Multiple Mediating Pathways from Psychosocial Competence to Self-Care Skills in University Students: A Cross-Validation Study Using Structural Equation Modeling and Network Analysis

Multiple Mediating Pathways from Psychosocial Competence to Self-Care Skills in University Students: A Cross-Validation Study Using Structural Equation Modeling and Network Analysis | 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 Multiple Mediating Pathways from Psychosocial Competence to Self-Care Skills in University Students: A Cross-Validation Study Using Structural Equation Modeling and Network Analysis Chen Jianfeng, Pan Yikang, Yin Huaigang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9074591/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective : This study aimed to elucidate the mechanisms through which psychosocial competence shapes self-care skills in college students by testing a hypothesized chain mediation model: "psychosocial competence → health-promoting behavior → health information literacy → self-care skills." Methods : A cross-sectional survey was conducted among 390 freshmen and sophomores. The structural equation model (SEM) was used to test the macro-level paths, while the network analysis based on the EBICglasso algorithm was adopted to explore the micro-level item associations, and the bridge strength was calculated to identify the nodes connecting different theoretical communities. Results: SEM revealed significant indirect effects for health-promoting behavior (β = 0.126, p = 0.020), health information literacy (β = 0.385, p = 0.001), and the chain pathway (β = 0.083, p = 0.002). The total indirect effect accounted for 67.1% of the total effect, and the model explained 76.7% of the variance in self-care skills (R² = 0.767). Network analysis identified two high-bridge-strength nodes: SCA1 ("Interpret fitness test data") and HIL4 ("Make decisions based on information"), which served as key connectors linking the communities of Psychosocial Competence (PSC), Health-Promoting Behavior (HPB), Health Information Literacy (HIL), and Self-Care Skills (SCA). Conclusion: Psychosocial competence influences self-care skills through multiple pathways, with health information literacy playing a central mediating role. The identified bridge nodes, SCA1 and HIL4, represent potential precise targets for health interventions among college students. Psychosocial competence Self-care skills Health information literacy Chain mediation Network analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction The transition from adolescence to adulthood represents a critical developmental window during which health beliefs, behavioral patterns, and self-management skills are formed, profoundly shaping lifelong health(Fleary et al., 2018 ; Arnett, 2000 ). University students face specific challenges and opportunities as they move toward independent living and assume greater responsibility for their health (Rosário et al., 2025 ). However, a considerable proportion of young adults exhibit inadequate self-care skills, manifesting as low health information literacy (Bao et al., 2022 ), a lack of sustained engagement in health-promoting behaviors (S. Wang et al., 2025 ), and limited capacity for autonomous disease prevention (Odunsi & Farris, 2025 ). Therefore, a thorough understanding of the underlying mechanisms shaping self-care skills development in this population is crucial for designing targeted early intervention strategies aimed at mitigating chronic disease risk and promoting lifelong health (Alosaimi et al., 2023 ). The development of self-care skills is rooted in psychosocial competence, which itself develops through repeated health behaviors and the active acquisition of health knowledge. This study is grounded in social cognitive theory (Bandura, 1986 ), the integrated model of health literacy (Sørensen et al., 2012 ), and Orem's self-care theory (Orem et al., 1995 ). These frameworks guide a serial mediation model: "competence → behavior → cognition → skill". Social cognitive theory explains how competence drives behavior, emphasizing psychosocial capacities, especially self-efficacy, in motivating health actions (Bandura, 1997 ). This study's psychosocial competence construct builds on the health action process approach (Schwarzer & Luszczynska, 2008 ) and serves as a multidimensional resource for health challenges. The construct is broader than general self-efficacy. encompassing life values, social participation, and psychological resilience in health contexts (Chen & Xiao, 2025 ; Lv et al., 2025 ). Individuals with higher psychosocial competence are more likely to initiate and sustain health-promoting behaviors such as regular exercise, a balanced diet, and effective stress management ((Guo et al., 2024 ; Zhou et al., 2025 ). This line of reasoning supports our proposed pathway. However, building stable self-care skills requires more than behavioral engagement; it also necessitates a cognitive shift. The Health Literacy Integration Model posits that engaging in health behaviors generates the need for health information. which prompts individuals to seek, appraise, and use health information, thereby developing health information literacy. Habiba and Koli ( 2024 ) define health information literacy as a higher-order cognitive ability that includes accessing, understanding, evaluating, and applying health information for health-related decisions (Habiba & Koli, 2024 ). Health-promoting behaviors are not merely outcomes; they also create opportunities for frequent engagement with health information. Repeated practice further consolidates health information literacy. Orem's self-care theory and other research—although demonstrated in professional groups such as nurses (C. Liu et al., 2025 )—concur that advanced self-care skills, such as health monitoring and risk identification, require deep health knowledge—not just surface learning. Notably, different theories offer divergent perspectives on the temporal order of cognition and behavior. Social Cognitive Theory posits that cognition (e.g., self-efficacy) drives behavior, whereas the Health Literacy Integration Model emphasizes the role of behavior in shaping cognition (Zhang et al., 2025 ). Our "competence → behavior → cognition → skill" chain mediation model integrates these sequences, positioning them as mutually reinforcing rather than mutually exclusive. This pathway underscores the behavior-to-cognition link. Practice in real-world health situations scaffolds health information literacy (Stormacq et al., 2023 ). Real-world challenges provide the necessary context for learning; abstract learning lacks both context and motivation (Tang et al., 2025 ). Research on embodied cognition supports this view: health knowledge is best acquired in authentic scenarios (Tempone-Wiltshire & Matthews, 2025 ). Based on this foundation, we propose a core hypothesis: psychosocial competence influences self-care skills through mediating mediations. The mediators are health-promoting behaviors and health information literacy, encompassing both parallel mediation and a chain mediation pathway: competence → behavior → cognition → skill. To test this, we pose three questions. (1) Do health-promoting behaviors and health information literacy together mediate the relationship between psychosocial competence and self-care skills? (2) Does a significant chain mediation effect occur along the competence → behavior → cognition → skill pathway? (3) At the indicator level, which variables serve as bridges between psychosocial competence, health behavior, health information literacy, and self-care skills? We employ a combined strategy: structural equation modeling (Kline, 2023 ) and network analysis (Borsboom et al., 2021 ). SEM tests relationships among latent variables and their pathways, including direct, indirect, and chain effects. Network analysis deconstructs constructs into empirical networks among indicators and reveals high-bridge centrality nodes that integrate domains. Together, these methods provide a broad and detailed understanding of how psychosocial competence shapes self-care skills. The theoretical model is presented in Fig. 1 . 