positive mental health and well-being [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The healthy personality contributes significantly to various aspects of well-being, highlighting the importance of integrating personality factors into current mental health interventions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Extraversion is characterized by a social, energetic, and proactive engagement with the environment; agreeableness entails a cooperative, trusting, and interpersonal approach; conscientiousness reflects an organized and industrious attitude towards tasks and goal attainment; neuroticism involves a sensitive, analytical, and anxious disposition; openness to experience represents a creative and exploratory mindset towards intellectual pursuits and real-life experiences [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMost studies suggest that individuals with higher Neuroticism levels may experience poorer mental well-being outcomes, while higher levels of Extraversion, Conscientiousness, and Openness may lead to better mental well-being [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, research on the relationship between the Big Five personality traits and mental well-being has yielded inconsistent results. For example, most studies have reported strong positive correlations between Openness and mental well-being [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], while one study found a positive association between Openness and poorer mental well-being, noting that individuals with depression tended to have higher Openness scores than non-depressed individuals [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Nevertheless, a longitudinal study discovered that changes in Openness scores were not linked to the onset or recovery from depressive or anxiety disorders [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, a study examining the comprehensive relationship between Big Five personality traits and health indicators revealed that the effects were more pronounced for agreeableness, conscientiousness, and neuroticism compared to extraversion or openness [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Generally, the five-factor model of personality suggests that Neuroticism and Extraversion exhibit the strongest correlations with mental health outcomes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Given these inconsistencies, further elucidation of the connections between Big Five personality traits and mental well-being is warranted. Analyzing the studies solely based on dimensions and total item scores may be contentious, as it overlooks the heterogeneity at the component/symptom level of mental well-being, which could obscure the intricate relationships between different mental well-being components and Big Five personality traits. Therefore, it is imperative to explore potential pathways between mental well-being and Big Five personality traits from a more granular, component-level perspective.\u003c/p\u003e\u003cp\u003eNetwork analysis is a statistical method that models relationships between psychological constructs at the component level. In this framework, nodes represent the variables, while edges illustrate the relationships between them [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Network analysis offers several advantages, making it a suitable analytical approach for this study. First, previous research has used network analysis to explore the internal structures of mental well-being and the interplay between the big five personality traits and burnout among medical professionals [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Second, existing research predominantly focused on examining the relationship between Big Five personality traits and mental well-being at a broader construct level (via sum scores) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This may ignore the unique relationships among different components of Big Five personality traits and mental well-being at a component level. Third, by employing partial correlation and regularization processes, network analysis can elucidate the connections between Big Five personality traits and mental well-being components, mitigating the challenges of over-interpretation and result reproducibility. Additionally, network analysis introduces novel indices such as \"bridge expected influence (BEI),\" which measures the potential influence between nodes in different communities. A node with higher positive BEI indicates greater extent for activating other communities. Conversely, a node with higher negative BEI indicates greater extent for deactivating other communities [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The BEI provides valuable insights for developing evidence-based interventions that target personality factors in clinical settings. For example, Liu et al. highlighted the bridging effect of Big Five personality traits on problematic smartphone use symptoms, emphasizing the significant roles of Neuroticism and Conscientiousness in prevention and intervention strategies for addressing this issue [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aimed to: (a) identify potential pathways connecting Big Five personality traits and mental well-being at the trait-to-component level; and (b) determine the ability of different traits of the Big Five personality traits to activate/deactivate mental well-being by estimating node bridge expected influence. Our goal was to enhance the understanding of the relationship between Big Five personality traits and mental well-being from a network perspective.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants\u003c/h2\u003e\u003cp\u003eThe survey was conducted onsite at Xijing Hospital between April 16 and 18, 2021, utilizing printed questionnaires for data acquisition. A total of 458 healthcare providers working at the participating hospital constituted the initial study cohort (Ethics No. KY20202063-F-2). All respondents provided written informed consent before completing the research instruments. Data collection commenced with basic demographic assessment. Subsequent quality checks led to the elimination of 38 responses due to either validity concerns or incomplete demographic reporting. The final dataset comprised 420 participants, yielding a sampling error of 4.8% with a 95% confidence interval.