Latent Profile Analysis of Clinical Nurses’ Self-Concept Clarity and Its Influencing Factors—A Cross-Sectional Study

Latent Profile Analysis of Clinical Nurses’ Self-Concept Clarity and Its Influencing Factors—A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Latent Profile Analysis of Clinical Nurses’ Self-Concept Clarity and Its Influencing Factors—A Cross-Sectional Study Yao Lingyu, Liu Yuan, Chen Feifei, Cui Xia, Wang Xiaoyun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8277474/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Nurses’ self-concept clarity can influence their mental health and professional values significantly. The research on self-concept clarity has primarily assessed individuals' levels of self-concept clarity on the basis of scale scores but has largely overlooked individual heterogeneity. Aims To investigate the potential categories and differences in self-concept clarity characteristics among clinical nurses, analyze the relevant influencing factors, and provide insights for the development of theoretical models and intervention strategies. Methods A convenience sampling method was used to recruit 448 clinical nurses from a tertiary hospital in Shandong Province between July and October 2025. A latent profile analysis (LPA) was employed to identify distinct profiles of self-concept clarity among nurses, and the Big Five Personality Scale was administered to assess their personality traits. A univariate analysis and a logistic regression were conducted to examine the influencing factors. Results On the basis of their self-concept clarity characteristics, the 448 nurses were classified into four groups: “low overt–high covert” (12.28%), “stable high level” (25.89%), “high overt–low covert” (2.01%), and “stable medium–low level” (59.82%). Nurses with higher levels of agreeableness were more likely to belong to the “stable medium–low level” group ( OR = 0.804, P < 0.05) compared with the “low overt-high covert” group. Nurses who exhibited higher levels of extraversion, agreeableness, and rigor were more likely to be assigned to the “stable high level” group ( OR = 1.138, 1.155, 1.430; all P < 0.05). Conversely, greater neuroticism was associated significantly with the “stable medium–low level” group ( OR = 0.534; P < 0.05). Furthermore, nurses with high levels of openness and rigor were more likely to be classified into the “high overt–low covert” group ( OR = 1.400, 1.506; P < 0.05). Conclusion Clinical nurses exhibit distinct variations in self-concept clarity. Accordingly, managers should implement targeted intervention strategies that consider individual personality traits, thereby enhancing nurses’ self-concept clarity. Such measures are critical for advancing the psychological well-being of nurses and ensuring the stability and development of nursing teams. Self-concept clarity Personality traits Latent profile analysis Root cause analysis Nurse Figures Figure 1 Background Self-concept clarity (SCC) refers to the degree to which an individual's self-beliefs are well defined, confidently expressed, internally consistent, and temporally stable, thereby reflecting a highly organized self-schema 1,2,3 . SCC is widely regarded as both a stable, positive self-perception and a valuable individual resource 4 . The research has consistently demonstrated that SCC functions as a protective factor that mitigates negative emotions and enhances coping ability 5 , thereby improving overall psychological well-being 6 . For example, individuals with higher SCC levels consistently report higher levels of subjective well-being 7 , self-efficacy 8 , and a sense of meaning in life 9 , whereas those with lower SCC levels are more likely to experience negative emotions such as anxiety 10 , depression, and loneliness 11 , with greater obstacles to personal growth and development. Personality traits are intricately linked to SCC. The research has demonstrated that a perfectionistic personality is a significant negative predictor of SCC and that SCC is correlated negatively with neuroticism 12,13 . In contrast, individuals with higher SCC levels tend to maintain more stable personality traits and emotional states, exhibit more social engagement, and receive more social support 14,15 , all of which contribute to positive physical and mental health outcomes. Most of the SCC research has focused on university students and nursing interns, and relatively few studies have investigated qualified nurses. Additionally, many investigations have assessed SCC solely via total scale scores 1, 16,17 , focusing primarily on mediating effects while neglecting the variability in responses to individual items. SCC is a critical positive psychological resource, particularly for individuals in high-stress professions 1 . In the case of clinical nurses, SCC transcends mere self-awareness to foster a distinct and robust professional identity. The nursing role is inherently multifaceted and is often characterized by ambiguity in responsibilities, significant emotional labor, and recurrent exposure to human suffering and ethical challenges 18 . Nurses with high SCC are better equipped to navigate these challenges by maintaining a stable sense of self, dissociating personal worth from professional setbacks, and making decisions consistent with their core values. In contrast, low SCC levels may predispose nurses to internalize stress, potentially culminating in emotional exhaustion, depersonalization, and a diminished sense of personal accomplishment, which are the principal dimensions of burnout 19 . The research has established a significant association between an inadequately defined self-concept and adverse mental health and professional outcomes among health care populations 20 . Therefore, investigating SCC is not simply the examination of a personality trait but rather an exploration of a fundamental protective factor buffer against occupational hazards that affect both nurses’ well-being and the quality of patient care. Historically, investigations into SCC and its correlates within nursing have relied primarily on variable-centered approaches (e.g., regression models). These methods are invaluable for identifying average relationships between variables across an entire population; for example, they have revealed that SCC, as a global factor, is related inversely to burnout 21 . However, many existing approaches have assumed a homogeneous population, potentially obscuring significant heterogeneity. The nursing population may not be monolithic with respect to SCC. Instead, distinct subgroups or latent profiles may exist among nurses, each characterized by unique configurations of SCC. For example, one subgroup might exhibit high clarity across all domains, whereas another could display clarity in clinical competence alongside ambiguity in work–life boundaries. Variable-centered analytic methods typically average these diverse profiles, thereby concealing their unique characteristics and potentially differential relationships with outcome variables such as job satisfaction or turnover intention. This limitation underscores the need to employ person-centered analytic techniques, such as latent profile analysis (LPA), in this context 22 . Latent profile analysis (LPA) is a person-centered, probability-based modeling approach employed to identify and explore distinct subpopulations within a larger group. This method classifies individuals who exhibit similar response patterns into discrete subgroups, thereby maximizing differences between groups while minimizing variability within groups 23,24, 25 . This method is particularly well suited for capturing the nuanced manifestations of SCC among clinical nurses. To date, no studies have employed LPA to model the latent classes of nurses’ SCC. Therefore, this study uses LPA to examine nurses’ overall performance on SCC items at the individual level, classify them accordingly, identify risk factors associated with distinct SCC types, and explore the relationships between SCC types and personality traits. The aim is to provide insights into enhancing nurses’ SCC and self-awareness and to inform the development of relevant theoretical models and intervention strategies. This study seeks to address these identified research gaps by (1) employing LPA to identify distinct profiles of clinical nurses on the basis of their SCC scores and (2) examining the extent to which demographic (e.g., age and years of experience) and personality factors predict profile membership. The findings of this research are expected to provide a nuanced understanding of the psychological landscape within the nursing workforce, thereby facilitating the development of tailored interventions and support strategies for specific subgroups 26 . Ultimately, this work aims to promote improved mental health and retention among clinical nurses amid global nursing shortages 27 . Methods Study design This study utilized a cross-sectional observational design. Participants Between July and October 2025, a total of 448 clinical nurses from a tertiary hospital were recruited through convenience sampling. The inclusion criteria were as follows: (1) at least 18 years old, (2) a registered nurse, (3) at least 1 year of work experience, and (4) voluntary participation with informed consent. The exclusion criteria were as follows: (1) nurses on leave and (2) nurses who were not directly involved in clinical care. Sample Size In accordance with Kendall's standard for sample size calculation 28 , the number of participants should be 10–20 times the number of analytical variables. In this study, the variables included ten items of general information, one SCC dimension, and five personality dimensions, for which the sample size was estimated to range from 160–320. To account for potential errors, an additional 20% increase was applied. Ultimately, 469 questionnaires were distributed, and 448 valid responses were received, for an effective response rate of 95.52%. Data Collection The researchers utilized Questionnaire Star to distribute a standardized questionnaire link to the nursing staff. During data collection, a consistent set of instructions was provided to explain the study's purpose, research significance, completion procedures, and key precautions. Participation was voluntary, with informed consent obtained from all nurses prior to completing the survey. Each participant completed the questionnaire only once, and only those responses with completion times exceeding three minutes were retained to ensure thoughtful engagement. To further enhance data quality, two independent reviewers conducted questionnaire screening, and all the data were double-entered into Excel to minimize input errors. Measurements Demographic and disease-related questionnaire A self-designed questionnaire was used to collect data on demographic and professional variables, including age, gender, marital status, number of children, health status, monthly income, employment type, educational level, professional title, and work experience. The participants were registered nurses representing a range of clinical ranks—junior, intermediate