2 Methods 2.1 Participants and Procedure This cross-sectional study recruited first- and second-year undergraduate students from a comprehensive university. Sample size was estimated using two criteria. G*Power software (f² = 0.15, α = 0.05, power = 0.95, predictors = 3) indicated a minimum of 119 participants (Cohen, 2013 ). Simulations by Epskamp (2018) suggested that network stability (CS coefficient > 0.5) with 23 nodes requires at least 345 participants. Our final sample was 390 participants. This met the structural equation modeling threshold (> 200 cases) (Wolf et al., 2013 ) and network analysis requirements. Data were collected via the Questionnaire Star platform during spring 2025 using convenience sampling. Participants completed anonymous surveys within 15–20 minutes. As items related to self-care skills might involve traumatic health experiences, a trigger warning was provided at the beginning. The warning told participants they could skip sensitive items or withdraw at any time. This study used only anonymous survey procedures. It qualified for exemption from formal ethical review under Article 32 of the Chinese "Ethical Review Measures for Life Science and Medical Research Involving Humans" (National Health Commission of the PRC et al., 2023). The study followed the Declaration of Helsinki (World Medical Association., 2001). The study protocol was reviewed internally, and permission to conduct the research was granted by the Department of Physical Education at Changzhou Vocational Institute of Textile and Garment. 2.2 Measures Given the lack of existing scales fully aligning with the integrated constructs of this study, a scale was developed following standardized procedures (DeVellis & Thorpe, 2021 ). Domain definitions were established through literature review and expert panel discussions (comprising 3 health psychology professors, 2 nursing professors, and 2 undergraduate student representatives), generating an initial item pool of 26 items (Carpenter, 2018 ). Expert content validity assessment yielded item-level content validity indices (I-CVIs) ranging from 0.87 to 0.93, leading to the deletion of irrelevant or redundant items (Yusoff, 2019 ). During pilot testing (n = 48), item analysis and exploratory factor analysis (EFA) were conducted: item analysis eliminated poorly discriminating items (critical ratio, i.e., t-value, < 3.0 or item-total correlation < 0.40) (Watkins, 2021 ); EFA using principal component analysis with varimax rotation extracted a four-factor structure, and items with factor loadings 0.40 were removed, resulting in 3 deleted items (Goretzko et al., 2021 ). Confirmatory factor analysis (CFA) was performed on the remaining 23 items using the formal sample (N = 390) to evaluate the four-factor model fit (McNeish & Wolf, 2023 ), retaining all 23 items (see Table S1 for full items). The scale employed a 5-point Likert scoring system (1 = strongly disagree, 5 = strongly agree). All scales demonstrated satisfactory structural validity via CFA, with fit indices as follows: Psychosocial Competence (6 items) assessed comprehensive capacities in self-awareness, life values, social engagement, cross-cultural competence, resource utilization, and psychological resilience (Schwarzer & Luszczynska, 2008 ; Taylor et al., 2011 ). CFA indicated good fit: χ²(9) = 13.52, p = 0.141, CFI = 0.994, TLI = 0.989, RMSEA = 0.036, SRMR = 0.015; Cronbach’s α = 0.946. Health-Promoting Behaviors (5 items) evaluated engagement in nutrition, sleep, screen time, and physical activity (Chao, 2023 ). CFA results: χ²(5) = 12.47, p = 0.029, CFI = 0.985, TLI = 0.969, RMSEA = 0.062, SRMR = 0.019; Cronbach’s α = 0.921. Health Information Literacy (6 items) measured cognitive abilities in accessing, evaluating, communicating, and applying health information (Nutbeam, 2008; Sørensen et al., 2012 ). CFA: χ²(9) = 23.91, p = 0.005, CFI = 0.978, TLI = 0.964, RMSEA = 0.065, SRMR = 0.022; Cronbach’s α = 0.951. Self-Care Skills (6 items) assessed practical competencies in health monitoring, first aid, risk identification, and emergency equipment use ((Orem et al., 1995 ; Taylor et al., 2011 ). CFA: χ²(9) = 22.31, p = 0.008, CFI = 0.981, TLI = 0.969, RMSEA = 0.062, SRMR = 0.020; Cronbach’s α = 0.948. Item analysis indicated that all items across the scales exhibited good discriminability, with corrected item-total correlations ranging from 0.76 to 0.89 (see Supplementary Table S2 for detailed results). Cronbach's α values for all dimensions exceeded 0.92, demonstrating high internal consistency reliability. 2.3 Data Analysis Structural Equation Modeling (SEM): Psychosocial competence was specified as the independent variable, self-care skills as the dependent variable, and health-promoting behaviors, along with health information literacy as mediating variables. Model estimation was performed using Mplus 8.3 (Muthén & Muthén, 2017 ) with maximum likelihood (ML) estimation. Mediation effects were tested using the bias-corrected bootstrap method (5,000 resamples) (Hayes, 2017 ). Model fit was evaluated based on the following criteria: CFI ≥ 0.90, TLI ≥ 0.90, RMSEA ≤ 0.08, and SRMR ≤ 0.08 (Xia & Yang, 2019 ). Model fit superiority was assessed by comparing AIC/BIC values between the serial mediation model and a constrained parallel mediation model in which the HPB → HIL path was fixed to zero (i.e., a parallel mediation model). Network Analysis: Conducted using R 4.1.3 (Team, 2020 ) with the qgraph 1.9.5 and bootnet 1.5 packages. The network included 23 observed variables as nodes. Based on the Spearman correlation matrix, a regularized partial correlation network was estimated via the EBICglasso algorithm (γ = 0.50, nlambda = 100, lambda.min.ratio = 0.1, with no penalization of diagonal elements) (Borsboom et al., 2021 ). Missing data were handled using pairwise deletion. In the network visualization, positive edges were represented in green and negative edges in red; only edges with absolute weights > 0.05 were retained, and node layout was determined using the Fruchterman–Reingold algorithm. Bridge strength was quantified using the Bridge Strength index (Jones et al., 2021 ), and community structure was defined a priori based on four latent variables corresponding to four communities. Edge weight stability was assessed via 1,000 nonparametric bootstrap samples, and the correlation stability (CS) coefficient was computed. To evaluate the robustness of the network estimation, sensitivity analyses were conducted using alternative tuning parameters (γ = 0.25, 0.75). 3 Results 3.1 Descriptive Statistics and Correlation Analysis As shown in Table 1 , the absolute values of skewness (0.003–0.196) and kurtosis (0.119–0.439) for all observed variables were below the empirical thresholds (skewness < 2, kurtosis < 7) (Kamath et al., 2025 ). Data points in the Q-Q plots were approximately distributed along the theoretical diagonal, satisfying the prerequisites for parametric tests. The four core latent variables exhibited significant pairwise positive correlations, with correlation coefficients ranging from 0.636 to 0.825 (p < 0.001). Common method bias was assessed using the unmeasured latent method factor approach (Podsakoff et al., 2024 ). The results indicated minimal changes in model fit after introducing the method factor (ΔCFI = 0.002, ΔTLI = -0.002, ΔRMSEA = 0.001), suggesting no substantial common method bias in this study (McNeish, 2025 ). Table 1 Descriptive Statistics and Correlation Matrix (N = 390) Variable M SD Skewness Kurtosis 1 2 3 4 1. PSC 2.2145 0.59392 -0.006 0.243 1 2. HPB 2.3831 0.61950 -0.196 0.196 0.636** 1 3. HIL 2.1949 0.59194 -0.003 -0.119 0.825** 0.663** 1 4. SCA 2.2919 0.60930 -0.018 0.439 0.774** 0.661** 0.805** 1 Note: PSC = mean score of psychosocial competence; HPB = mean score of health-promoting behavior; HIL = mean score of health information literacy; SCA = mean score of Self-Care Skills. Bolded coefficients indicate correlations among variables within the same theoretical community. According to Cohen (1988), r ≥ 0.5 is considered a strong correlation (Cohen, 2013 ). ∗p<.05, ∗∗p<.01, ∗∗∗p<.001 (American Psychological Association, 2020 ), same below. 3.2 Measurement Model Confirmatory factor analysis supported the four-factor measurement model, indicating an acceptable to good fit: χ²(224) = 730.329, CFI = 0.946, TLI = 0.939, RMSEA = 0.076 (90% CI [0.071, 0.081]), SRMR = 0.035. All constructs demonstrated adequate internal consistency and convergent validity, with composite reliability (CR) values ranging from 0.921 to 0.950 and average variance extracted (AVE) values from 0.700 to 0.759, both exceeding recommended thresholds (Hair et al., 2010 ). Discriminant validity was further established via the Fornell–Larcker criterion (Fornell & Larcker, 1981 ; Cheung et al., 2024 ) (see Table S3, with bolded diagonal elements representing the square root of AVE exceeding off-diagonal correlations) and by examining heterotrait–monotrait (HTMT) ratios (Roemer et al., 2021 ), all of which were below 0.85 (see Supplementary Material Table S4). Residual analysis revealed no systematic misfit: over 95% of standardized residuals fell within ± 2.58, and all latent variable residual variances were positive—indicating the absence of estimation anomalies such as Heywood cases (Kline, 2023 ). 