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Measures\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Big Five personality traits\u003c/h2\u003e\u003cp\u003eThe Big Five personality traits were evaluated using the Chinese Big-Five Personality Inventory-15 (CBF-PI-15), featuring five distinct subscales that measure neuroticism, conscientiousness, agreeableness, openness, and extraversion [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Each subscale contains three items evaluated on a six-point Likert scale, with response options spanning from \"strongly disagree\" (1) to \"strongly agree\" (6). Previous psychometric evaluations have established satisfactory measurement properties for this instrument, demonstrating adequate convergent, discriminant, and criterion-related validity. Its application in contemporary psychological research, particularly in network analytic studies, has been well documented (e.g., [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]). The reliability estimates for each personality dimension in our sample were acceptable: Neuroticism (α\u0026thinsp;=\u0026thinsp;0.83), Conscientiousness (α\u0026thinsp;=\u0026thinsp;0.75), Agreeableness (α\u0026thinsp;=\u0026thinsp;0.70), Openness (α\u0026thinsp;=\u0026thinsp;0.88), and Extraversion (α\u0026thinsp;=\u0026thinsp;0.70).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Warwick-Edinburgh Mental Well-being Scale\u003c/h2\u003e\u003cp\u003eThe 14-item Warwick-Edinburgh Mental Well-being Scale (WEMWBS) is a concise psychological assessment tool utilized for measuring mental well-being [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Each item is rated on a five-point Likert scale, ranging from 1 (\"none of the time\") to 5 (\"all of the time\"), with the total score calculated as the simple sum of the responses. The Chinese version of WEMWBS, employed in this study, demonstrates strong reliability and validity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], enabling the evaluation of various aspects of mental well-being [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The Cronbach\u0026rsquo;s α coefficient for WEMWBS in the present investigation was 0.96.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Network analysis\u003c/h2\u003e\u003cp\u003eBefore constructing the network, the goldbricker function in the R-package networktools was used to detect redundant nodes, and the results showed that there were no redundant nodes in the network.\u003c/p\u003e\u003cp\u003eWe employed the graphical Least Absolute Shrinkage and Selection Operator (LASSO) regularization technique in conjunction with the Extended Bayesian Information Criterion (EBIC) to estimate The trait-to-component network, and the hyperparameter gamma was maintained at 0.5 to balance network specificity and sensitivity [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In the resulting network configuration, edges represent regularized partial correlations between node pairs, indicating the strength of association between variables after controlling for all other nodes in the network [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Network visualization was accomplished using the Fruchterman-Reingold algorithm through the qgraph package in R, which optimizes node placement for interpretability while maintaining the underlying statistical relationships [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBridge expected influence were derived using the network tools package, with bridge expected influence quantifying each node's propensity to activate or deactivate neighboring communities[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Positive values indicate activating potential, while negative values suggest inhibitory effects [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The network was partitioned into two predefined communities: the first contained five nodes representing Big Five personality traits, while the second included fourteen nodes corresponding to mental well-being components.\u003c/p\u003e\u003cp\u003eTo evaluate the stability of the estimated network, a bootstrap resampling procedure (1,000 samples) was implemented via the R package bootnet [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This analysis assessed the accuracy of edge weights by computing 95% confidence intervals and performing difference tests between edges. Furthermore, the stability of bridge expected influence indices was quantified using the correlation stability coefficient (CS-coefficient), derived from a case-dropping bootstrap approach, with subsequent difference tests. In line with established guidelines, a CS-coefficient above 0.50 was deemed to indicate acceptable stability, while a value below 0.25 was considered unsatisfactory [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Descriptive data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analytical sample consisted of 420 healthcare professionals, including 221 nursing staff and 199 physicians. Participants ranged in age from 22 to 50 years (M = 32.74, SD = 5.37), with female respondents comprising 81.7% of the sample (n = 343).\u0026nbsp;304 people are married, 106 people are divorced or unmarried; In terms of work experience, among the participants, 135 had worked for less than or equal to 5 years, 150 had worked between 6-10 years, and 135 had worked for more than 10 years. Table 1 presents the abbreviations, mean scores, and standard deviations for the variables included in the network analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Abbreviations, mean scores, standard deviations and bridge expected influence for each variable selected in the current network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eAbbr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003eBEI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 90.6977%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraits of Big Five Personality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eAgreeableness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eAgr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e15.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eConscientiousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eCon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e14.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eExtraversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eExt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eNeuroticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eNeu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e7.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eOpenness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eOpe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e10.