and senior—as classified by the Chinese nursing career ladder. Self-Concept Clarity Scale The Self-Concept Clarity Scale (SCCS), developed by Campbell et al. (1990) 29 , comprises 12 items rated on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). All items except 6 and 11 are reverse scored, with higher total scores reflecting greater SCC levels. In the present study, the scale exhibited good internal consistency, as evidenced by a Cronbach’s α of 0.844. Chinese Version of the Ten-Item Personality Inventory The Big Five personality traits were assessed using the Chinese version of the Ten-Item Personality Inventory (TIPI–C), which was originally developed by Gosling et al. (2003) 30 and subsequently revised by Li 31 . This instrument consists of 10 items that assess five personality dimensions: Neuroticism (N), Extraversion (E), Openness (O), Agreeableness (A), and Conscientiousness (C). Items 2, 4, 6, 8, and 10 are reverse scored to ensure accurate measurement of the intended constructs. The responses are measured on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), with higher scores indicating the stronger expression of the corresponding personality trait. In this study, the overall Cronbach’s α for the TIPI–C was 0.673, with the subscale coefficients as follows: 0.629 for Neuroticism (N), 0.712 for Extraversion (E), 0.591 for Openness (O), 0.633 for Agreeableness (A), and 0.708 for Conscientiousness (C). Ethical approval Ethical approval for this study was granted by the Research Ethics Committee of The Second Qilu Hospital of Shandong University (KYLL202510753). The study adhered to the Declaration of Helsinki. The Project Research Information Sheet included a statement explaining that survey completion indicated consent. Informed consent was obtained from all the participants. Statistical Analysis The data analysis was conducted via SPSS 25.0 software and Mplus 8.3 software. A latent profile analysis was performed on the 12 items of the SCCS to classify the nurses’ SCC. The LPA model 22 included the following fit indices: (1) information criteria: Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and adjusted BIC (aBIC); (2) classification accuracy: entropy, ranging from 0–1; and (3) likelihood ratio tests: the Lo–Mendell–Rubin (LMR) test and the bootstrap likelihood ratio test (BLRT). The optimal number of classes was determined on the basis of the following criteria: (1) the smallest AIC, BIC, and aBIC values; (2) an entropy value exceeding 0.7; and (3) significant p values ( P < 0.05) for both the LMR and BLRT tests. Descriptive statistics (means ± standard deviations for continuous variables and frequencies with percentages for categorical variables), group comparisons (independent t tests and chi-square tests), and a multinomial logistic regression analysis to assess factors associated with SCC class membership were performed with SPSS 25.0. A P value of less than 0.05 was considered statistically significant. Results Latent Profile Analysis of Nurses' Self-Concept Clarity An exploratory LPA was conducted using the 12 SCCS items as indicators, with models testing between one and five latent classes. The model fit indices are presented in Table 1. As the number of classes increased, the AIC, BIC, and aBIC values decreased. In the four-class model, the entropy exceeded 0.9, and the results of both the LMR and BLRT tests were significant ( P < 0.01), indicating an excellent fit. Although the five-class model exhibited further reductions in the AIC, BIC, and aBIC and maintained an entropy greater than 0.8, the LMR test was not significant ( P = 0.221). Consequently, the four-class model was selected as the optimal solution. The average latent class probabilities for the most likely class membership were high (ranging from 88.2–99.1%; see Table 2), thus supporting the distinctiveness and reliability of the four classes. [Insert Table 1 Here] [Insert Table 2 Here] Characteristics of and Labels for the Latent Classe s Figure 1 presents the conditional means of the 12 SCCS items plotted across the four latent classes. The x-axis represents the 12 items, and the y-axis corresponds to the conditional mean scores. The quartiles of the total SCCS score (33.00, 36.00, and 43.00) served as a reference for assessing SCC levels. As shown in Figure 1, the profiles for latent classes C2 and C4 were notably stable, with mean total scores of 46.83 ± 3.653 for C2 and 5.08 ± 3.180 for C4. On the basis of these thresholds and the observed profile stability, Latent Class C2 was designated the “ stable high level” group , whereas latent class C4 was identified as the “stable medium−low level” group . The profiles for Groups C1 and C3 exhibited reduced stability. Except for Items 6 (“I rarely feel conflict about my understanding of my various qualities [e.g., ability, temper, personality]”) and 11 (“I have a clear sense of who I am”), the scores for group C1 were generally lower across the other items. This pattern suggests that nurses in this group exhibited “low overt confidence and a relatively ambiguous self-perception at the surface level, while potentially maintaining higher levels of subconscious self-evaluation”. Consequently, group C1 was designated the “ low overt–high covert” group . Conversely, the profile for group C3 revealed the opposite trend, which was characterized by superficially high confidence and clearly defined goals but accompanied by an underlying tendency toward subconscious self-doubt. Therefore, group C3 was designated the “high overt – low covert” group. [Insert Figure 1 Here] Relationships among Demographic Characteristics, Personality, and SCC Latent Profiles The comparisons of demographic characteristics across the four SCC classes revealed a statistically significant difference solely in the distribution of health status ( P 0.05). In contrast, all Big Five personality traits—Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness differed significantly across the four classes ( P < 0.05; see Table 3). [Insert Table 3 Here] Multinomial Logistic Regression Analysis of Factors Influencing the Latent Classes of SCC Variables that exhibited statistically significant differences in the univariate analysis were incorporated as independent variables in a multinomial logistic regression model. The “stable medium–low level” group (C4, n = 268) served as the reference category. The results are shown in Table 4. [Insert Table 4 Here] Discussion Self-Concept Clarity of Clinical Nurses Can be Categorized into Four Distinct Latent Profiles Previous studies have often relied on aggregate SCCS scores, potentially obscuring distinct item response patterns among individuals with comparable totals. In contrast, this study used LPA to identify heterogeneity among nurses, revealing four distinct SCC profiles. The results of this study indicated that more than half of the nurses (59.82%) exhibited a “stable medium–low level”, which is consistent with findings from previous research 1,16 . This group can be considered representative of the “normal” or “baseline” segment within the nursing population. Most nurses exhibited a satisfactory but not exceptional level of SCC. This phenomenon may be attributed to the high-pressure, demanding nature of the medical environment, where nurses frequently alternate among the roles of caregiver, executor, communicator, and emotional support provider. The inherent diversity and situational nature of these roles often pose a challenge for nurses in seeking to maintain a consistent, stable level of self-clarity 32 . In addition, this group included a greater proportion of younger, less experienced nurses who may face more challenges in adapting to role transitions, managing interpersonal dynamics, and acquiring professional skills. Therefore, systematic support for this nursing population should be prioritized in terms of management practices. By emphasizing the intrinsic value of nursing work, facilitating career development, and ensuring robust team support, managers can reinforce nurses’ role identification and provide meaningful and valuable feedback. Such strategies are likely to enhance nurses’ self-concept and ultimately foster synergistic improvement in both professional satisfaction and mental health. The “low overt–high covert” group (12.28%) and the “high overt–low covert” group (2.01%) represent two distinct and potentially conflicting modes of self-awareness. The former may indicate a discrepancy between external behaviors and internal self-perception; for example, an individual might be capable of completing work while experiencing an ambiguous sense of professional value, thereby potentially serving as a precursor to career burnout 33,34 . Because the latter group had a limited sample size, this phenomenon may be rare or transient. It may result from externally imposed occupational expectations that have not yet been fully integrated into an individual’s internal framework, thereby causing overall instability. Given the close relationships among self-concept, motivation, and job satisfaction 35, targeted interventions such as mindfulness meditation 36,37 should be developed to promote accurate self-assessment and ensure that external behaviors reflect internal beliefs consistently. In this study, the “stable high level” group (25.89%) represented an ideal state characterized by clear, stable, and positive self-awareness among nurses. SCC is widely recognized as a critical indicator of psychological health 38 and plays an essential role in professional resilience, stress resistance, and job satisfaction. Individuals with high SCC levels report greater psychological well-being 39 , leverage their professional strengths more effectively, deliver higher quality care, experience increased professional benefits, and exhibit a lower turnover rate 40,41,42 . Consequently, the findings of this study suggest that nursing managers should develop personalized support strategies based on specific SCC profiles. Such strategies could include systematic education, targeted training, effective empowerment, and optimal resource allocation. Implementing these precise interventions may foster a positive environment for career development, enhance nurses’ subjective well-being and sense of meaning, strengthen their self-awareness, and ultimately promote both the stability of the nursing team and the continuous improvement of health care quality. Multivariate Analysis of the Factors Influencing SCC Profiles The study results indicated that nurses who exhibited high levels of rigor and extraversion were more likely to be categorized into the “stable high level” self-awareness group. Notably, rigor (OR = 1.430) plays a particularly prominent role, suggesting that a systematic and responsible approach—marked by traits such as organization, accountability, and a drive for achievement—enables nurses to effectively manage complex tasks, derive a sense of control from successful outcomes, and continually reinforce their positive self-concept as capable and reliable professionals 43 . Extraversion (OR = 1.138) enhances this process through facilitating positive social