3.3 Chain Mediation Model Test As shown in Fig. 2 and Table 2 , psychosocial competence exerted a significant total effect on self-care skills (β = 0.885, 95% CI [0.760, 1.014], p < 0.001). The direct effect remained statistically significant after accounting for mediators (β = 0.269, 95% CI [0.045, 0.498], p = 0.021), and the full model accounted for 76.7% of the variance in self-care skills (R² = 0.767). Three distinct indirect pathways emerged: (1) health-promoting behavior as an independent mediator (β = 0.126, p = 0.020); (2) health information literacy as an independent mediator (β = 0.385, p = 0.001); and (3) the chain pathway through "health-promoting behavior → health information literacy” (β = 0.083, p = 0.002). The total indirect effect was 0.594 (p < 0.001), representing 67.1% of the total effect. Of this, the chain mediation effect constituted 9.4% of the total effect and 14.0% of the total indirect effect. A nested model comparison strongly favored the chain mediation specification over the constrained parallel model (Δχ²(1) = 29.84, p < 0.001; ΔAIC = 27.841), providing robust statistical support for the hypothesized directional sequence (Burnham & Anderson, 2004 ). Table 2 Path Coefficients and Effect Decomposition for the Chain Mediation Model Path β SE 95% CI p Direct effects PSC → HPB 0.673 0.053 [0.560, 0.769] < .001 PSC → HIL 0.717 0.065 [0.590, 0.835] < .001 HPB → HIL 0.229 0.067 [0.105, 0.368] .001 HPB → SCA 0.173 0.068 [0.058, 0.326] .011 HIL → SCA 0.496 0.117 [0.267, 0.727] < .001 PSC → SCA (direct) 0.269 0.117 [0.045, 0.498] .021 Indirect effects PSC → HPB → SCA 0.126 0.054 [0.041, 0.253] .020 PSC → HIL → SCA 0.385 0.115 [0.188, 0.634] .001 PSC → HPB → HIL → SCA (chain) 0.083 0.027 [0.041, 0.150] .002 Total indirect effect 0.594 0.122 [0.375, 0.847] < .001 Total effect 0.885 0.064 [0.760, 1.014] < .001 Proportion mediated 0.671 — [0.421, 0.946] — Proportion chain mediation 0.094 — [0.045, 0.168] — Note: β = standardized coefficient; SE = standard error; CI = confidence interval. Proportion mediated = total indirect effect / total effect. Proportion chain mediation = chain indirect effect / total effect. — = not applicable. 3.4 Network Structure Analysis Network estimation was conducted using the EBICglasso algorithm applied to Spearman rank-order correlations (tuning parameter γ = 0.5), yielding an initial network comprising 23 nodes and 118 edges (Fig. 3 ). Edge pruning was performed using a conservative weight threshold of |ρ| > 0.05, retaining 63 edges and resulting in a network density of 0.249. Of these, 59 edges were positive and 4 were negative; the mean node predictability—quantifying the proportion of variance in each node explained by its neighbors—was 0.729, indicating moderate-to-strong local stability. Structural inspection revealed that 14 of the top 15 strongest edges (ranked by absolute weight) represented intra-community connections, underscoring cohesive substructures within latent constructs. The strongest edge was SCA3–SCA5 (ρ = 0.380), followed by HPB1–HPB2 (ρ = 0.346), HIL2–HIL4 (ρ = 0.332), and HIL5–HIL6 (ρ = 0.331). Among inter-community edges, the strongest positive association was observed between PSC5 and HIL3 (ρ = 0.187). All four negative edges occurred exclusively between the Psychosocial Competence (PSC) and Self-Care Skills (SCA) communities, with absolute weights ranked as follows: PSC2–SCA3 (ρ = −0.157), PSC4–SCA1 (ρ = −0.132), PSC5–SCA2 (ρ = −0.096), and PSC2–SCA5 (ρ = −0.088). These cross-construct inhibitory associations warrant theoretical and clinical attention, particularly given their consistent directionality and localization. 3.5 Bridge Centrality Analysis Community detection (Fig. 4 A) identified four distinct communities corresponding to the theoretical constructs: Psychosocial Competence (PSC), Self-Care Skills (SCA), Health-Promoting Behavior (HPB), and Health Information Literacy (HIL). Bridge strength—the sum of absolute edge weights connecting a node to nodes in *other* communities—was computed for all 23 nodes. Mean bridge strength was higher in non-mediating communities (PSC and SCA: M = 0.275, SD = 0.128) than in mediating communities (HPB and HIL: M = 0.234, SD = 0.124), suggesting that nodes representing antecedent and outcome constructs serve as relatively stronger structural bridges across the network. As shown in Fig. 4 B, the three nodes with the highest bridge strength were SCA1 (“Interpret fitness test data”, bridge strength = 0.572), HIL4 (“Make decisions based on information”, bridge strength = 0.480), and SCA3 (“Handle common injuries/emergencies”, bridge strength = 0.383). These findings were robust across centrality metrics: one-step bridge expected influence—a directional measure capturing the net influence a node exerts on nodes outside its own community—exhibited near-perfect convergence with bridge strength (r = 0.94); the same three nodes ranked first (SCA1: 0.541), second (HIL4: 0.463), and third (SCA3: 0.372). Full bridge centrality indices—including bridge strength, bridge betweenness, bridge closeness, and bridge expected influence—are reported in Supplementary Table S5. 3.6 Network Robustness Assessment To evaluate the stability and reliability of the estimated network structure, we conducted a nonparametric bootstrap procedure (1,000 resamples) on the original network (118 edges). Figure 5 A displays the 15 strongest edges alongside their 95% bootstrapped confidence intervals (CIs); narrow CIs reflect greater estimation precision and replicability. As shown in Fig. 5 B, within-community edges demonstrated markedly higher stability (mean stability coefficient = 0.915, SD = 0.122) than bridge edges (mean = 0.684, SD = 0.172), with this difference reaching statistical significance (Welch’s t(113.9) = − 8.72, p 0.5 for “moderate” robustness (Epskamp et al., 2018 ). Further inspection revealed that 86.8% of within-community edges achieved stability ≥ 0.75, compared to only 36.9% of bridge edges—highlighting the relative fragility of cross-community connections. Among the four negative edges, only PSC2–SCA3 exhibited high stability (CS = 0.952); the remaining three (PSC4–SCA1, PSC5–SCA2, PSC2–SCA5) fell below the 0.5 threshold and were thus deemed insufficiently stable for confident interpretation. Finally, sensitivity analyses varying the EBICglasso tuning parameter (γ = 0.25 and γ = 0.75) confirmed structural consistency: the community partition, global topology, and top 10 strongest edges—including all key intra- and inter-community associations reported in Sections 3.4 and 3.5 —remained invariant across specifications (see Supplementary Material Table S6). Collectively, these results provide strong empirical support for the reliability and generalizability of the network architecture. 4 Discussion Integrating structural equation modeling and network psychometrics, this study elucidates a multilayered mechanism through which psychosocial competence shapes self-care skills among college students. Results robustly support a theoretically grounded chain mediation pathway—“competence → behavior → cognition → skill”—in which psychosocial competence exerts both direct and indirect influences: (1) independent mediation via health promotion behavior and health information literacy, and (2) sequential mediation through the “health promotion behavior → health information literacy” pathway. Complementing these macro-level causal inferences, network analysis identified SCA1 (“Interpret fitness test data”) and HIL4 (“Make decisions based on information”) as high-bridge-strength nodes—functioning as critical micro-level connectors across latent communities. Together, these findings advance an integrated process model that bridges macro-theoretical frameworks with micro-structural dynamics. 