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponents of Mental Well-Being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been feeling optimistic about the future\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been feeling useful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been feeling relaxed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been feeling interested in other people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve had energy to spare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been dealing with problems well\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been thinking clearly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been feeling good about myself\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been feeling close to other people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been feeling confident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been able to make up my own mind about things\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been feeling loved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been interested in new things\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.6346%;\"\u003e\n \u003cp\u003eI\u0026rsquo;ve been feeling cheerful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4618%;\"\u003e\n \u003cp\u003eMW14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4651%;\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.13621%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30233%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: Abbr, Abbreviation; M, mean; SD, standard deviation; BEI, bridge expected influence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Network analysis of Big Five personality traits and mental well-being\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe estimated network depicted in Figure 1 retained 32 between-community edges (45.71% of the total) with non-zero edge weights (ranging from -0.11 to 0.16) out of a possible 70 between-community edges. Detailed edge weights within the final network are provided in Table S1 (in supplementary materials). Among the 14 mental well-being components, seven exhibited negative correlations with Neuroticism (weights ranging from -0.11 to -0.004). The most pronounced negative correlation was observed between Neuroticism and Feeling optimistic about the future (MW1; edge weight = -0.11). The second-strongest negative edge connected Neuroticism to Feeling confident (MW10; edge weight = -0.10). Conscientiousness demonstrated connections with six mental well-being components (out of 14), with all the six edges exhibiting positive weights (weights ranged from 0.0005 to 0.14). The mental well-being component Feeling useful (MW2; edge weight = 0.14) exhibited the strongest positive relationship with Conscientiousness. The second strongest positive edge was observed between Conscientiousness and Thinking clearly (MW7; edge weight = 0.11). Among the fourteen mental well-being components, six edges showed positive correlations and one edge showed negative correlation with Agreeableness (weights ranging from -0.008 to 0.10). The mental well-being component Feeling cheerful (MW14; edge weight = 0.10) exhibited the strongest positive relationship with Agreeableness. Five mental well-being components (out of 14) demonstrated positive links with Openness (weights ranging from 0.02 to 0.16). The mental well-being component Interested in new things (MW13; edge weight = 0.16) exhibited the strongest positive relationship with Openness. Similarly, seven mental well-being components (out of 14) were positively associated with Extraversion, with weights ranging from 0.001 to 0.10. The edge between Extraversion and Interested in other people exhibited the strongest positive correlation (MW4; edge weight = 0.103). The second-strongest positive edge connected Extraversion and Feeling confident (MW10; edge weight = 0.102). Figure S1 (Supplementary Material) showed the bootstrapped 95% confidence intervals for edge weights. The bootstrapped difference test for edge weights was displayed in Figure S2 (Supplementary Material).\u003c/p\u003e\n\u003cp\u003eTable 1 and Figure 1b illustrate the raw bridge expected influence values. Conscientiousness and Openness exhibited the highest positive bridge expected influences among all nodes (value = 0.37 and 0.35), while Neuroticism showed the highest negative bridge expected influence (value = -0.38). Figure S3 (in Supplementary Material) demonstrated the adequate stability of the bridge expected influence, with a CS-coefficient value of 0.75 exceeding 0.50. The bootstrapped difference test (Figure S4 in the Supplementary Material) revealed differences in the bridge expected influence among nodes.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study investigated network models depicting interactions among the Big Five personality traits and components of mental well-being. Our analysis revealed distinct inter-community connections, encompassing both positive and negative correlations. The results on the relationship between the Big Five personality traits and components of mental well-being has brought a more granular perspective, which is conducive to further deepening the theoretical framework. Our findings support our objective through bridge expected influence analysis, indicating that Conscientiousness, Openness, Extraversion, and Agreeableness contribute positively to the mental well-being component community, whereas Neuroticism has a detrimental effect. Understanding the relationship between the personality traits of medical staff and their mental well-being components could enable the development of more targeted psychological interventions, such as exploring more personalized intervention methods that could stimulate medical staff\u0026apos;s optimism about the future and confidence with high neuroticism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1. Relations between Big Five personality traits and mental well-being components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis revealed specific associations between Big Five personality traits and mental well-being components, each potentially reflecting distinct psychosocial mechanisms. Notably, a significant negative correlation was observed between Neuroticism and Optimistic about the future (MW1). Neuroticism is defined as a dimension of maladjustment or negative emotionality in contrast to adjustment and emotional stability. In the late 1990s, researchers reached a consensus that Neuroticism fundamentally involves a predisposition to experience negative emotions [42, 43]. Previous studies have linked Neuroticism to various phenomena associated with psychological distress, such as persistent low subjective well-being and physical health issues [44, 45]. Moreover, Neuroticism has been found to impact outcomes like occupational success [46]. Consequently, it is plausible that medical staff with high levels of Neuroticism tend to experience negative emotions, exhibit lower work performance, maintain a pessimistic outlook, and lack optimism for the future.