interactions; outgoing nurses tend to communicate more effectively with patients, colleagues, and physicians, thereby benefitting from regular positive social feedback 44,45 (e.g., gratitude and recognition). This dynamic supports the formation of a stable self-concept by helping individuals set clear and attainable goals, adopt robust problem-solving strategies, and allocate cognitive resources efficiently when confronted with negative events 36 , all of which are characteristics that are integral to high SCC levels. The “high overt–low covert” group was distinctly associated with high levels of openness and rigor. Individuals with heightened openness display curiosity and a willingness to experiment with new approaches 45 , which may manifest in their proactive pursuit of new skills and adaptation to different roles within the nursing profession. Although such outward behavioral diversity and competence are commendable, a lack of deep internal reflection and integration may impede the development of a stable and deeply embedded self-concept. While rigorousness ensures adherence to professional norms driven by responsibility and discipline, it may not necessarily lead to the internalization of intrinsic values. Although the sample size for this group was small, thus warranting cautious interpretation, these findings underscore that a clear self-concept encompasses both an understanding of “what to do” and, critically, “why to do it,” thereby highlighting the importance of intrinsic motivation. Furthermore, the study results revealed that nurses with higher levels of neuroticism were more likely to be included in the “stable low level” self-awareness group (OR = 0.534). Neurotic individuals typically experience emotional instability and are prone to negative emotions, rendering them more sensitive to criticism, conflict, and failure 43 . This predisposition can lead to self-doubt and repetitive negative thinking, which compromise the continuity and resilience of these individuals’ self-concepts. Even if a certain degree of self-awareness is present, the underlying foundation may remain fragile, making these individuals more susceptible to pressure, a finding that aligns with previous research 46 . Agreeableness exhibits an intriguing duality: it facilitated entry into the “stable high level” group (OR = 1.155), and also characterized the “stable medium–low level” group. This apparent contradiction underscores the multifaceted role of agreeableness in professional contexts. Nurses who exhibit high levels of humanism, cooperation, friendliness, and altruism effectively foster strong team relationships, garner positive feedback, and enhance self-awareness 43,47,48 . Conversely, excessive agreeableness may lead to conformity and conflict avoidance, thereby impeding the deep exploration and persistence needed to recognize and uphold one’s true values and intrinsic needs. In summary, agreeableness may help establish an adaptive “good-enough” self-concept that is compatible with environmental demands; nevertheless, achieving a clear, stable self-concept likely requires the synergistic influence of additional traits such as extraversion and rigor. On the basis of these findings, in the process of designing targeted intervention strategies, nursing managers should comprehensively consider the interplay between personality traits and self-concept clarity. For the majority of nurses in the “medium–low level” group, interventions should prioritize emotional management and the redefinition of professional values using strategies such as mindfulness, narrative nursing, informational support, and positive motivation techniques. For individuals in the “low overt–high covert” group, creating a secure environment that encourages self-expression and reflection, through group counseling and clinical supervision, may facilitate the integration of internal feelings with external performance and help prevent psychological exhaustion. Furthermore, by evaluating nurses’ personality traits and self-concept clarity, managers can optimize human resource allocation; for example, assigning nurses with high rigor and low neuroticism to high-pressure departments (e.g., intensive care units and emergency departments) may promote psychological stability, enhance medical quality and safety, and improve patient satisfaction. Although the influence of demographic and other immutable factors on SCC was relatively minor in this study, potentially because of the limited sample from a single hospital, future research should consider larger, multicenter studies to validate and extend these findings. Conclusion Nurses’ SCC is heterogeneous and can be categorized into four distinct profiles: “low overt–high covert,” “stable high level,” “high overt–low covert,” and “stable medium–low level.” The relatively low proportion of nurses associated with the “stable high level” group suggests significant potential for improvement. However, the use of convenience sampling from a single hospital in this study limits the generalizability of these findings. Future research should employ larger, multicenter samples that encompass hospitals of varying levels and from different geographic regions. Additionally, given the cross-sectional design of the current study, causal inferences cannot be drawn; thus, longitudinal research is necessary to elucidate the developmental trajectory and long-term impacts of SCC. Declarations Funding This study was supported by the Second Qilu Hospital of Shandong University [Grant Number 2023HL040]. Author contributions YLY: Conceptualization, investigation, data curation, methodology, writing & original draft; LY: Conceptualization, data curation, methodology, writing & original draft; CFF: methodology, investigation, project administration, supervision; CX: methodology, supervision, writing & review & editing; WXY: Conceptualization, methodology, supervision, writing & review & editing. All the authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. Ethics approval and consent to participate Ethical approval for this study was granted by the Research Ethics Committee of The Second Qilu Hospital of Shandong University (KYLL202510753). The study adhered to the Declaration of Helsinki. The Project Research Information Sheet included a statement explaining that survey completion indicated consent. Informed consent was obtained from all participants. 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Self-concept clarity and the bodily self: Malleability across modalities. Personality and Social Psychology Bulletin.2020;46(5): 808-820. KIM Y. Personality of organizational social media accounts and its relationship with characteristics of their photos: analyses of startups' Instagram photos. BMC Psychol.2024;12(1): 233. Zhang X, Wang S. The relationship between Big Five personality, causal orientation, and academic procrastination among college students. Youth Studies Journal.2016;(2): 35-40. Tables Table 1. Fit Indices for Latent Profile Analysis Models of Nurses’ Self-Concept Clarity Model k AIC BIC aBIC Entropy LMR BLRT Class Probabilities Class 1 24 14538.13 14636.64 14560.47 1.00 Class 2 37 13373.05 13524.93 13407.50 0.927 ＜0.001 ＜0.001 0.714/0.286 Class 3 50 13218.90 13424.14 13265.46 0.870 0.017 ＜0.001 0.123/0.607/0.270 Class 4 63 13068.29 13326.90 13126.96 0.909 0.003 ＜0.001 0.128/0.260/0.021/0.591 Class 5 76 12998.09 13310.05 13068.86 0.839 0.221 ＜0.001 0.143/0.413/0.210/0.022/0.212 Note: AIC=Akaike information criterion, BIC=Bayesian information criterion, aBIC=adjusted BIC, LMR=Lo-Mendell–Rubin adjusted likelihood ratio test, BLRT=bootstrap likelihood ratio test Table 2. Latent Profile Membership Probabilities (Diagonal) and Cross-Probabilities for the 4- Profile Model Class C1 (n = 55) C2 (n = 116) C3 (n = 9) C4 (n = 268) C1 0.882 0.000 0.000 0.118 C2 0.000 0.973 0.003 0.024 C3 0.000 0.007 0.991 0.003 C4 0.034 0.013 0.000 0.953 Table 3. Comparison of Demographic Characteristics and Personality Traits among the Different SCC Latent Classes Variable C1 (n=55) C2 (n=116) C3 (n=9) C4 (n=268) x 2 P Age (years) 15.257 0.207 ≤25 0 2 0 1 26–30 28 52 4 129 31–40 23 38 3 110 41–50 4 23 2 26 ≥51 0 1 0 2 Gender 4.444 0.187 Male 7 20 1 26 Female 48 96 8 242 Marital status 5.903 0.435 Unmarried 24 33 4 91 Married 31 82 5 175 Divorced/widowed 0 1 0 2 Number of children 10.506 0.298 None 30 43 4 125 One 15 50 3 104 ≥Two 10 21 2 38 Health status 21.907 0.001 Good health 29 87 4 140 Moderate health 19 24 5 108 Chronic diseases 7 5 0 20 Employment relationship 6.939 0.270 Civil servant position 2 14 1 20 Human resources agency 1 9 0 15 Contract based 52 93 8 233 Monthly income (￥) 7.019 0.605 1000–5000 14 27 3 67 5001–10000 30 59 4 148 >10000 1 6 1 5 Academic qualifications 5.429 0.460 Specialty and below 6 26 2 46 Undergraduate degree 49 88 7 219 Graduate student and above 0 2 0 3 Title 9.778 0.334 Junior 50 95 8 232 Intermediate 5 17 1 35 Senior 0 4 0 1 Years of work experience 0.496 0.481 1–5 14 26 1 69 6–10 25 51 5 119 11–20 12 23 1 60 21–30 4 15 2 18 >30 0 1 0 2 Big Five personality traits Neurotic 9.13±1.656 6.18±2.252 6.44±1.509 9.27±1.891 5.584 ＜ 0.001 Extraversion 7.91±1.735 9.30±2.326 10.22±2.906 7.78±1.667 2.848 0.001 Openness 8.29±1.383 9.35±2.048 11.11±8.01 8.01±1.479 2.922 0.001 Agreeableness 8.98±1.593 10.94±2.023 11.56±2.455 9.33±1.572 2.765 0.005 Stringency 8.55±1.741 10.62±2.137 11.89±2.205 8.58±1.627 3.569 ＜ 0.001 *Note: C1 = “low overt–high covert” group, C2 = “stable high level” group, C3 = “high overt–low covert” group, and C4 = “stable medium–low level” group. *High SD for openness in C3, likely due to very small sample size (n = 9). Table 4. Multinomial Logistic Regression Analysis of Factors Influencing SCC Latent Class Membership (Reference: C4 = “stable medium –l ow level” group) Variable C1 (n = 55) C2 (n = 116) C3 (n = 9) β Wald P value OR value 95% CI β Wald P value OR value 95% CI β Wald P value OR value 95% CI Health status Good -0.606 1.888 0.169 0.546 0.230~1.295 0.640 2.511 0.113 1.897 0.859~4.189 0.707 2.146 0.325 2.028 0.876~4.694 Moderate -0.695 2.279 0.131 0.499 0.203~1.230 -0.026 0.004 0.952 0.974 0.417~2.277 -1.642 0.732 0.084 1.062 0.798~1.179 Personality Traits Neuroticism (N) 0.060 0.759 0.384 1.062 0.928~1.214 -0.627 147.920 ＜ 0.001 0.534 0.483~0.591 -0.194 3.799 0.051 0.824 0.678~1.001 Extraversion (E) 0.014 0.025 0.875 1.014 0.850~1.210 0.129 6.548 0.011 1.138 1.031~1.256 0.032 0.084 0.772 1.032 0.834~1.278 Openness (O) 0.103 0.978 0.323 1.108 0.904~1.359 0.083 1.993 0.158 1.087 0.968~1.220 0.337 7.779 0.005 1.400 1.105~1.774 Agreeableness (A) -0.218 5.090 0.024 0.804 0.666~0.972 0.144 6.603 0.010 1.155 1.035~1.290 0.043 0.111 0.739 1.044 0.812~1.341 Conscientiousness (C) -0.095 0.961 0.327 0.910 0.753~1.099 0.358 37.281 ＜ 0.001 1.430 1.275~1.604 0.409 8.160 0.004 1.506 1.137~1.994 *Note: C1 = “low overt–high covert” group, C2 = “stable high level” group, C3 = “high overt–low covert” group, and C4 = “stable medium–low level” group. Ref = reference category. OR = odds ratio. CI = confidence interval. †Note: P values for health status in the C3 model might be unreliable because of very small cell size (n = 9). The ORs for C3 should be interpreted with caution because of the extremely small sample size. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 11 Feb, 2026 Editor invited by journal 20 Jan, 2026 Editor assigned by journal 13 Jan, 2026 Submission checks completed at journal 13 Jan, 2026 First submitted to journal 04 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8277474","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591241679,"identity":"b45b676d-6e51-48c3-9af4-03f0ba80aa5f","order_by":0,"name":"Yao Lingyu","email":"","orcid":"","institution":"The Second Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Lingyu","suffix":""},{"id":591241681,"identity":"87438137-ef6c-4e73-b0e0-4b7b225671c8","order_by":1,"name":"Liu Yuan","email":"","orcid":"","institution":"The Second Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Yuan","suffix":""},{"id":591241682,"identity":"b989e01d-4689-414a-998b-99042e6974a0","order_by":2,"name":"Chen Feifei","email":"","orcid":"","institution":"The Second Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Feifei","suffix":""},{"id":591241683,"identity":"16f2ff01-a2a0-4c09-933a-8727745ec948","order_by":3,"name":"Cui Xia","email":"","orcid":"","institution":"The Second Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Cui","middleName":"","lastName":"Xia","suffix":""},{"id":591241684,"identity":"128f02e8-c6c8-4a0a-9778-87626e401c7d","order_by":4,"name":"Wang Xiaoyun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACZgTrwIEPFaRpYUs8OOMMafbxGB/mbSFCncFx5oePedvuJG64kfPhAG8Dgzy/2AH8WiSb2YyNedueAbXkbjgguYPBcObsBPxa+JkZzKR52w7nbrsN1GJ4hiHB4DYBLWzM7N+gWnIeHEhsI0ILPzMPzJYchgMHidEi2cxTbDjn3OH6/fefGRxsOCNB2C8G549vfPCm7LCxZM/hx5//VNjI80sT0AICTDwItgRh5SDA+IM4daNgFIyCUTBSAQCbrUo82vHV9AAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Wang","middleName":"","lastName":"Xiaoyun","suffix":""}],"badges":[],"createdAt":"2025-12-04 09:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8277474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8277474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102825268,"identity":"b10599ba-0959-4561-a094-e5a3f9ae6b93","added_by":"auto","created_at":"2026-02-17 08:41:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":926696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConditional means of the four potential categories of nurses’ self-concept clarity across the 12 items\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8277474/v1/4722ee456e516d01f3d2decf.png"},{"id":102825371,"identity":"5f5771bc-be9b-4aa3-9862-678757d985f1","added_by":"auto","created_at":"2026-02-17 08:41:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1369247,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8277474/v1/61b17c98-5845-42c7-a18f-fa8ccaf02ec2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Latent Profile Analysis of Clinical Nurses’ Self-Concept Clarity and Its Influencing Factors—A Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eSelf-concept clarity (SCC) refers to the degree to which an individual\u0026apos;s self-beliefs are well defined, confidently expressed, internally consistent, and temporally stable, thereby reflecting a highly organized self-schema\u003csup\u003e1,2,3\u003c/sup\u003e. SCC is widely regarded as both a stable, positive self-perception and a valuable individual resource\u003csup\u003e4\u003c/sup\u003e. The research has consistently demonstrated that SCC functions as a protective factor that mitigates negative emotions and enhances coping ability\u003csup\u003e5\u003c/sup\u003e, thereby improving overall psychological well-being\u003csup\u003e6\u003c/sup\u003e. For example, individuals with higher SCC levels consistently report higher levels of subjective well-being\u003csup\u003e7\u003c/sup\u003e, self-efficacy\u003csup\u003e8\u003c/sup\u003e, and a sense of meaning in life\u003csup\u003e9\u003c/sup\u003e, whereas those with lower SCC levels are more likely to experience negative emotions such as anxiety\u003csup\u003e10\u003c/sup\u003e, depression, and loneliness \u003csup\u003e11\u003c/sup\u003e, with greater obstacles to personal growth and development. Personality traits are intricately linked to SCC. The research has demonstrated that a perfectionistic personality is a significant negative predictor of SCC and that SCC is correlated negatively with neuroticism\u003csup\u003e12,13\u003c/sup\u003e. In contrast, individuals with higher SCC levels tend to maintain more stable personality traits and emotional states, exhibit more social engagement, and receive more social support \u003csup\u003e14,15\u003c/sup\u003e, all of which contribute to positive physical and mental health outcomes. Most of the SCC research has focused on university students and nursing interns, and relatively few studies have investigated qualified nurses. Additionally, many investigations have assessed SCC solely via total scale scores\u003csup\u003e\u0026nbsp;1,\u0026nbsp;16,17\u003c/sup\u003e, focusing primarily on mediating effects while neglecting the variability in responses to individual items.\u003c/p\u003e\n\u003cp\u003eSCC is a critical positive psychological resource, particularly for individuals in high-stress professions\u003csup\u003e1\u003c/sup\u003e. In the case of clinical nurses, SCC transcends mere self-awareness to foster a distinct and robust professional identity. The nursing role is inherently multifaceted and is often characterized by ambiguity in responsibilities, significant emotional labor, and recurrent exposure to human suffering and ethical challenges\u003csup\u003e18\u003c/sup\u003e. Nurses with high SCC are better equipped to navigate these challenges by maintaining a stable sense of self, dissociating personal worth from professional setbacks, and making decisions consistent with their core values. In contrast, low SCC levels may predispose nurses to internalize stress, potentially culminating in emotional exhaustion, depersonalization, and a diminished sense of personal accomplishment, which are the principal dimensions of burnout \u003csup\u003e19\u003c/sup\u003e. The research has established a significant association between an inadequately defined self-concept and adverse mental health and professional outcomes among health care populations\u003csup\u003e\u0026nbsp;20\u003c/sup\u003e. Therefore, investigating SCC is not simply the examination of a personality trait but rather an exploration of a fundamental protective factor buffer against occupational hazards that affect both nurses\u0026rsquo; well-being and the quality of patient care.\u003c/p\u003e\n\u003cp\u003eHistorically, investigations into SCC and its correlates within nursing have relied primarily on variable-centered approaches (e.g., regression models). These methods are invaluable for identifying average relationships between variables across an entire population; for example, they have revealed that SCC, as a global factor, is related inversely to burnout \u003csup\u003e21\u003c/sup\u003e. However, many existing approaches have assumed a homogeneous population, potentially obscuring significant heterogeneity. The nursing population may not be monolithic with respect to SCC. Instead, distinct subgroups or latent profiles may exist among nurses, each characterized by unique configurations of SCC. For example, one subgroup might exhibit high clarity across all domains, whereas another could display clarity in clinical competence alongside ambiguity in work\u0026ndash;life boundaries. Variable-centered analytic methods typically average these diverse profiles, thereby concealing their unique characteristics and potentially differential relationships with outcome variables such as job satisfaction or turnover intention. This limitation underscores the need to employ person-centered analytic techniques, such as latent profile analysis (LPA), in this context \u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eLatent profile analysis (LPA) is a person-centered, probability-based modeling approach employed to identify and explore distinct subpopulations within a larger group. This method classifies individuals who exhibit similar response patterns into discrete subgroups, thereby maximizing differences between groups while minimizing variability within groups \u003csup\u003e23,24, 25\u003c/sup\u003e. This method is particularly well suited for capturing the nuanced manifestations of SCC among clinical nurses. To date, no studies have employed LPA to model the latent classes of nurses\u0026rsquo; SCC. Therefore, this study uses LPA to examine nurses\u0026rsquo; overall performance on SCC items at the individual level, classify them accordingly, identify risk factors associated with distinct SCC types, and explore the relationships between SCC types and personality traits. The aim is to provide insights into enhancing nurses\u0026rsquo; SCC and self-awareness and to inform the development of relevant theoretical models and intervention strategies.\u003c/p\u003e\n\u003cp\u003eThis study seeks to address these identified research gaps by (1) employing LPA to identify distinct profiles of clinical nurses on the basis of their SCC scores and (2) examining the extent to which demographic (e.g., age and years of experience) and personality factors predict profile membership. The findings of this research are expected to provide a nuanced understanding of the psychological landscape within the nursing workforce, thereby\u0026nbsp;facilitating the development of tailored interventions and support strategies for specific subgroups\u003csup\u003e26\u003c/sup\u003e. Ultimately,\u0026nbsp;this\u0026nbsp;work\u0026nbsp;aims\u0026nbsp;to promote improved mental health and retention among clinical nurses amid global nursing shortages\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized a cross-sectional observational design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween July and October 2025, a total of 448 clinical nurses from a tertiary hospital were recruited through convenience sampling. The inclusion criteria were as follows: (1) at least 18 years old, (2) a registered nurse, (3) at least 1 year of work experience, and (4) voluntary participation with informed consent. The exclusion criteria were as follows: (1) nurses on leave and (2) nurses who were not directly involved in clinical care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with Kendall\u0026apos;s standard for sample size calculation\u003csup\u003e28\u003c/sup\u003e, the number of participants should be 10\u0026ndash;20 times the number of analytical variables. In this study, the variables included ten items of general information, one SCC dimension, and five personality dimensions, for which the sample size was estimated to range from 160\u0026ndash;320. To account for potential errors, an additional 20% increase was applied. Ultimately, 469 questionnaires were distributed, and 448 valid responses were received, for an effective response rate of 95.52%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researchers utilized Questionnaire Star to distribute a standardized questionnaire link to the nursing staff. During data