4.1 Theoretical Framework of Multiple Mediation Paths The influence of psychosocial capabilities on self-care skills operates through three distinct pathways, forming a theoretical framework characterized by cognitive dominance, behavioral supplementation, and chain-like mediation. The mediating effect of health information literacy accounts for the largest share, with its standardized coefficient approximately three times that of health promotion behavior (β = 0.385 vs. 0.126). This difference in magnitude indicates the pivotal role of the cognitive processing stage in resource transformation (McAnally & Hagger, 2023 ). However, the relative advantage of the effect size reflects only the static strength of association. The temporal relationship between behavior and cognition—whether health promotion behavior constitutes a necessary prerequisite for the development of health information literacy—involves a deep tension between social cognitive theory and the health literacy model (Rüegg, 2022 ). The above findings engage in an interesting dialogue with previous research. Liu et al. ( 2024 ) reported a mediating effect of psychological capital (β = 0.099), comparable in magnitude to the health promotion behavior path in this study. However, they did not distinguish the relative contributions of cognitive and behavioral channels (X. Liu et al., 2024 ). The effect of decomposition in this study reveals that when both health information literacy and behavioral variables are included, the dominance of the cognitive path becomes more pronounced. This difference may stem from differences in health outcome types: the development of emotional management skills may rely more on accumulating behavioral experience (Zhu et al., 2024 ), whereas the construction of self-care skills may rely more on information processing and situational judgment (Zeng et al., 2025 ). The presence of a chain mediation path (β = 0.083) suggests that the behavior-cognition sequence may have a distinct theoretical function rather than merely an additive effect. Although this path has a smaller effect size, its model fit advantage implies that the "behavior → cognition" directionality may reveal the black box of the mechanism—that is, how behavioral practice triggers cognitive needs and is then internalized as stable skills (Suri et al., 2019 ). The theoretical implications of this directionality issue require clarification through the boundary condition that "ability level moderates the necessity of the behavior-cognition sequence." 4.2 Directionality of the Behavior-Cognition Sequence and Its Theoretical Implications The statistical significance of the chain path (β = 0.083, p = 0.002) supports the existence of the "behavior → cognition" direction. However, its effect size is substantially smaller than the independent mediating effect of health information literacy, suggesting that the strength of this sequence is highly context-dependent (Murakami et al., 2023 ). Bourke et al.'s ( 2025 ) longitudinal study further found that among adolescents transitioning to adulthood, behavioral changes preceding cognitive adaptation tend to predict a more stable health trajectory (Bourke et al., 2025 ). Notably, this pattern was statistically significant only in the group with low psychosocial ability. These findings collectively point to a moderating mechanism: when psychosocial ability is high, individuals can directly activate cognitive strategies; when ability is low, they must rely on behavioral practice to elicit cognitive processes. Odunsi and Farris ( 2025 ) further confirmed that cognitive variables have significantly higher predictive power for preventive behaviors than behavioral variables, supporting the efficiency advantage of the "cognition-first" strategy in resource-rich contexts (Odunsi & Farris, 2025 ). Shoji et al.'s ( 2025 ) cross-national study provided external support for the cross-contextual stability of this path, identifying health literacy as a stable driver of health behaviors (Shoji et al., 2025 ). In summary, the chain path identified in this study does not merely represent a compromise between the two theoretical perspectives; rather, it reveals the boundary conditions under which "ability level moderates the necessity of the behavior-cognition sequence." Based on the directionality observed, a dynamic reciprocal relationship may exist between health literacy and behavior (Osborne et al., 2022 ). However, the cross-sectional design precludes confirmation of the temporal sequence within this cycle, and future studies should employ longitudinal designs to test this possibility (Li et al., 2022 ). 4.3 Cross-disciplinary Integration Function of Bridge Nodes in the Network The structural equation model revealed the dominant position of the cognitive hub at the latent variable level, while network analysis further identified key connection points at the observed indicator level. The bridge strength between SCA1 ("Interpret fitness test data") and HIL4 ("Make decisions based on information") significantly exceeded that of other nodes, establishing them as core hubs connecting the four theoretical communities. The high bridge strength of SCA1 indicates that the ability to interpret health data operates at the interface of information internalization, psychological resource mobilization, and practical application. This finding aligns with research showing that eHealth literacy influences health behavior through multiple pathways (Kim & Oh, 2021 ); however, network analysis more precisely identifies "data interpretation"—rather than general "health information literacy"—as an intervention target. The high bridge strength of HIL4 echoes Tian et al.'s ( 2025 ) emphasis on critical health literacy, suggesting that the ability to evaluate information and make decisions serves as a core hub connecting cognition and action, beyond mere information acquisition skills (Tian et al., 2025 ). Network analysis also revealed micro-level associations not apparent at the latent variable level. Although psychosocial competence and self-care skills were generally strongly positively correlated, negative edges emerged at the network level, with the most stable negative correlation observed between PSC2 ("Value life and health") and SCA3 ("Handle common injuries/emergencies"). This counterintuitive finding may reflect a cognitive conflict between values and behavior: individuals who deeply reflect on the meaning of life may assess their first-aid abilities more cautiously. This interpretation remains post-hoc speculation and warrants verification through qualitative methods (L. Wang et al., 2024 ). The finding engages with research on psychological first aid training, which suggests that such training not only enhances objective skills but also reshapes learners' cognitive reference systems regarding their own abilities (Mtiraoui et al., 2025 ; Öztürk et al., 2025 ). 4.4 Limitations and Future Research Directions This study has several limitations. First, the cross-sectional design precludes causal inference. Although the chain mediation model is theoretically grounded, it cannot rule out reverse causality; future research should employ longitudinal designs to test the directionality of the paths. Second, the sample representativeness is limited. As participants were restricted to freshmen and sophomores from a single comprehensive university, caution is warranted when generalizing the findings to other populations. Third, the study relied on a single measurement method—self-report—for all variables. The negative correlation between PSC2 and SCA3 may therefore reflect self-assessment bias among high-ability individuals rather than a genuine ability-behavior dissociation. Future studies should incorporate objective indicators to validate these findings. Fourth, the network analysis was exploratory in nature. The cross-sectional network cannot capture the dynamic evolution of relationships among variables, and future research should test the replicability of the observed network structure. 4.5 Practical Implications Based on these findings, this study proposes a stratified intervention strategy. The first layer is general education, which integrates health information literacy into core curricula, with a focus on cultivating the information evaluation and decision-making ability represented by HIL4—the weakest skill among college students in an information-overloaded environment (Nakayama et al., 2022 ). The second layer involves targeted training. This includes developing health data visualization tools for SCA1 to translate abstract indicators into personalized action suggestions and designing contradictory information decision-making simulations for HIL4 to train evidence evaluation skills on controversial topics. Assessment should employ node-specific outcome indicators: accuracy of data interpretation for SCA1 and quality of decision-making reasoning for HIL4, rather than a general health literacy score (Saldert et al., 2018 ). The third layer focuses on capacity building, enhancing psychosocial capabilities through modules on psychological resilience and emotion regulation, while adopting a parallel strategy to simultaneously activate cognitive and behavioral goals. 