\u003c/p\u003e\n\u003cp\u003eThe association between Conscientiousness and the six components of mental wellbeing was consistently positive. Numerous cross-sectional studies have demonstrated that individuals with higher levels of Conscientiousness tend to experience more frequent positive affect, greater life satisfaction, and lower levels of negative affect compared to those with lower Conscientiousness [47-49]. High levels of conscientiousness are linked to better regulation of negative affect [50] and a reduced likelihood of experiencing depression and anxiety disorders [51]. A recent longitudinal study found that individuals initially high in Conscientiousness reported increased subjective well-being over time, while those with high initial levels of subjective well-being tended to become more conscientious [52]. Furthermore, the mental wellbeing component Feeling useful (MW2) showed the strongest positive correlation with Conscientiousness, highlighting the connection between self-efficacy and Conscientiousness. This finding aligns with a previous study involving nurse anesthetists, which indicated that those with high levels of Conscientiousness also exhibited high scores in self-efficacy. Regression analysis further revealed that Conscientiousness was most strongly associated with self-efficacy [53].\u003c/p\u003e\n\u003cp\u003eMost associations between Agreeableness and mental well-being, particularly with components such as Feeling cheerful (MW14), were predominantly positive. Agreeableness typically encompasses traits like cooperativeness, sympathy, tolerance, and forgiveness towards others, while avoiding competition, conflict, coercion, and aggression [54]. Notably, an intriguing inverse relationship was observed between Agreeableness and Confidence. This outcome could be linked to the unique dynamics within the medical field. A gradual erosion of the doctor-patient relationship appears to be an overarching global trend in healthcare systems. Pun et al. highlighted a deficiency in trust between doctors and patients in East Asia, including Mainland China, Japan, and South Korea [55]. Recent reports indicate a significant deterioration in doctor-patient interactions in China [56]. The 2018 White Paper on the Practice of Chinese Doctors, published by the Chinese Medical Doctor Association, revealed that 66% of doctors encountered conflicts with patients, with over 30% experiencing violence [57]. Faced with escalating tensions in doctor-patient relationships, healthcare professionals may feel compelled to exhibit increased kindness and politeness in delivering medical care to patients, which could potentially impact their confidence over time. However, further research is warranted to offer a more nuanced elucidation of this discovery.\u003c/p\u003e\n\u003cp\u003eThe final network analysis revealed predominantly positive connections among components within the mental well-being community. Specifically, three edges with the highest weights in the mental well-being community were identified: Dealing with problems well (MW6) - Thinking clearly (MW7), Feeling good about myself (MW8) - Feeling confident (MW10), and Feeling optimistic about the future (MW1) - Feeling useful (MW2). These findings regarding the strongest edges are consistent with our earlier research on the network structure of mental well-being among medical staff during the COVID-19 pandemic [58]. Additionally, a study investigating mental well-being in four UK cohorts using network analysis also reported significant associations between Feeling good about myself (MW8) - Feeling confident (MW10), as well as between Dealing with problems well (MW6) - Thinking clearly (MW7) [59].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. Bridge expected influence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bridge centrality analysis revealed the differential roles of personality dimensions in influencing mental well-being components. Nodes exhibiting higher bridge expected influence values demonstrated greater potential for transmitting effects across communities, thereby serving as potentially influential intervention targets.\u0026nbsp;This empirical evidence supports the enhancement of medical staff mental well-being through the lens of the Big Five personality traits. Consequently, promoting medical staff mental well-being may entail diminishing the Neuroticism levels while bolstering Conscientiousness and Openness.\u003c/p\u003e\n\u003cp\u003eConscientiousness and Openness demonstrated significantly positive bridge expected influence values in the study, indicating their effective activation of the mental well-being components. The finding partly contrasted with prior study employing network analysis to explore the bridging effects of each Big Five personality trait on the symptom community of problematic smartphone use and burnout, which reported Conscientiousness as having the highest negative bridge centrality [27, 31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStrengthening the training of medical personnel is one of the key measures to enhance their sense of responsibility. Through training, not only the medical theoretical level and clinical operational skills of medical staff could be improved, but their awareness of ethical and professional norms may also be strengthened, and their sense of social responsibility and professional ethics could be enhanced. Some innovative intervention measures are also worth paying attention to. For example, the use of psychological interventions such as cognitive-behavioral therapy and metacognitive training might have a positive impact on enhancing the conscientiousness of healthcare workers. People with high openness usually have higher creativity and willingness to learn new things, could adapt to society, understand others [60], and effectively manage conflicts, obtain good social support [61], and maintain high-quality interpersonal relationships [62]. Managers could regularly organize brainstorming meetings to encourage medical staff to propose innovative ideas, support their participation in innovative projects or research, and stimulate their creative thinking in diverse ways to enhance individual openness.\u003c/p\u003e\n\u003cp\u003eConversely, Neuroticism displays the highest negative value of bridge expected influence, suggesting its potential to deactivate the well-being components community. Individuals with elevated Neuroticism levels often endure heightened stress, tend to exaggerate the severity of threat, and compromise their overall happiness.\u0026nbsp;The identification of bridge nodes provides valuable insights for clinical applications, as these nodes represent optimal targets for interventions aimed at disrupting maladaptive network dynamics and promoting psychological well-being.