collection, a consistent set of instructions was provided to explain the study\u0026apos;s purpose, research significance, completion procedures, and key precautions. Participation was voluntary, with informed consent obtained from all nurses prior to completing the survey. Each participant completed the questionnaire only once, and only those responses with completion times exceeding three minutes were retained to ensure thoughtful engagement. To further enhance data quality, two independent reviewers conducted questionnaire screening, and all the data were double-entered into Excel to minimize input errors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic and disease-related questionnaire\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA self-designed questionnaire was used to collect data on demographic and professional variables, including age, gender, marital status, number of children, health status, monthly income, employment type, educational level, professional title, and work experience. The participants were registered nurses representing a range of clinical ranks\u0026mdash;junior, intermediate and senior\u0026mdash;as classified by the Chinese nursing career ladder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-Concept Clarity Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Self-Concept Clarity Scale (SCCS), developed by Campbell et al. (1990)\u003csup\u003e29\u003c/sup\u003e, comprises 12 items rated on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). All items except 6 and 11 are reverse scored, with higher total scores reflecting greater SCC levels. In the present study, the scale exhibited good internal consistency, as evidenced by a Cronbach\u0026rsquo;s \u0026alpha; of 0.844.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChinese Version of the Ten-Item Personality Inventory\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Big Five personality traits were assessed using the Chinese version of the Ten-Item Personality Inventory (TIPI\u0026ndash;C), which was originally developed by Gosling et al. (2003)\u003csup\u003e30\u003c/sup\u003e and subsequently revised by Li \u003csup\u003e31\u003c/sup\u003e. This instrument consists of 10 items that assess five personality dimensions: Neuroticism (N), Extraversion (E), Openness (O), Agreeableness (A), and Conscientiousness (C). Items 2, 4, 6, 8, and 10 are reverse scored to ensure accurate measurement of the intended constructs. The responses are measured on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), with higher scores indicating the stronger expression of the corresponding personality trait. In this study, the overall Cronbach\u0026rsquo;s \u0026alpha; for the TIPI\u0026ndash;C was 0.673, with the subscale coefficients as follows: 0.629 for Neuroticism (N), 0.712 for Extraversion (E), 0.591 for Openness (O), 0.633 for Agreeableness (A), and 0.708 for Conscientiousness (C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted by the Research Ethics Committee of The Second Qilu Hospital of Shandong University (KYLL202510753). The study adhered to the Declaration of Helsinki. The Project Research Information Sheet included a statement explaining that survey completion indicated consent. Informed consent was obtained from all the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analysis was conducted via SPSS 25.0 software and Mplus 8.3 software. A latent profile analysis was performed on the 12 items of the SCCS to classify the nurses\u0026rsquo; SCC. The LPA model \u003csup\u003e22\u003c/sup\u003e included the following fit indices: (1) information criteria: Akaike\u0026rsquo;s information criterion (AIC), Bayesian information criterion (BIC), and adjusted BIC (aBIC); (2) classification accuracy: entropy, ranging from 0\u0026ndash;1; and (3) likelihood ratio tests: the Lo\u0026ndash;Mendell\u0026ndash;Rubin (LMR) test and the bootstrap likelihood ratio test (BLRT). The optimal number of classes was determined on the basis of the following criteria: (1) the smallest AIC, BIC, and aBIC values; (2) an entropy value exceeding 0.7; and (3) significant p values (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) for both the LMR and BLRT tests. Descriptive statistics (means \u0026plusmn; standard deviations for continuous variables and frequencies with percentages for categorical variables), group comparisons (independent \u003cem\u003et\u003c/em\u003e tests and chi-square tests), and a multinomial logistic regression analysis to assess factors associated with SCC class membership were performed with SPSS 25.0. A \u003cem\u003eP\u003c/em\u003e value of less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eLatent Profile Analysis of Nurses\u0026apos; Self-Concept Clarity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn exploratory LPA was conducted using the 12 SCCS items as indicators, with models testing between one and five latent classes. The model fit indices are presented in Table 1. As the number of classes increased, the AIC, BIC, and aBIC values decreased. In the four-class model, the entropy exceeded 0.9, and the results of both the LMR and BLRT tests were significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01), indicating an excellent fit. Although the five-class model exhibited further reductions in the AIC, BIC, and aBIC and maintained an entropy greater than 0.8, the LMR test was not significant (\u003cem\u003eP\u003c/em\u003e = 0.221). Consequently, the four-class model was selected as the optimal solution. The average latent class probabilities for the most likely class membership were high (ranging from 88.2\u0026ndash;99.1%; see Table 2), thus supporting the distinctiveness and reliability of the four classes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Insert Table 1 Here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Insert Table 2 Here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of and Labels for the Latent\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eClasse\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Figure 1 presents the conditional means of the 12 SCCS items plotted across the four latent classes.\u0026nbsp;The x-axis represents the 12 items, and the y-axis\u0026nbsp;corresponds to\u0026nbsp;the conditional mean scores. The quartiles of the total SCCS score (33.00, 36.00, and 43.00) served as a reference for\u0026nbsp;assessing\u0026nbsp;SCC levels. As shown in Figure 1, the profiles for\u0026nbsp;latent\u0026nbsp;classes\u0026nbsp;C2\u0026nbsp;and\u0026nbsp;C4\u0026nbsp;were\u0026nbsp;notably\u0026nbsp;stable,\u0026nbsp;with\u0026nbsp;mean total scores\u0026nbsp;of\u0026nbsp;46.83 \u0026plusmn; 3.653\u0026nbsp;for\u0026nbsp;C2\u0026nbsp;and 5.08 \u0026plusmn; 3.180\u0026nbsp;for\u0026nbsp;C4.\u0026nbsp;On the basis of these\u0026nbsp;thresholds\u0026nbsp;and\u0026nbsp;the observed\u0026nbsp;profile stability,\u0026nbsp;Latent Class\u0026nbsp;C2\u0026nbsp;was\u0026nbsp;designated\u0026nbsp;the\u0026nbsp;\u0026ldquo;\u003cstrong\u003estable high level\u0026rdquo; group\u003c/strong\u003e,\u0026nbsp;whereas latent class\u0026nbsp;C4\u0026nbsp;was\u0026nbsp;identified as\u0026nbsp;the \u003cstrong\u003e\u0026ldquo;stable medium\u0026minus;low level\u0026rdquo; group\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe profiles for Groups C1 and C3 exhibited reduced stability. Except for Items 6 (\u0026ldquo;I rarely feel conflict about my understanding of my various qualities [e.g., ability, temper, personality]\u0026rdquo;) and 11 (\u0026ldquo;I have a clear sense of who I am\u0026rdquo;),\u0026nbsp;the scores\u0026nbsp;for group\u0026nbsp;C1\u0026nbsp;were generally lower\u0026nbsp;across\u0026nbsp;the other items. This pattern\u0026nbsp;suggests\u0026nbsp;that\u0026nbsp;nurses in this group exhibited\u0026nbsp;\u0026ldquo;low overt confidence and a\u0026nbsp;relatively\u0026nbsp;ambiguous self-perception\u0026nbsp;at the surface level,\u0026nbsp;while\u0026nbsp;potentially\u0026nbsp;maintaining\u0026nbsp;higher\u0026nbsp;levels of subconscious self-evaluation\u0026rdquo;. Consequently,\u0026nbsp;group\u0026nbsp;C1\u0026nbsp;was\u0026nbsp;designated\u0026nbsp;the\u0026nbsp;\u0026ldquo;\u003cstrong\u003elow overt\u0026ndash;high covert\u0026rdquo; group\u003c/strong\u003e.\u0026nbsp;Conversely, the profile\u0026nbsp;for group\u0026nbsp;C3\u0026nbsp;revealed\u0026nbsp;the opposite trend, which was characterized by superficially high confidence and clearly defined\u0026nbsp;goals\u0026nbsp;but accompanied by an underlying tendency toward subconscious self-doubt.\u0026nbsp;Therefore, group\u0026nbsp;C3\u0026nbsp;was designated\u0026nbsp;the \u0026ldquo;high overt\u003cstrong\u003e\u0026ndash;\u003c/strong\u003elow covert\u0026rdquo; group.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Insert Figure 1 Here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationships among Demographic Characteristics, Personality, and SCC Latent Profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comparisons of demographic characteristics across the four SCC classes revealed a statistically significant difference solely in the distribution of health status (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). No significant differences were observed in sex, marital status, number of children, employment type, monthly income, education level, or other demographic variables (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). In contrast, all Big Five personality traits\u0026mdash;Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness differed significantly across the four classes (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; see Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Insert Table 3 Here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultinomial Logistic Regression Analysis of Factors Influencing the Latent Classes of SCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariables that exhibited statistically significant differences in the univariate analysis were incorporated as independent variables in a multinomial logistic regression model. The \u0026ldquo;stable medium\u0026ndash;low level\u0026rdquo; group (C4, n = 268) served as the reference category. The results are shown in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Insert Table 4 Here]\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eSelf-Concept Clarity of Clinical Nurses Can be Categorized into Four Distinct Latent Profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies have often relied on aggregate SCCS scores, potentially obscuring distinct item response patterns among individuals with comparable totals. In contrast, this study used LPA to identify heterogeneity among nurses, revealing four distinct SCC profiles. The results of this study indicated that more than half of the nurses (59.82%) exhibited a \u0026ldquo;stable medium\u0026ndash;low level\u0026rdquo;, which is consistent with findings from previous research \u003csup\u003e1,16\u003c/sup\u003e. This group can be considered representative of the \u0026ldquo;normal\u0026rdquo; or \u0026ldquo;baseline\u0026rdquo; segment within the nursing population. Most nurses exhibited a satisfactory but not exceptional level of SCC. This phenomenon may be attributed to the high-pressure, demanding nature of the medical environment, where nurses frequently alternate among the roles of caregiver, executor, communicator, and emotional support provider. The inherent diversity and situational nature of these roles often pose a challenge for nurses in seeking to maintain a consistent, stable level of self-clarity \u003csup\u003e32\u003c/sup\u003e. In addition, this group included a greater proportion of younger, less experienced nurses who may face more challenges in adapting to role transitions, managing interpersonal dynamics, and acquiring professional skills. Therefore, systematic support for this nursing population should be prioritized in terms of management practices. By emphasizing the intrinsic value of nursing work, facilitating career development, and ensuring robust team support, managers can reinforce nurses\u0026rsquo; role identification and provide meaningful and valuable feedback. Such strategies are likely to enhance nurses\u0026rsquo; self-concept and ultimately foster synergistic\u0026nbsp;improvement in both professional satisfaction and mental health.\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;low overt\u0026ndash;high covert\u0026rdquo; group (12.28%) and the \u0026ldquo;high overt\u0026ndash;low covert\u0026rdquo; group (2.01%) represent two distinct and potentially conflicting modes of self-awareness. The former may indicate a discrepancy between external behaviors and internal self-perception; for example, an individual might be capable of completing work while experiencing an ambiguous sense of professional value, thereby potentially serving as a precursor to career burnout \u003csup\u003e33,34\u003c/sup\u003e.\u0026nbsp;Because the latter group had a limited sample size, this phenomenon may be rare or transient. It may result from externally imposed occupational expectations that have not yet been fully integrated into an individual\u0026rsquo;s internal framework, thereby causing overall instability.\u0026nbsp;Given the close relationships among self-concept, motivation, and job satisfaction\u003csup\u003e35,\u003c/sup\u003e targeted interventions such as mindfulness meditation\u003csup\u003e36,37\u003c/sup\u003e should be developed to promote accurate self-assessment and ensure that external behaviors reflect internal beliefs consistently. In this study, the \u0026ldquo;stable high level\u0026rdquo; group (25.89%) represented an ideal state characterized by clear, stable, and positive self-awareness among nurses. SCC is widely recognized as a critical indicator of psychological health\u003csup\u003e\u0026nbsp;38\u003c/sup\u003e and plays an essential role in professional resilience, stress resistance, and job satisfaction. Individuals with high SCC levels report greater psychological well-being \u003csup\u003e39\u003c/sup\u003e, leverage their professional strengths more effectively, deliver higher quality care, experience increased professional benefits, and exhibit a lower turnover rate \u003csup\u003e40,41,42\u003c/sup\u003e. Consequently, the findings of this study suggest that nursing managers should develop personalized support strategies based on specific SCC profiles. Such strategies could include systematic education, targeted training, effective empowerment, and optimal resource allocation. Implementing these precise interventions may foster a positive environment for career development, enhance nurses\u0026rsquo; subjective well-being and sense of meaning, strengthen their self-awareness, and ultimately promote both the stability of the nursing team and the continuous improvement of health care quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Analysis of the Factors Influencing SCC Profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study results indicated that nurses who exhibited high levels of rigor and extraversion were more likely to be categorized into the \u0026ldquo;stable high level\u0026rdquo; self-awareness group. Notably, rigor (OR = 1.430) plays a particularly prominent role, suggesting that a systematic and responsible approach\u0026mdash;marked by traits such as organization, accountability, and a drive for achievement\u0026mdash;enables nurses to effectively manage complex tasks, derive a sense of control from successful outcomes, and continually reinforce their positive self-concept as capable and reliable professionals \u003csup\u003e43\u003c/sup\u003e. Extraversion (OR = 1.138) enhances this process through facilitating positive social interactions; outgoing nurses tend to communicate more effectively with patients, colleagues, and physicians, thereby benefitting from regular positive social feedback \u003csup\u003e44,45\u0026nbsp;\u003c/sup\u003e(e.g., gratitude and recognition). This dynamic supports the formation of a stable self-concept by helping individuals set clear and attainable goals, adopt robust problem-solving strategies, and allocate cognitive resources efficiently when confronted with negative events \u003csup\u003e36\u003c/sup\u003e, all of which are characteristics that are integral to high SCC levels.\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;high overt\u0026ndash;low covert\u0026rdquo; group was distinctly associated with high levels of openness and rigor. Individuals with heightened openness display curiosity and a willingness to experiment with new approaches\u003csup\u003e45\u003c/sup\u003e, which may manifest in their proactive pursuit of new skills and adaptation to different roles within the nursing profession. Although such outward behavioral diversity and competence are commendable, a lack of deep internal reflection and integration may impede the development of a stable and deeply embedded self-concept. While rigorousness ensures adherence to professional norms driven by responsibility and discipline, it may not necessarily lead to the internalization of intrinsic values. Although the sample size for this group was small, thus warranting cautious interpretation, these findings underscore that a clear self-concept encompasses both an understanding of \u0026ldquo;what to do\u0026rdquo; and, critically, \u0026ldquo;why to do it,\u0026rdquo; thereby highlighting the importance of intrinsic motivation.\u003c/p\u003e\n\u003cp\u003eFurthermore, the study results revealed that nurses with higher levels of neuroticism were more likely to be included in the \u0026ldquo;stable low level\u0026rdquo; self-awareness group (OR = 0.534). Neurotic individuals typically experience emotional instability and are prone to negative emotions, rendering them more sensitive to criticism, conflict, and failure \u003csup\u003e43\u003c/sup\u003e. This predisposition can lead to self-doubt and repetitive negative thinking, which compromise the continuity and resilience of these individuals\u0026rsquo; self-concepts. Even if a certain degree of self-awareness is present, the underlying foundation may remain fragile, making these individuals more susceptible to pressure, a finding that aligns with previous research\u003csup\u003e46\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAgreeableness exhibits an intriguing duality: it facilitated entry into the \u0026ldquo;stable high level\u0026rdquo; group (OR = 1.155), and also characterized the \u0026ldquo;stable medium\u0026ndash;low level\u0026rdquo; group. This apparent contradiction underscores the multifaceted role of agreeableness in professional contexts. Nurses who exhibit high levels of humanism, cooperation, friendliness, and altruism effectively foster strong team relationships, garner positive feedback, and enhance self-awareness \u003csup\u003e43,47,48\u003c/sup\u003e. Conversely, excessive agreeableness may lead to conformity and conflict avoidance, thereby impeding the deep exploration and persistence needed to recognize and uphold one\u0026rsquo;s true values and intrinsic needs. In summary, agreeableness may help establish an adaptive \u0026ldquo;good-enough\u0026rdquo; self-concept that is compatible with environmental demands; nevertheless, achieving a clear, stable self-concept likely requires the synergistic influence of additional traits such as extraversion and rigor.\u003c/p\u003e\n\u003cp\u003eOn the basis of these findings, in the process of designing targeted intervention strategies, nursing managers should comprehensively consider the interplay between personality traits and self-concept clarity. For the majority of nurses in the \u0026ldquo;medium\u0026ndash;low level\u0026rdquo; group, interventions should prioritize emotional management and the redefinition of professional values using strategies such as mindfulness, narrative nursing, informational support, and positive motivation techniques. For individuals in the \u0026ldquo;low overt\u0026ndash;high covert\u0026rdquo; group, creating a secure environment that encourages self-expression and reflection, through group counseling and clinical supervision, may facilitate the integration of internal feelings with external performance and help prevent psychological exhaustion. Furthermore, by evaluating nurses\u0026rsquo; personality traits and self-concept clarity, managers can optimize human resource allocation; for example, assigning nurses with high rigor and low neuroticism to high-pressure departments (e.g., intensive care units and emergency departments) may promote psychological stability, enhance medical quality and safety, and improve patient satisfaction. Although the influence of demographic and other immutable factors on SCC was relatively minor in this study, potentially because of the limited sample from a single hospital, future research should consider larger, multicenter studies to validate and extend these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNurses\u0026rsquo; SCC is heterogeneous and can be categorized into four distinct profiles: \u0026ldquo;low overt\u0026ndash;high covert,\u0026rdquo; \u0026ldquo;stable high level,\u0026rdquo; \u0026ldquo;high overt\u0026ndash;low covert,\u0026rdquo; and \u0026ldquo;stable medium\u0026ndash;low level.\u0026rdquo; The relatively low proportion of nurses associated with the \u0026ldquo;stable high level\u0026rdquo; group suggests significant potential for improvement. However, the use of convenience sampling from a single hospital in this study limits the generalizability of these findings. Future research should employ larger, multicenter samples that encompass hospitals of varying levels and from different geographic regions. Additionally, given the cross-sectional design of the current study, causal inferences cannot be drawn; thus, longitudinal research is necessary to elucidate the developmental trajectory and long-term impacts of SCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Second Qilu Hospital of Shandong University [Grant Number 2023HL040].