\u0026nbsp;Encouraging healthcare professionals to choose between guided or silent meditation, or between different types of practices such as meditation or mindfulness yoga, has a positive effect on the adjustment and transformation of neuroticism. When practicing mindfulness, gradually introducing mindfulness exercises, starting with a short course and increasing the duration as participants become more comfortable, could make mindfulness-based interventions more effective [63].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3. Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study utilizes a novel component-based approach, specifically network analysis, to investigate the relationship between the Big Five personality traits and mental well-being components in medical staff. However, there are several notable limitations to consider. Firstly, while our theoretical framework presupposes directional influences from personality traits to mental well-being, the cross-sectional nature of our design prevents definitive causal inferences. The potential for bidirectional relationships between study variables cannot be ruled out. Secondly, the generalizability of our network structure may be constrained by our specific assessment instruments. Future investigations utilizing alternative measurement approaches would help establish the robustness of our findings. Thirdly, the exclusive reliance on self-report measures introduces the possibility of common method variance. Incorporating multi-method assessment strategies in future longitudinal designs would strengthen the validity of the observed relationships.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur study examines the association between the Big Five personality traits and components of mental well-being among medical professionals from a trait-to-component perspective. By exploring the connections between the Big Five personality traits and components of mental well-being, we can deepen our understanding of the potential mechanisms underlying the relationship between these traits and mental well-being. This investigation provides a nuanced insight into how medical staff with diverse personalities may impact mental well-being through various pathways, in which a more detailed and in-depth supplement could be made to the theoretical framework of the relationship between these two concepts. Notably, our findings suggest that addressing the mental well-being needs of medical professionals, particularly concerning Neuroticism (exhibiting the most substantial positive bridging effect) and Conscientiousness (demonstrating the most pronounced negative bridging effect), could have profound implications for clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial draft of the publication was prepared by ZRK and WYF. ZRK and RL collected all the data and prepared all the figures. WQY and CBH revised the grammar and expression of the article. CBH and RL designed the research and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the General projects Clinical Medicine Plus X Research Center - Research Project from CBH, Grant number LHJJ24HL03.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets and R-codes used in this study are not publicly available\u003c/p\u003e\n\u003cp\u003ebut are available from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the individuals who participated in the study. We also thank all the administrative staff and doctors in the hospital who help us with the recruitment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with the Declaration of Helsinki, the data collection process for Project No. KY20202063-F-2 was approved by the Ethics Committee of the First Affiliated Hospital of the Fourth Military Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to participants\u0026rsquo; involvement, all participants gave informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work described has not been published previously, and is not under consideration for publication elsewhere.\u003c/p\u003e\n\u003cp\u003eAll authors have contributed significantly to the research and the preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDiener E, Oishi S, Tay L. Advances in subjective well-being research. Nat Hum Behav. 2018;2(4):253\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41562-018-0307-6\u003c/span\u003e\u003cspan address=\"10.1038/s41562-018-0307-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAriza-Montes A, Mahmood F, Han H, et al. The Mental Well-Being of Health Care Professionals During the COVID-19 Pandemic. J Occup Environ Med. 2022;64(5):429\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/JOM.0000000000002470\u003c/span\u003e\u003cspan address=\"10.1097/JOM.0000000000002470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiewiadomska E, Łabuz-Roszak B, Pawłowski P, et al. The Physical and Mental Well-Being of Medical Doctors in the Silesian Voivodeship. Int J Environ Res Public Health. 2022;19(20):13410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph192013410\u003c/span\u003e\u003cspan address=\"10.3390/ijerph192013410\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerlanda S, de Cordova F, Fraizzoli M, et al. Risk and protective factors of well-being among healthcare staff. A thematic analysis. Int J Environ Res Public Health. 2020;17(18):6651. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph17186651\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17186651\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Agteren J, Iasiello M, Lo L. Improving the wellbeing and resilience of health services staff via psychological skills training. BMC Res Notes. 2018;11(1):924. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13104-018-4034-x\u003c/span\u003e\u003cspan address=\"10.1186/s13104-018-4034-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong A, Huang J, Lin S, et al. Psychometric Properties of the Chinese Warwick-Edinburgh Mental Well-being Scale in Medical Staff: Cross-sectional Study. J Med Internet Res. 2022;24(11):e38108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/38108\u003c/span\u003e\u003cspan address=\"10.2196/38108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalazar de Pablo G, Vaquerizo-Serrano J, Catalan A, et al. Impact of coronavirus syndromes on physical and mental health of health care workers: systematic review and meta-analysis. J Affect Disord. 2020;275:48\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2020.06.022\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2020.06.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuiz-Fern\u0026aacute;ndez MD, P\u0026eacute;rez-Garc\u0026iacute;a E, Ortega-Gal\u0026aacute;n \u0026Aacute;M. Quality of Life in Nursing Professionals: Burnout, Fatigue, and Compassion Satisfaction. Int J Environ Res Public Health. 