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYLY: Conceptualization, investigation, data curation, methodology, writing \u0026amp; original draft; LY: Conceptualization, data curation, methodology, writing \u0026amp; original draft; CFF: methodology, investigation, project administration, supervision; CX: methodology, supervision, writing \u0026amp; review \u0026amp; editing; WXY: Conceptualization, methodology, supervision, writing \u0026amp; review \u0026amp; editing. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted by the Research Ethics Committee of The Second Qilu Hospital of Shandong University (KYLL202510753). The study adhered to the Declaration of Helsinki. The Project Research Information Sheet included a statement explaining that survey completion indicated consent. Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the first author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all departments and all nurses for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCampbell JD, Trapnell PD, Heine SJ, \u003cem\u003eet al\u003c/em\u003e. Self-concept clarity: Measurement, personality correlates, and cultural boundaries. Journal of Personality and Social Psychology.1996;70(1): 141-156.\u003c/li\u003e\n\u003cli\u003eNiu G, Sun X, Zhou Z, \u003cem\u003eet al\u003c/em\u003e. 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Self and Identity.2023;22(2): 181-196.\u003c/li\u003e\n\u003cli\u003eRen XY, Duan JY, Xu Y,Feng CZ.Knowing oneself: the concept of self-concept clarity, its influencing mechanism, and development strategies.Chinese Journal of Applied Psychology.2022;28(3),237-244.\u003c/li\u003e\n\u003cli\u003eDummel S. Relating mindfulness to attitudinal ambivalence through self- concept clarity. Mindfulness.2018;12,1- 8.\u003c/li\u003e\n\u003cli\u003eWang Q, Zhang J, Fan R, et al. Research progress on the management of self-concept clarity in new nurses. Journal of Evidence-Based Nursing. 2022;8(1): 42-45.\u003c/li\u003e\n\u003cli\u003eHanley AW, Garland EL. Clarity of mind: Structural equation modeling of associations between dispositional mindfulness, self-concept clarity and psychological well-being. Personality and Individual Differences.2017;106: 334-339.\u003c/li\u003e\n\u003cli\u003eZhang Y, Dai Y, Tian Y, et al. Status quo and influencing factors of professional self-concept among junior nurses in multi-ethnic plateau areas. Journal of Nursing (China). 2022;29(20): 50-54.\u003c/li\u003e\n\u003cli\u003eGoliroshan S, Nobahar M, Raeisdana N, et al. The Protective Role of Professional Self-concept and Job Embeddedness on Nurses\u0026apos; Burnout: Structural Equation Modeling. BMC Nursing.2021;20(1): 203.\u003c/li\u003e\n\u003cli\u003eLi X, Zhang S, Zhao T, et al. The mediating effect of professional self-concept between job satisfaction and turnover intention among nurses with master\u0026apos;s degrees. Chinese Journal of Nursing.2021;56(7): 1038-1043.\u003c/li\u003e\n\u003cli\u003eJie HT, Li N, Wang HH,et al.The Relationship between Five Pattern Personality and Big Five Personality Based on Latent Profile Analysis.Chinese Archives of Traditional Chinese Medicine.2025;1-14.\u003c/li\u003e\n\u003cli\u003eNiessen D,Danner D,Spengler M, et al.Big five personality traits predict successful transitions from school to vocational education and training:a large-scale study.Front Psychol.2020;11:1827-1845.\u003c/li\u003e\n\u003cli\u003ePerez DC,Molero D M,Martos A,et al.Burnout and engagement:personality profiles in nursing professionals.J Clin Med.2019;8(3):286-300\u003c/li\u003e\n\u003cli\u003eKrol SA, Th\u0026eacute;riault R, Olson JA, et al. Self-concept clarity and the bodily self: Malleability across modalities. Personality and Social Psychology Bulletin.2020;46(5): 808-820.\u003c/li\u003e\n\u003cli\u003eKIM Y. Personality of organizational social media accounts and its relationship with characteristics of their photos: analyses of startups\u0026apos; Instagram photos. BMC Psychol.2024;12(1): 233.\u003c/li\u003e\n\u003cli\u003eZhang X, Wang S. The relationship between Big Five personality, causal orientation, and academic procrastination among college students. Youth Studies Journal.2016;(2): 35-40.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Fit Indices for Latent Profile Analysis Models of Nurses\u0026rsquo; Self-Concept Clarity\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.21649%;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.12371%;\"\u003e\n \u003cp\u003ek\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003eaBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003eBLRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.8969%;\"\u003e\n \u003cp\u003eClass\u0026nbsp;Probabilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.21649%;\"\u003e\n \u003cp\u003eClass 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.12371%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e14538.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e14636.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e14560.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.8969%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.21649%;\"\u003e\n \u003cp\u003eClass 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.12371%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13373.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13524.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13407.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.8969%;\"\u003e\n \u003cp\u003e0.714/0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.21649%;\"\u003e\n \u003cp\u003eClass 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.12371%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13218.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13424.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13265.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.8969%;\"\u003e\n \u003cp\u003e0.123/0.607/0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.21649%;\"\u003e\n \u003cp\u003eClass 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.12371%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13068.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13326.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13126.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.8969%;\"\u003e\n \u003cp\u003e0.128/0.260/0.021/0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.21649%;\"\u003e\n \u003cp\u003eClass 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.12371%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e12998.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13310.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3093%;\"\u003e\n \u003cp\u003e13068.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.8969%;\"\u003e\n \u003cp\u003e0.143/0.413/0.210/0.022/0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: AIC=Akaike information criterion, BIC=Bayesian information criterion, aBIC=adjusted BIC, LMR=Lo-Mendell\u0026ndash;Rubin adjusted likelihood ratio test, BLRT=bootstrap likelihood ratio test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Latent Profile Membership Probabilities (Diagonal) and Cross-Probabilities for the 4-\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eProfile Model\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5258%;\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003eC1 (n = 55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003eC2 (n = 116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003eC3 (n = 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003eC4 (n = 268)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5258%;\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5258%;\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5258%;\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.5258%;\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Comparison of Demographic Characteristics and Personality Traits among the Different SCC Latent Classes\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.125%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003eC1 (n=55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003eC2 (n=116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003eC3 (n=9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003eC4 (n=268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003ex\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e15.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026le;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 26\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 41\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026ge;51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e4.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e5.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Unmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Divorced/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eNumber of children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e10.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;One\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;Two\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eHealth status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e21.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Good health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Moderate health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Chronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eEmployment relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e6.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Civil servant position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Human resources agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Contract based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eMonthly income (￥)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e7.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1000\u0026ndash;5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e5001\u0026ndash;10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eAcademic qualifications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e5.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Specialty and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Undergraduate degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Graduate student and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eTitle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e9.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.125%;\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Intermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Senior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eYears of work experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;6\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;11\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.125%;\"\u003e\n \u003cp\u003eBig Five personality traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Neurotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e9.13\u0026plusmn;1.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e6.18\u0026plusmn;2.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e6.44\u0026plusmn;1.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e9.27\u0026plusmn;1.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e5.