2020;17(4):1253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph17041253\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17041253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDunn LB, Iglewicz A, Moutier C. A conceptual model of medical student well-being: promoting resilience and preventing burnout. Acad Psychiatry 2008 Jan-Feb;32(1):44\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1176/appi.ap.32.1.44\u003c/span\u003e\u003cspan address=\"10.1176/appi.ap.32.1.44\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLedikwe JH, Kleinman NJ, Mpho M, et al. Associations between healthcare worker participation in workplace wellness activities and job satisfaction, occupational stress and burnout: a cross-sectional study in Botswana. BMJ Open. 2018;8(3):e018492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2017-018492\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2017-018492\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYakes EA, Dean S, Labadie RF, et al. Factors Associated With Physician Empowerment and Well-being at an Academic Medical Center. J Occup Environ Med. 2020;62(7):478\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/JOM.0000000000001874\u003c/span\u003e\u003cspan address=\"10.1097/JOM.0000000000001874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCosta PT Jr., McCrae RR. Revised NEO personality inventory (NEO-PI-R) and the five factor inventory (NEO-FFI): Professional manual. Odessa, Florida: Psychological Assessment Resources, Inc.; 1992.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang W, Steffens F, Pineda S, et al. Personality traits and dimensions of mental health. Sci Rep. 2023;13(1):7091. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-33996-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-33996-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConway CC, Simms LJ. Maximizing the applied value of structural models of psychopathology: Introduction to a special issue of Personality and Mental Health. Personal Ment Health. 2020;14(1):3\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/pmh.1474\u003c/span\u003e\u003cspan address=\"10.1002/pmh.1474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStrickhouser JE, Zell E, Krizan Z. Does personality predict health and well-being? A Metasynthesis. Health Psychol. 2017;36(8):797\u0026ndash;810. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/hea0000475\u003c/span\u003e\u003cspan address=\"10.1037/hea0000475\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcAdams DP. The five-factor model in personality: a critical appraisal. J Pers. 1992;60(2):329\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1467-6494.1992.tb00976.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-6494.1992.tb00976.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnglim J, Horwood S, Smillie LD, et al. Predicting psychological and subjective well-being from personality: A meta-analysis. Psychol Bull. 2020;146(4):279\u0026ndash;323. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/bul0000226\u003c/span\u003e\u003cspan address=\"10.1037/bul0000226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoshanloo M. Reciprocal relationships between personality traits and psychological well-being. Br J Psychol. 2023;114(1):54\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/bjop.12596\u003c/span\u003e\u003cspan address=\"10.1111/bjop.12596\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLewis EG, Cardwell JM. The big five personality traits, perfectionism and their association with mental health among UK students on professional degree programmes. BMC Psychol. 2020;8(1):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40359-020-00423-3\u003c/span\u003e\u003cspan address=\"10.1186/s40359-020-00423-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang W. The associations between personality traits and mental health in people with and without asthma. J Affect Disord. 2023;333:102\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2023.04.022\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2023.04.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWolfenstein M, Trull TJ. Depression and openness to experience. J Pers Assess. 1997;69:614\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1207/s15327752jpa6903_14\u003c/span\u003e\u003cspan address=\"10.1207/s15327752jpa6903_14\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarsten J, Penninx BW, Riese H, et al. The state effect of depressive and anxiety disorders on big five personality traits. J Psychiatr Res. 2012;46(5):644\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jpsychires.2012.01.024\u003c/span\u003e\u003cspan address=\"10.1016/j.jpsychires.2012.01.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrooks SK, Webster RK, Smith LE, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet. 2020;395(10227):912\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(20)30460-8\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30460-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFried EI, van Borkulo CD, Cramer AO, et al. Mental disorders as networks of problems: a review of recent insights. Soc Psychiatry Psychiatr Epidemiol. 2017;52(1):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00127-016-1319-z\u003c/span\u003e\u003cspan address=\"10.1007/s00127-016-1319-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFried EI, Cramer AOJ. Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology. Perspect Psychol Sci. 2017;12(6):999\u0026ndash;1020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1745691617705892\u003c/span\u003e\u003cspan address=\"10.1177/1745691617705892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu L, Ren L, Wang Y, et al. The item network and domain network of burnout in Chinese nurses. BMC Nurs. 2021;20(1):147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12912-021-00670-8\u003c/span\u003e\u003cspan address=\"10.1186/s12912-021-00670-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Wu L, Liu C, et al. A network analysis bridging the gap between the big five personality traits and burnout among medical staff. BMC Nurs. 2024;23(1):92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12912-024-01751-0\u003c/span\u003e\u003cspan address=\"10.1186/s12912-024-01751-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHongmei Z, Jingwei Y. Investigating the Role of Personality Traits on the Mental Health of the Residents of China: How Effective is the BIG Five? Am J Health Behav. 2022;46(6):706\u0026ndash;715. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5993/AJHB.46.6.13\u003c/span\u003e\u003cspan address=\"10.5993/AJHB.46.6.13\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36721288.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Liu C, Wulandari T, et al. The relationship between dimensions of empathy and symptoms of depression among university students during the COVID-19 pandemic: A network analysis. Front Public Health. 2022;10:1034119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2022.1034119\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.1034119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang S, Liu C, Rotaru K, et al. The relations between emotion regulation, depression and anxiety among medical staff during the late stage of COVID-19 pandemic: a network analysis. Psychiatry Res. 2022;317:114863. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psychres.2022.114863\u003c/span\u003e\u003cspan address=\"10.1016/j.psychres.2022.114863\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu C, Ren L, Rotaru K, et al. Bridging the links between Big Five personality traits and problematic smartphone use: A network analysis. J Behav Addict. 2023;12(1):128\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1556/2006.2022.00093\u003c/span\u003e\u003cspan address=\"10.1556/2006.2022.00093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang G, Cheng Y, Barnhart WR, et al. A network analysis of disordered eating symptoms, big-five personality traits, and psychological distress in Chinese adults. Int J Eat Disord. 2023;56(10):1842\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/eat.24012\u003c/span\u003e\u003cspan address=\"10.1002/eat.24012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTennant R, Hiller L, Fishwick R, et al. The Warwick-Edinburgh mental well-being scale (Wemwbs): development and UK validation. Health Qual Life Outcom. 2007;5:63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1477-7525-5-63\u003c/span\u003e\u003cspan address=\"10.1186/1477-7525-5-63\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoushede V, Lasgaard M, Hinrichsen C, et al. Measuring mental well-being in Denmark: Validation of the original and short version of the Warwick-Edinburgh mental well-being scale (WEMWBS and SWEMWBS) and cross-cultural comparison across four European settings. Psychiatry Res. 2019;271:502\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psychres.2018.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.psychres.2018.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFung SF. Psychometric evaluation of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) with Chinese University Students. Health Qual Life Outcomes. 2019;17(1):46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12955-019-1113-1\u003c/span\u003e\u003cspan address=\"10.1186/s12955-019-1113-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23(4):617\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/met0000167\u003c/span\u003e\u003cspan address=\"10.1037/met0000167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIsvoranu AM, Epskamp S. Which estimation method to choose in network psychometrics? Deriving guidelines for applied researchers. Psychol Methods. 2023;28(4):925\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/met0000439\u003c/span\u003e\u003cspan address=\"10.1037/met0000439\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFruchterman TMJ, Reingold EM. Graph drawing by force-directed placement. Softw Pract Experience. 2010;21(11):1129\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/spe.4380211102\u003c/span\u003e\u003cspan address=\"10.1002/spe.4380211102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp S, Cramer AOJ, Waldorp LJ, et al. qgraph: Network Visualizations of Relationships in Psychometric Data. J Stat Softw. 2012;48(4):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18637/JSS.V048.I04\u003c/span\u003e\u003cspan address=\"10.18637/JSS.V048.I04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones PJ, Ma R, McNally RJ. Bridge Centrality: A Network Approach to Understanding Comorbidity. Multivariate Behavioral Research. 2021 Mar-Apr;56(2):353\u0026thinsp;\u0026ndash;\u0026thinsp;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/00273171.2019.1614898\u003c/span\u003e\u003cspan address=\"10.1080/00273171.2019.1614898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3758/s13428-017-0862-1\u003c/span\u003e\u003cspan address=\"10.3758/s13428-017-0862-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatthews G, Deary IJ, Whiteman MC. Personality traits. New York, NY: Cambridge University Press; 2003.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWidiger TA. Neuroticism. In: Leary MR, Hoyle RH, editors. Handbook of individual differences in social behavior. New York; London: The Guilford Press; 2009. pp. 129\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeller D, Watson D, Ilies R. The role of person versus situation in life satisfaction: A critical examination. Psychol Bull. 2004;130(4):574\u0026ndash;600. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/0033-2909.130.4.574\u003c/span\u003e\u003cspan address=\"10.1037/0033-2909.130.4.574\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatson D. Mood and temperament. New York: Guilford Press; 2000.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLahey BB. Public health significance of neuroticism. Am Psychol. 2009;64(4):241\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/a0015309\u003c/span\u003e\u003cspan address=\"10.1037/a0015309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang W, Whelan E, Malvaso A. Understanding the Role of Cancer Diagnosis in the Associations between Personality and Life Satisfaction. Healthc (Basel). 2023;11(16):2359. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/healthcare11162359\u003c/span\u003e\u003cspan address=\"10.3390/healthcare11162359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith J, Ryan LH, R\u0026ouml;cke C. The day-to-day effects of conscientiousness on well-being. Res Hum Dev. 2013;10:9\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/15427609.2013.760257\u003c/span\u003e\u003cspan address=\"10.1080/15427609.2013.760257\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSteel P, Schmidt J, Shultz J. Refining the relationship between personality and subjective well-being. Psychol Bull. 2008;134:138\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/0033-2909.134.1.138\u003c/span\u003e\u003cspan address=\"10.1037/0033-2909.134.1.138\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJavaras KN, Schaefer SM, van Reekum CM, et al. Conscientiousness predicts greater recovery from negative emotion. Emotion. 2012;12(5):875\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/a0028105\u003c/span\u003e\u003cspan address=\"10.1037/a0028105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKotov R, Gamez W, Schmidt F, et al. Linking big personality traits to anxiety, depressive, and substance use disorders: a meta-analysis. Psychol Bull. 2010;136(5):768\u0026ndash;821. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/a0020327\u003c/span\u003e\u003cspan address=\"10.1037/a0020327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoto CJ. Is happiness good for your personality? Concurrent and prospective relations of the big five with subjective well-being. J Pers. 2015;83:45\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jopy.12081\u003c/span\u003e\u003cspan address=\"10.1111/jopy.12081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwiatosz-Muc M, Kotus M, Aftyka A. Personality Traits and the Sense of Self-Efficacy among Nurse Anaesthetists. Multi-Centre Questionnaire Survey. Int J Environ Res Public Health. 2021;18(17):9381. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph18179381\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18179381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCrae RR, Costa PT Jr. Validation of the five-factor model of personality across instruments and observers. J Pers Soc Psychol. 1987;52(1):81\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037//0022-3514.52.1.81\u003c/span\u003e\u003cspan address=\"10.1037//0022-3514.52.1.81\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePun JKH, Chan EA, Wang S, et al. Health professional-patient communication practices in East Asia: An integrative review of an emerging field of research and practice in Hong Kong, South Korea, Japan, Taiwan, and Mainland China. Patient Educ Couns. 2018;101(7):1193\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pec.2018.01.018\u003c/span\u003e\u003cspan address=\"10.1016/j.pec.2018.01.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaba R, Sooriakumaran P. The evolution of the doctor-patient relationship. Int J Surg. 2007;5(1):57\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijsu.2006.01.005\u003c/span\u003e\u003cspan address=\"10.1016/j.ijsu.2006.01.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng W, Ding G, Tang Q, et al. Continuing violence against medical personnel in China: A flagrant violation of Chinese law. Biosci Trends. 2016;10(3):240\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5582/bst.2016.01094\u003c/span\u003e\u003cspan address=\"10.5582/bst.2016.01094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen C, Li F, Liu C, et al. The relations between mental well-being and burnout in medical staff during the COVID-19 pandemic: A network analysis. Front Public Health. 2022;10:919692. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2022.919692\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.919692\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStochl J, Soneson E, Wagner AP, et al. Identifying key targets for interventions to improve psychological wellbeing: replicable results from four UK cohorts. Psychol Med. 2019;49(14):2389\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S0033291718003288\u003c/span\u003e\u003cspan address=\"10.1017/S0033291718003288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCrae RR. Social consequences of experiential openness. Psychol. Bull. 1996;120:323\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/0033-2909.120.3.323\u003c/span\u003e\u003cspan address=\"10.1037/0033-2909.120.3.323\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCrae RR, Sutin AR. Openness to Experience. In: Leary MR, Hoyle RH, editors. Handbook of Individual Differences in Social Behavior. New York, NY, USA: Guilford; 2009. pp. 257\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNezlek JB, Sch\u0026uuml;tz A, Schr\u0026ouml;der\u0026ndash;Ab\u0026eacute; M, Smith VC. A cross-cultural study of relationships between daily social interaction and the five-factor model of personality. J Personal. 2011;79:811\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1467-6494.2011.00706.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-6494.2011.00706.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAngarita-Osorio N, Escorihuela RM, Ca\u0026ntilde;ete T. The relationship between neuroticism as a personality trait and mindfulness skills: a scoping review. Front Psychol. 2024;15:1401969. 10.3++389/fpsyg.2024.1401969.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Big Five personality traits, medical staff, mental well-being, network analysis, psychological health","lastPublishedDoi":"10.21203/rs.3.rs-7767568/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7767568/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMedical staff experience various stresses that significantly impact their mental well-being. While the Big Five personality traits (Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness) have been linked to mental well-being, most studies have focus on the overall construct level. Research examining the connections between these traits and mental-being at a component level is currently lacking.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eThe Big Five personality traits and mental well-being components of 420 medical staff were assessed using the Chinese Big-Five Personality Inventory-15 and the 14-item Warwick-Edinburgh Mental Well-being Scale. Through network analysis, we examined the distinct connections between different Big Five personality traits and mental well-being components. Additionally, we used bridge centrality indexes to pinpoint the bridging effects of each Big Five personality traits on the mental well-being components community.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e\u003cp\u003eThere are distinct positive connections (e.g., Conscientiousness - Feeling Useful, Openness - Feeling interested in new things) and negative connection (e.g., Neuroticism - Feeling optimistic about future) between different dimensions of the Big Five personality traits and mental well-being components. Conscientiousness exhibited the highest positive bridging effects on the mental well-being components community, while Neuroticism showed the highest negative bridging effects.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThese findings enhance current knowledge by elucidating the potential pathways between the Big Five personality traits and mental well-being components, providing novel insights for reassessing targets and developing intervention strategies to improve the mental health of medical staff in challenging medical settings.\u003c/p\u003e","manuscriptTitle":"Bridging the connections between Big Five personality traits and mental well-being among medical staff: A network analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 08:18:29","doi":"10.21203/rs.3.rs-7767568/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":"8bef24e0-db8c-44b3-adc4-cd09417cc17e","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T08:10:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 08:18:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7767568","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7767568","identity":"rs-7767568","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}