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Extraversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e7.91\u0026plusmn;1.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e9.30\u0026plusmn;2.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e10.22\u0026plusmn;2.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e7.78\u0026plusmn;1.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e8.29\u0026plusmn;1.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e9.35\u0026plusmn;2.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e11.11\u0026plusmn;8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e8.01\u0026plusmn;1.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Agreeableness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e8.98\u0026plusmn;1.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e10.94\u0026plusmn;2.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e11.56\u0026plusmn;2.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e9.33\u0026plusmn;1.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e2.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Stringency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e8.55\u0026plusmn;1.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e10.62\u0026plusmn;2.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e11.89\u0026plusmn;2.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e8.58\u0026plusmn;1.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e3.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*Note: C1 = \u0026ldquo;low overt\u0026ndash;high covert\u0026rdquo; group, C2 = \u0026ldquo;stable high level\u0026rdquo; group, C3 = \u0026ldquo;high overt\u0026ndash;low covert\u0026rdquo; group, and C4 = \u0026ldquo;stable medium\u0026ndash;low level\u0026rdquo; group. *High SD for openness in C3, likely due to very small sample size (n = 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multinomial Logistic Regression Analysis of Factors Influencing SCC Latent Class Membership (Reference: C4 = \u0026ldquo;stable medium\u003c/strong\u003e\u0026ndash;l\u003cstrong\u003eow level\u0026rdquo; group)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.5096%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 21.5765%;\"\u003e\n \u003cp\u003eC1 (n = 55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 22.8551%;\"\u003e\n \u003cp\u003eC2 (n = 116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 21.5765%;\"\u003e\n \u003cp\u003eC3 (n = 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e95%\u0026nbsp;CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2354%;\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e95%\u0026nbsp;CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e95%\u0026nbsp;CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.5096%;\"\u003e\n \u003cp\u003eHealth status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2354%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.5096%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e-0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e1.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.230~1.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2354%;\"\u003e\n \u003cp\u003e2.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.859~4.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e2.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e2.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.876~4.694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.5096%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e-0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e2.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.203~1.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2354%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.417~2.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e-1.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.798~1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.5096%;\"\u003e\n \u003cp\u003ePersonality Traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2354%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.5096%;\"\u003e\n \u003cp\u003eNeuroticism (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.928~1.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e-0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2354%;\"\u003e\n \u003cp\u003e147.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.483~0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e-0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e3.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.678~1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.5096%;\"\u003e\n \u003cp\u003eExtraversion (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.850~1.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2354%;\"\u003e\n \u003cp\u003e6.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e1.031~1.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.834~1.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.5096%;\"\u003e\n \u003cp\u003eOpenness (O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.904~1.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2354%;\"\u003e\n \u003cp\u003e1.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.968~1.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e7.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e1.105~1.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.5096%;\"\u003e\n \u003cp\u003eAgreeableness (A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e-0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e5.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.666~0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2354%;\"\u003e\n \u003cp\u003e6.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e1.035~1.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.812~1.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.5096%;\"\u003e\n \u003cp\u003eConscientiousness (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e0.753~1.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2354%;\"\u003e\n \u003cp\u003e37.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e1.275~1.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.4795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3563%;\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9568%;\"\u003e\n \u003cp\u003e8.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9157%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.8747%;\"\u003e\n \u003cp\u003e1.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.4729%;\"\u003e\n \u003cp\u003e1.137~1.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*Note: C1 = \u0026ldquo;low overt\u0026ndash;high covert\u0026rdquo; group, C2 = \u0026ldquo;stable high level\u0026rdquo; group, C3 = \u0026ldquo;high overt\u0026ndash;low covert\u0026rdquo; group, and C4 = \u0026ldquo;stable medium\u0026ndash;low level\u0026rdquo; group. Ref = reference category. OR = odds ratio. CI = confidence interval. \u0026dagger;Note: \u003cem\u003eP\u003c/em\u003e values for health status in the C3 model might be unreliable because of very small cell size (n = 9). The ORs for C3 should be interpreted with caution because of the extremely small sample size.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Self-concept clarity, Personality traits, Latent profile analysis, Root cause analysis, Nurse","lastPublishedDoi":"10.21203/rs.3.rs-8277474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8277474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Nurses’ self-concept clarity can influence their mental health and professional values significantly. The research on self-concept clarity has primarily assessed individuals' levels of self-concept clarity on the basis of scale scores but has largely overlooked individual heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims\u003c/strong\u003e To investigate the potential categories and differences in self-concept clarity characteristics among clinical nurses, analyze the relevant influencing factors, and provide insights for the development of theoretical models and intervention strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA convenience sampling method was used to recruit 448 clinical nurses from a tertiary hospital in Shandong Province between July and October 2025. A latent profile analysis (LPA) was employed to identify distinct profiles of self-concept clarity among nurses, and the Big Five Personality Scale was administered to assess their personality traits. A univariate analysis and a logistic regression were conducted to examine the influencing factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e On the basis of their self-concept clarity characteristics, the 448 nurses were classified into four groups: “low overt–high covert” (12.28%), “stable high level” (25.89%), “high overt–low covert” (2.01%), and “stable medium–low level” (59.82%). Nurses with higher levels of agreeableness were more likely to belong to the “stable medium–low level” group (\u003cem\u003eOR\u003c/em\u003e = 0.804, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) compared with the “low overt-high covert” group. Nurses who exhibited higher levels of extraversion, agreeableness, and rigor were more likely to be assigned to the “stable high level” group (\u003cem\u003eOR \u003c/em\u003e= 1.138, 1.155, 1.430; all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Conversely, greater neuroticism was associated significantly with the “stable medium–low level” group (\u003cem\u003eOR\u003c/em\u003e = 0.534; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Furthermore, nurses with high levels of openness and rigor were more likely to be classified into the “high overt–low covert” group (\u003cem\u003eOR\u003c/em\u003e = 1.400, 1.506; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Clinical nurses exhibit distinct variations in self-concept clarity. Accordingly, managers should implement targeted intervention strategies that consider individual personality traits, thereby enhancing nurses’ self-concept clarity. Such measures are critical for advancing the psychological well-being of nurses and ensuring the stability and development of nursing teams.\u003c/p\u003e","manuscriptTitle":"Latent Profile Analysis of Clinical Nurses’ Self-Concept Clarity and Its Influencing Factors—A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 08:39:20","doi":"10.21203/rs.3.rs-8277474/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-12T04:23:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-21T03:44:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T08:07:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T08:02:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-12-04T09:18:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"741497c9-3734-455d-97db-081c0ff18c0e","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T08:39:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 08:39:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8277474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8277474","identity":"rs-8277474","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}