5 Conclusion This study, through cross-method validation using structural equation modeling and network analysis, has revealed multiple pathways through which psychosocial abilities influence self-care skills. Health information literacy is the core mediating variable, with the cognitive path being more efficient than the behavioral path. The chain path has verified the situational-driven effect of behavior on cognition, but the pattern of effect sizes suggests that a "cognition-first" strategy may be more cost-effective. SCA1 and HIL4, as bridge nodes, provide operational targets for precise intervention. The above findings offer a stratified design basis for health intervention among college students in the digital age and simultaneously expand the dialogue boundary between social cognitive theory and the integrated model of health literacy. Declarations Funding This work was supported by the Key Funding Project of the Jiangsu Province Education Science "14th Five-Year Plan" [Grant Number B/2023/02/90], titled "Assessment and Promotion Pathways of Health Literacy among University Students in the Context of 'Healthy China'." Author Information Authors and Affiliations Chen Jianfeng¹, Pan Yikang¹, Yin Huaigang² ¹ Department of Physical Education, Changzhou Vocational Institute of Textile and Garment, Changzhou 213164, China; ² Nanjing Sport Institute, Nanjing, Jiangsu 210014, China Contributions Chen Jianfeng was responsible for study design, data analysis, manuscript writing, and final approval. Pan Yikang participated in data collection, literature collation, and initial draft writing. Yin Huaigang participated in study design, result interpretation, and manuscript revision. All authors read and approved the final manuscript. Corresponding Author Correspondence to: Chen Jianfeng (Department of Physical Education, Changzhou Vocational Institute of Textile and Garment, Changzhou 213164, China; [email protected] ). Ethics Declarations Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. According to Article 32 of the Chinese "Ethical Review Measures for Life Science and Medical Research Involving Humans" (National Health Commission of the PRC et al., 2023), this study qualifies for exemption from formal ethical review because it involves only anonymous questionnaire procedures with no collection of personally identifiable information. The study protocol was reviewed internally, and permission to conduct the research was granted by the Department of Physical Education at Changzhou Vocational Institute of Textile and Garment. All participants were informed of the study purpose, the anonymous nature of their participation, and their right to withdraw at any time before the survey. Completion and submission of the questionnaire were considered implied informed consent. Competing Interests The authors declare no competing interests. Data Availability The datasets generated and analyzed during this study are not publicly available but can be obtained from the corresponding author upon reasonable request. References Alosaimi, N., Sherar, L. B., Griffiths, P., & Pearson, N. (2023). Clustering of diet, physical activity and sedentary behaviour and related physical and mental health outcomes: A systematic review. BMC Public Health , 23 (1), 1572. https://doi.org/10.1186/s12889-023-16372-6 American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). American Psychological Association. https://doi.org/10.1037/0000165-000 Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist , 55 (5), 469–480. https://doi.org/10.1037/0003-066X.55.5.469 Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory . Prentice-hall. Bandura, A. (1997). Self-efficacy: The exercise of control . Macmillan. Bao, X., Chen, D., Shi, L., Xia, Y., Shi, Z., & Wang, D. (2022). The relationship between COVID-19-related prevention cognition and healthy lifestyle behaviors among university students: Mediated by e-health literacy and self-efficacy. Journal of Affective Disorders , 309 , 236–241. https://doi.org/10.1016/j.jad.2022.04.044 Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., Van Borkulo, C. D., Van Bork, R., & Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers , 1 (1), 58. https://doi.org/10.1038/s43586-021-00055-w Bourke, M., Brown, D., & Kwan, M. Y. W. (2025). Lifestyle Behavior Patterns During the Transition From Adolescence to Emerging Adulthood: Associations With Mental Health and Wellbeing. Emerging Adulthood , 13 (6), 1381–1394. https://doi.org/10.1177/21676968251376750 Burnham, K. P., & Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research , 33 (2), 261–304. https://doi.org/10.1177/0049124104268644 Carpenter, S. (2018). Ten Steps in Scale Development and Reporting: A Guide for Researchers. Communication Methods and Measures , 12 (1), 25–44. https://doi.org/10.1080/19312458.2017.1396583 Chao, D.-P. (2023). Health-promoting lifestyle and its predictors among health-related and non-health-related university students in Taiwan: A cross-sectional quantitative study. BMC Public Health , 23 (1), 827. https://doi.org/10.1186/s12889-023-15760-2 Chen, X., & Xiao, H. (2025). E-health literacy and health-promoting behaviors among nursing students in China: The mediating role of self-efficacy. Psychology, Health & Medicine , 30 (8), 1757–1767. https://doi.org/10.1080/13548506.2025.2481193 Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management , 41 (2), 745–783. https://doi.org/10.1007/s10490-023-09871-y Cohen, J. (2013). Statistical power analysis for the behavioral sciences . routledge. DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications . Sage publications. Fleary, S. A., Joseph, P., & Pappagianopoulos, J. E. (2018). Adolescent health literacy and health behaviors: A systematic review. Journal of Adolescence , 62 (1), 116–127. https://doi.org/10.1016/j.adolescence.2017.11.010 Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 无 , 18 (1), 39–50. Goretzko, D., Pham, T. T. H., & Bühner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology , 40 (7), 3510–3521. https://doi.org/10.1007/s12144-019-00300-2 Guo, S., Fu, H., & Guo, K. (2024). Effects of physical activity on subjective well-being: The mediating role of social support and self-efficacy. Frontiers in Sports and Active Living , 6 , 1362816. https://doi.org/10.3389/fspor.2024.1362816 Habiba, U., & Koli, F. S. (2024). The Mediating Role of Students’ Health Information Literacy Skills: Exploring the Relationship Between Web Resource Utilization and Health Information Evaluation Proficiency. Health Expectations , 27 (4), e14176. https://doi.org/10.1111/hex.14176 Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis, 7th edn. Hoboken . NJ: Prentice hall. Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach . Guilford publications. Jones, P. J., Ma, R., & McNally, R. J. (2021). Bridge Centrality: A Network Approach to Understanding Comorbidity. Multivariate Behavioral Research , 56 (2), 353–367. https://doi.org/10.1080/00273171.2019.1614898 Kamath, A., Poojari, S., & Varsha, K. (2025). Assessing the robustness of normality tests under varying skewness and kurtosis: A practical checklist for public health researchers. BMC Medical Research Methodology , 25 (1), 206. https://doi.org/10.1186/s12874-025-02641-y Kim, S., & Oh, J. (2021). The Relationship between E-Health Literacy and Health-Promoting Behaviors in Nursing Students: A Multiple Mediation Model. International Journal of Environmental Research and Public Health , 18 (11), 5804. https://doi.org/10.3390/ijerph18115804 Kline, R. B. (2023). Principles and practice of structural equation modeling . Guilford publications. Li, S., Cui, G., Zhou, F., Liu, S., Guo, Y., Yin, Y., & Xu, H. (2022). The longitudinal relationship between eHealth literacy, health-promoting lifestyles, and health-related quality of life among college students: A cross-lagged analysis. Frontiers in Public Health , 10 , 868279. Liu, C., Chen, Z., Qin, H., Yang, Y., Cui, X., Lei, R., & Li, B. (2025). Health Knowledge Acquisition and Health Communication Ability of Nurses: A Chain Mediating Model of Health Literacy and Health Education Competence. Journal of Advanced Nursing , 81 (9), 5903–5913. https://doi.org/10.1111/jan.16728 Liu, X., Wang, Z., Zhang, C., Xu, J., Shen, Z., Peng, L., Mi, Y., & Xu, H. (2024). Psychological Capital and Its Factors as Mediators Between Interpersonal Sensitivity and Depressive Symptoms Among Chinese Undergraduates. Psychology Research and Behavior Management , Volume 17 , 429–441. https://doi.org/10.2147/PRBM.S452993 Lv, C., Wang, Z., Cui, H., Zhang, K., Wang, X., Wang, X., Alsudais, T. A., & Duan, S. (2025). Investigating the influence of adolescents’ social and emotional skills on health behavior: A moderated mediation analysis. Frontiers in Psychology , 16 , 1712176. https://doi.org/10.3389/fpsyg.2025.1712176 McAnally, K., & Hagger, M. S. (2023). Health literacy, social cognition constructs, and health behaviors and outcomes: A meta-analysis. Health Psychology , 42 (4), 213–234. https://doi.org/10.1037/hea0001266 McNeish, D. (2025). Dynamic measurement invariance cutoffs for two-group fit index differences. Psychological Methods . McNeish, D., & Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods , 28 (1), 61–88. https://doi.org/10.1037/met0000425 Mtiraoui, A., Mahjoubi, H., Achour, A., Ghardallou, M., & Nakhli, J. (2025). The Impact of Psychological First Aid Training (RAPID‐PFA) on Self‐Efficacy, Perceived Competencies and Disaster Preparedness of Nursing Students in Tunisian Public Institutions: A Randomized Controlled Trial. Journal of Contingencies and Crisis Management , 33 (1), e70019. https://doi.org/10.1111/1468-5973.70019 Murakami, K., Kuriyama, S., & Hashimoto, H. (2023). Economic, cognitive, and social paths of education to health-related behaviors: Evidence from a population-based study in Japan. Environmental Health and Preventive Medicine , 28 , 9–9. Muthén, B., & Muthén, L. (2017). Mplus. In Handbook of item response theory (pp. 507–518). Chapman and Hall/CRC. Nakayama, K., Yonekura, Y., Danya, H., & Hagiwara, K. (2022). Associations between health literacy and information-evaluation and decision-making skills in Japanese adults. BMC Public Health , 22 (1), 1473. https://doi.org/10.1186/s12889-022-13892-5 National Health Commission of the PRC, Ministry of Education of the PRC, Ministry of Science and Technology of the PRC, & National Administration of Traditional Chinese Medicine of the PRC. (2023). Ethical Review Measures for Life Science and Medical Research Involving Humans (No. 4 document of the National Health Commission) . http://big5.www.gov.cn/gate/big5/www.gov.cn/zhengce/zhengceku/2023-02/28/content_5743658.htm (in Chinese) Odunsi, I. A., & Farris, K. L. (2025). Predicting College Students’ Preventative Behavior During a Pandemic: The Role of the Health Belief Model, Source Credibility, and Health Literacy. American Behavioral Scientist , 69 (12), 1516–1533. https://doi.org/10.1177/00027642231164044 Orem, D. E., Taylor, S. G., & Renpenning, K. M. (1995). Nursing: Concepts of practice (5th ed). Mosby. Osborne, R. H., Elmer, S., Hawkins, M., Cheng, C. C., Batterham, R. W., Dias, S., Good, S., Monteiro, M. G., Mikkelsen, B., Nadarajah, R. G., & others. (2022). Health literacy development is central to the prevention and control of non-communicable diseases. BMJ Global Health , 7 (12). Öztürk, M. H., Yeşilyaprak, T., & Kuday, A. D. (2025). Enhancing students’ knowledge and self-efficacy through integrated first aid and psychological first aid training. Psychology, Health & Medicine , 1–14. https://doi.org/10.1080/13548506.2025.2506017 Podsakoff, P. M., Podsakoff, N. P., Williams, L. J., Huang, C., & Yang, J. (2024). Common Method Bias: It’s Bad, It’s Complex, It’s Widespread, and It’s Not Easy to Fix. Annual Review of Organizational Psychology and Organizational Behavior , 11 (1), 17–61. https://doi.org/10.1146/annurev-orgpsych-110721-040030 Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management & Data Systems , 121 (12), 2637–2650. Rosário, J., Pires, J. C., Dias, S., & Pedro, A. R. (2025). Exploring perceptions of health literacy, healthcare access, and utilisation among higher education students in Alentejo, Southern Portugal: A qualitative study. PLOS One , 20 (6), e0326575. https://doi.org/10.1371/journal.pone.0326575 Rüegg, R. (2022). Decision-making ability: A missing link between health literacy, contextual factors, and health. HLRP: Health Literacy Research and Practice , 6 (3), e213–e223. Saldert, C., Jensen, L. R., Blom Johansson, M., & Simmons-Mackie, N. (2018). Complexity in measuring outcomes after communication partner training: Alignment between goals of intervention and methods of evaluation. Aphasiology , 32 (10), 1167–1193. https://doi.org/10.1080/02687038.2018.1470317 Schwarzer, R., & Luszczynska, A. (2008). How to Overcome Health-Compromising Behaviors: The Health Action Process Approach. European Psychologist , 13 (2), 141–151. https://doi.org/10.1027/1016-9040.13.2.141 Shoji, Y., Irwan, A. M., Ochiai, R., Syahrul, S., Shinohara, E., Fiqri, A. M., Takeuchi, S., Erfina, E., Iida, M., Saleh, A., Moriguchi, F., Nakamura, S., & Kanoya, Y. (2025). The Impact of eHealth Literacy on Health Behaviors for Non-communicable Disease Prevention Among University Students in Japan and Indonesia. Cureus . https://doi.org/10.7759/cureus.78450 Sørensen, K., Van Den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., Slonska, Z., Brand, H., & (HLS-EU) Consortium Health Literacy Project European. (2012). Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health, 无 , 12 (1), 80. Q2 (医学3区). https://doi.org/10.1186/1471-2458-12-80 Stormacq, C., Oulevey Bachmann, A., Van Den Broucke, S., & Bodenmann, P. (2023). How socioeconomically disadvantaged people access, understand, appraise, and apply health information: A qualitative study exploring health literacy skills. PLOS ONE , 18 (8), e0288381. https://doi.org/10.1371/journal.pone.0288381 Suri, V. R., Majid, S., Foo, S., Dumaual-Sibal, H. T., & Chang, Y.-K. (2019). Understanding Health Literacy Through the Lens of Phronesis: The Case of Coronary Artery Disease Patients. In S. Kurbanoğlu, S. Špiranec, Y. Ünal, J. Boustany, M. L. Huotari, E. Grassian, D. Mizrachi, & L. Roy (Eds.), Information Literacy in Everyday Life (Vol. 989, pp. 166–175). Springer International Publishing. https://doi.org/10.1007/978-3-030-13472-3_16 Tang, Y., Lin, C. T., & Wu, L. (2025). Understanding Health Literacy Through Patients’ Interpretation of Health Education Leaflets: A Thematic Narrative Review. Health Expectations , 28 (6), e70479. https://doi.org/10.1111/hex.70479 Taylor, S. E., Way, B. M., & Seeman, T. E. (2011). Early adversity and adult health outcomes. Development and Psychopathology , 23 (3), 939–954. https://doi.org/10.1017/S0954579411000411 Team, R. C. (2020). RA language and environment for statistical computing, R Foundation for Statistical. Computing. Archives for Scientific Computing . Tempone-Wiltshire, J., & Matthews, F. (2025). Embodied Minds: An Embodied Cognitivist Understanding of Mindfulness in Public Health. Mindfulness , 16 (3), 725–737. https://doi.org/10.1007/s12671-024-02423-5 Tian, C. Y., Ng, C. C. W., Xie, L., Mo, P. K., Dong, D., Nutbeam, D., & Wong, E. L. (2025). Conceptualisation of critical health literacy—Insights from Western and East Asian perspectives: A scoping review. BMJ Global Health , 10 (5). Wang, L., Norman, I., Xiao, T., Li, Y., Li, X., Liu, T., Wang, J., Zeng, L., Zhong, Z., Jian, C., & others. (2024). Feasibility and acceptability of a culturally adapted psychological first aid training intervention (Preparing Me) to support the mental health and well-being of front-line healthcare workers in China: A feasibility randomized controlled trial. European Journal of Psychotraumatology , 15 (1), 2299195. Wang, S., Wei, J., Zhang, P., Song, J., Chen, J., & Li, G. (2025). The chain mediating effect of self-efficacy and health literacy between proactive personality and health-promoting behaviors among Chinese college students. Scientific Reports , 15 (1), 16101. https://doi.org/10.1038/s41598-025-00936-0 Watkins, M. W. (2021). A Step-by-Step Guide to Exploratory Factor Analysis with SPSS (1st ed.). Routledge. https://doi.org/10.4324/9781003149347 Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety. Educational and Psychological Measurement , 73 (6), 913–934. https://doi.org/10.1177/0013164413495237 World Medical Association. (2001). World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bulletin of the World Health Organization , 79 (4), 373–374. Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Methods , 51 (1), 409–428. https://doi.org/10.3758/s13428-018-1055-2 Yusoff, M. S. B. (2019). ABC of content validation and content validity index calculation. Education in Medicine Journal , 11 (2), 49–54. Zeng, M., Liu, Y., He, Y., & Huang, W. (2025). Correction: Relationship between stroke knowledge, health information literacy, and health self-management among patients with stroke: Multicenter cross-sectional study. JMIR Medical Informatics , 13 , e80547. Zhang, M. J., Guo, X., Wang, R. S., Mao, X., Xiang, G., Li, W., Zuo, C., Zhou, H., & Xu, D. R. (2025). Health literacy model integrating health education, health behaviors, self-rated health, and socioeconomic status in the Chinese population. Scientific Reports , 15 (1), 32320. https://doi.org/10.1038/s41598-025-07094-3 Zhou, Y., Xu, J., Wang, R., & Guan, X. (2025). Understanding how digital health literacy affects health self-management behaviors: The mediating role of self-efficacy in college students. Scientific Reports , 15 (1), 27230. https://doi.org/10.1038/s41598-025-12726-9 Zhu, W., Liu, J., Lou, H., Mu, F., & Li, B. (2024). The impact of electronic health literacy on emotional management ability among college students: The mediating roles of peer relationships and exercise self-efficacy. BMC Psychology , 12 (1), 747. https://doi.org/10.1186/s40359-024-02276-6 Alosaimi, N., Sherar, L. B., Griffiths, P., & Pearson, N. (2023). Clustering of diet, physical activity and sedentary behaviour and related physical and mental health outcomes: A systematic review. BMC Public Health , 23 (1), 1572. https://doi.org/10.1186/s12889-023-16372-6 Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist , 55 (5), 469–480. https://doi.org/10.1037/0003-066X.55.5.469 Association, A. P. & others. (2020). Publication manual of the American psychological association 2020. American Psychological Association . Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory . Prentice-hall. Bandura, A. (1997). Self-efficacy: The exercise of control . Macmillan. Bao, X., Chen, D., Shi, L., Xia, Y., Shi, Z., & Wang, D. (2022). The relationship between COVID-19-related prevention cognition and healthy lifestyle behaviors among university students: Mediated by e-health literacy and self-efficacy. Journal of Affective Disorders , 309 , 236–241. https://doi.org/10.1016/j.jad.2022.04.044 Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., Van Borkulo, C. D., Van Bork, R., & Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers , 1 (1), 58. https://doi.org/10.1038/s43586-021-00055-w Bourke, M., Brown, D., & Kwan, M. Y. W. (2025). Lifestyle Behavior Patterns During the Transition From Adolescence to Emerging Adulthood: Associations With Mental Health and Wellbeing. Emerging Adulthood , 13 (6), 1381–1394. https://doi.org/10.1177/21676968251376750 Burnham, K. P., & Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research , 33 (2), 261–304. https://doi.org/10.1177/0049124104268644 Carpenter, S. (2018). Ten Steps in Scale Development and Reporting: A Guide for Researchers. Communication Methods and Measures , 12 (1), 25–44. https://doi.org/10.1080/19312458.2017.1396583 Chao, D.-P. (2023). Health-promoting lifestyle and its predictors among health-related and non-health-related university students in Taiwan: A cross-sectional quantitative study. BMC Public Health , 23 (1), 827. https://doi.org/10.1186/s12889-023-15760-2 Chen, X., & Xiao, H. (2025). E-health literacy and health-promoting behaviors among nursing students in China: The mediating role of self-efficacy. Psychology, Health & Medicine , 30 (8), 1757–1767. https://doi.org/10.1080/13548506.2025.2481193 Cohen, J. (2013). Statistical power analysis for the behavioral sciences . routledge. DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications . Sage publications. Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods , 50 (1), 195–212. https://doi.org/10.3758/s13428-017-0862-1 Fleary, S. A., Joseph, P., & Pappagianopoulos, J. E. (2018). Adolescent health literacy and health behaviors: A systematic review. Journal of Adolescence , 62 (1), 116–127. https://doi.org/10.1016/j.adolescence.2017.11.010 Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 无 , 18 (1), 39–50. Goretzko, D., Pham, T. T. H., & Bühner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology , 40 (7), 3510–3521. https://doi.org/10.1007/s12144-019-00300-2 Guo, S., Fu, H., & Guo, K. (2024). Effects of physical activity on subjective well-being: The mediating role of social support and self-efficacy. Frontiers in Sports and Active Living , 6 , 1362816. https://doi.org/10.3389/fspor.2024.1362816 Habiba, U., & Koli, F. S. (2024). The Mediating Role of Students’ Health Information Literacy Skills: Exploring the Relationship Between Web Resource Utilization and Health Information Evaluation Proficiency. Health Expectations , 27 (4), e14176. https://doi.org/10.1111/hex.14176 Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis, 7th edn. Hoboken . NJ: Prentice hall. Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach . Guilford publications. Jones, P. J., Ma, R., & McNally, R. J. (2021). Bridge Centrality: A Network Approach to Understanding Comorbidity. Multivariate Behavioral Research , 56 (2), 353–367. https://doi.org/10.1080/00273171.2019.1614898 Kamath, A., Poojari, S., & Varsha, K. (2025). Assessing the robustness of normality tests under varying skewness and kurtosis: A practical checklist for public health researchers. BMC Medical Research Methodology , 25 (1), 206. https://doi.org/10.1186/s12874-025-02641-y Kim, S., & Oh, J. (2021). The Relationship between E-Health Literacy and Health-Promoting Behaviors in Nursing Students: A Multiple Mediation Model. International Journal of Environmental Research and Public Health , 18 (11), 5804. https://doi.org/10.3390/ijerph18115804 Kline, R. B. (2023). Principles and practice of structural equation modeling . Guilford publications. Li, S., Cui, G., Zhou, F., Liu, S., Guo, Y., Yin, Y., & Xu, H. (2022). The longitudinal relationship between eHealth literacy, health-promoting lifestyles, and health-related quality of life among college students: A cross-lagged analysis. Frontiers in Public Health , 10 , 868279. Liu, C., Chen, Z., Qin, H., Yang, Y., Cui, X., Lei, R., & Li, B. (2025). Health Knowledge Acquisition and Health Communication Ability of Nurses: A Chain Mediating Model of Health Literacy and Health Education Competence. Journal of Advanced Nursing , 81 (9), 5903–5913. https://doi.org/10.1111/jan.16728 Liu, X., Wang, Z., Zhang, C., Xu, J., Shen, Z., Peng, L., Mi, Y., & Xu, H. (2024). Psychological Capital and Its Factors as Mediators Between Interpersonal Sensitivity and Depressive Symptoms Among Chinese Undergraduates. Psychology Research and Behavior Management , Volume 17 , 429–441. https://doi.org/10.2147/PRBM.S452993 Lv, C., Wang, Z., Cui, H., Zhang, K., Wang, X., Wang, X., Alsudais, T. A., & Duan, S. (2025). Investigating the influence of adolescents’ social and emotional skills on health behavior: A moderated mediation analysis. Frontiers in Psychology , 16 , 1712176. https://doi.org/10.3389/fpsyg.2025.1712176 McAnally, K., & Hagger, M. S. (2023). Health literacy, social cognition constructs, and health behaviors and outcomes: A meta-analysis. Health Psychology , 42 (4), 213. McNeish, D. (2025). Dynamic measurement invariance cutoffs for two-group fit index differences. Psychological Methods . McNeish, D., & Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods , 28 (1), 61–88. https://doi.org/10.1037/met0000425 Mtiraoui, A., Mahjoubi, H., Achour, A., Ghardallou, M., & Nakhli, J. (2025). The Impact of Psychological First Aid Training (RAPID‐PFA) on Self‐Efficacy, Perceived Competencies and Disaster Preparedness of Nursing Students in Tunisian Public Institutions: A Randomized Controlled Trial. Journal of Contingencies and Crisis Management , 33 (1), e70019. https://doi.org/10.1111/1468-5973.70019 Murakami, K., Kuriyama, S., & Hashimoto, H. (2023). Economic, cognitive, and social paths of education to health-related behaviors: Evidence from a population-based study in Japan. Environmental Health and Preventive Medicine , 28 , 9–9. Muthén, B., & Muthén, L. (2017). Mplus. In Handbook of item response theory (pp. 507–518). Chapman and Hall/CRC. Nakayama, K., Yonekura, Y., Danya, H., & Hagiwara, K. (2022). Associations between health literacy and information-evaluation and decision-making skills in Japanese adults. BMC Public Health , 22 (1), 1473. https://doi.org/10.1186/s12889-022-13892-5 Odunsi, I. A., & Farris, K. L. (2025). Predicting College Students’ Preventative Behavior During a Pandemic: The Role of the Health Belief Model, Source Credibility, and Health Literacy. American Behavioral Scientist , 69 (12), 1516–1533. https://doi.org/10.1177/00027642231164044 Orem, D. E., Taylor, S. G., & Renpenning, K. M. (1995). Nursing: Concepts of practice (5th ed). Mosby. Osborne, R. H., Elmer, S., Hawkins, M., Cheng, C. C., Batterham, R. W., Dias, S., Good, S., Monteiro, M. G., Mikkelsen, B., Nadarajah, R. G., & others. (2022). Health literacy development is central to the prevention and control of non-communicable diseases. BMJ Global Health , 7 (12). Öztürk, M. H., Yeşilyaprak, T., & Kuday, A. D. (2025). Enhancing students’ knowledge and self-efficacy through integrated first aid and psychological first aid training. Psychology, Health & Medicine , 1–14. https://doi.org/10.1080/13548506.2025.2506017 Podsakoff, P. M., Podsakoff, N. P., Williams, L. J., Huang, C., & Yang, J. (2024). Common Method Bias: It’s Bad, It’s Complex, It’s Widespread, and It’s Not Easy to Fix. Annual Review of Organizational Psychology and Organizational Behavior , 11 (1), 17–61. https://doi.org/10.1146/annurev-orgpsych-110721-040030 Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2–an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management & Data Systems , 121 (12), 2637–2650. Rosário, J., Pires, J. C., Dias, S., & Pedro, A. R. (2025). Exploring perceptions of health literacy, healthcare access, and utilisation among higher education students in Alentejo, Southern Portugal: A qualitative study. PLOS One , 20 (6), e0326575. https://doi.org/10.1371/journal.pone.0326575 Rüegg, R. (2022). Decision-making ability: A missing link between health literacy, contextual factors, and health. HLRP: Health Literacy Research and Practice , 6 (3), e213–e223. Saldert, C., Jensen, L. R., Blom Johansson, M., & Simmons-Mackie, N. (2018). Complexity in measuring outcomes after communication partner training: Alignment between goals of intervention and methods of evaluation. Aphasiology , 32 (10), 1167–1193. https://doi.org/10.1080/02687038.2018.1470317 Schwarzer, R., & Luszczynska, A. (2008). How to Overcome Health-Compromising Behaviors: The Health Action Process Approach. European Psychologist , 13 (2), 141–151. https://doi.org/10.1027/1016-9040.13.2.141 Shoji, Y., Irwan, A. M., Ochiai, R., Syahrul, S., Shinohara, E., Fiqri, A. M., Takeuchi, S., Erfina, E., Iida, M., Saleh, A., Moriguchi, F., Nakamura, S., & Kanoya, Y. (2025). The Impact of eHealth Literacy on Health Behaviors for Non-communicable Disease Prevention Among University Students in Japan and Indonesia. Cureus . https://doi.org/10.7759/cureus.78450 Sørensen, K., Van Den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., Slonska, Z., Brand, H., & (HLS-EU) Consortium Health Literacy Project European. (2012). Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health, 无 , 12 (1), 80. Q2 (医学3区). https://doi.org/10.1186/1471-2458-12-80 Stormacq, C., Oulevey Bachmann, A., Van Den Broucke, S., & Bodenmann, P. (2023). How socioeconomically disadvantaged people access, understand, appraise, and apply health information: A qualitative study exploring health literacy skills. PLOS ONE , 18 (8), e0288381. https://doi.org/10.1371/journal.pone.0288381 Suri, V. R., Majid, S., Foo, S., Dumaual-Sibal, H. T., & Chang, Y.-K. (2019). Understanding Health Literacy Through the Lens of Phronesis: The Case of Coronary Artery Disease Patients. In S. Kurbanoğlu, S. Špiranec, Y. Ünal, J. Boustany, M. L. Huotari, E. Grassian, D. Mizrachi, & L. Roy (Eds.), Information Literacy in Everyday Life (Vol. 989, pp. 166–175). Springer International Publishing. https://doi.org/10.1007/978-3-030-13472-3_16 Tang, Y., Lin, C. T., & Wu, L. (2025). Understanding Health Literacy Through Patients’ Interpretation of Health Education Leaflets: A Thematic Narrative Review. Health Expectations , 28 (6), e70479. https://doi.org/10.1111/hex.70479 Taylor, S. E., Way, B. M., & Seeman, T. E. (2011). Early adversity and adult health outcomes. Development and Psychopathology , 23 (3), 939–954. https://doi.org/10.1017/S0954579411000411 Taylor, S. G., & Renpenning, K. M. (2011). Self-care science, nursing theory and evidence-based practice . Springer Publishing Company. Team, R. C. (2020). RA language and environment for statistical computing, R Foundation for Statistical. Computing. Archives for Scientific Computing . Tempone-Wiltshire, J., & Matthews, F. (2025). Embodied Minds: An Embodied Cognitivist Understanding of Mindfulness in Public Health. Mindfulness , 16 (3), 725–737. https://doi.org/10.1007/s12671-024-02423-5 Tian, C. Y., Ng, C. C. W., Xie, L., Mo, P. K., Dong, D., Nutbeam, D., & Wong, E. L. (2025). Conceptualisation of critical health literacy—Insights from Western and East Asian perspectives: A scoping review. BMJ Global Health , 10 (5). Wang, L., Norman, I., Xiao, T., Li, Y., Li, X., Liu, T., Wang, J., Zeng, L., Zhong, Z., Jian, C., & others. (2024). Feasibility and acceptability of a culturally adapted psychological first aid training intervention (Preparing Me) to support the mental health and well-being of front-line healthcare workers in China: A feasibility randomized controlled trial. European Journal of Psychotraumatology , 15 (1), 2299195. Wang, S., Wei, J., Zhang, P., Song, J., Chen, J., & Li, G. (2025). The chain mediating effect of self-efficacy and health literacy between proactive personality and health-promoting behaviors among Chinese college students. Scientific Reports , 15 (1), 16101. https://doi.org/10.1038/s41598-025-00936-0 Watkins, M. W. (2021). A Step-by-Step Guide to Exploratory Factor Analysis with SPSS (1st ed.). Routledge. https://doi.org/10.4324/9781003149347 Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety. Educational and Psychological Measurement , 73 (6), 913–934. https://doi.org/10.1177/0013164413495237 World Medical Association. (2001). World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bulletin of the World Health Organization , 79 (4), 373–374. Wu, Y., Howarth, M., Zhou, C., Ji, X., Ou, J., & Li, X. (2019). Reporting of ethical considerations in clinical trials in Chinese nursing journals. Nursing Ethics , 26 (4), 973–983. https://doi.org/10.1177/0969733017722191 Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Methods , 51 (1), 409–428. https://doi.org/10.3758/s13428-018-1055-2 Yusoff, M. S. B. (2019). ABC of content validation and content validity index calculation. Education in Medicine Journal , 11 (2), 49–54. Zeng, M., Liu, Y., He, Y., & Huang, W. (2025). Correction: Relationship between stroke knowledge, health information literacy, and health self-management among patients with stroke: Multicenter cross-sectional study. JMIR Medical Informatics , 13 , e80547. Zhang, M. J., Guo, X., Wang, R. S., Mao, X., Xiang, G., Li, W., Zuo, C., Zhou, H., & Xu, D. R. (2025). Health literacy model integrating health education, health behaviors, self-rated health, and socioeconomic status in the Chinese population. Scientific Reports , 15 (1), 32320. https://doi.org/10.1038/s41598-025-07094-3 Zhou, Y., Xu, J., Wang, R., & Guan, X. (2025). Understanding how digital health literacy affects health self-management behaviors: The mediating role of self-efficacy in college students. Scientific Reports , 15 (1), 27230. https://doi.org/10.1038/s41598-025-12726-9 Zhu, W., Liu, J., Lou, H., Mu, F., & Li, B. (2024). The impact of electronic health literacy on emotional management ability among college students: The mediating roles of peer relationships and exercise self-efficacy. BMC Psychology , 12 (1), 747. https://doi.org/10.1186/s40359-024-02276-6 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Supplementary Materials All analyses reported in this manuscript are fully reproducible using these materials. Supplementary materials include: Table S1 (full scale items with response anchors and source references), Table S2 (descriptive statistics, corrected item-total correlations, and Cronbach's α if item deleted), Table S3 (discriminant validity: Fornell–Larcker criterion), Table S4 (discriminant validity: Heterotrait–monotrait (HTMT) ratios), Table S5 (comprehensive node centrality indices: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence), and Table S6 (sensitivity analysis results across EBICglasso tuning parameters γ = 0.25, 0.50, and 0.75). The supplementary materials are available on the journal's website. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 15 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 12 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9074591","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620407118,"identity":"90082823-ef6f-4a1b-9f30-b8269e6739c7","order_by":0,"name":"Chen Jianfeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACfobjBx98qLBhZmNvIFKLZOOZZMMZZ9LY+XkOEKnF4PABM2nelsP8kjMSiHXZsQNpErwNzNIGNx9vvMFQYxNNUAdjz8HDFpI72IwNbqcVWzAcS8ttIKSFWeJA4g3DMzzJBrdzzCQYGw4T1sIm/8BAIrFNon7DzTNEauFhOGAkcbDNgFlyBg+RWiQYgIHccCaBmZ8H6JcEYvxif+D4wcd/Kv4Do/LwxhsfamwIa0EGBhIJpCiHaCFVxygYBaNgFIwMAABwh0P0VTshgAAAAABJRU5ErkJggg==","orcid":"","institution":"Changzhou Vocational Institute of Textile and Garment","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Jianfeng","suffix":""},{"id":620407119,"identity":"1b2a30f2-653d-4d98-844c-44c90568318d","order_by":1,"name":"Pan Yikang","email":"","orcid":"","institution":"Changzhou Vocational Institute of Textile and Garment","correspondingAuthor":false,"prefix":"","firstName":"Pan","middleName":"","lastName":"Yikang","suffix":""},{"id":620407120,"identity":"bee780ba-deff-4612-b810-61341f919cc5","order_by":2,"name":"Yin Huaigang","email":"","orcid":"","institution":"Nanjing Sport Institute","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Huaigang","suffix":""}],"badges":[],"createdAt":"2026-03-09 15:08:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9074591/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9074591/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106961516,"identity":"dd199772-3901-4d5d-b1a8-797ec944d496","added_by":"auto","created_at":"2026-04-15 09:25:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45087,"visible":true,"origin":"","legend":"\u003cp\u003eChain mediation theoretical model. PSC=psychosocial competence;\u003c/p\u003e\n\u003cp\u003eHPB=health-promoting behavior; HIL=health information literacy; SCA=self-care skills\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9074591/v1/64092ba0cc4031117fdae5c7.png"},{"id":106857436,"identity":"c6b83c92-2015-4636-a514-0f6eb4f8520d","added_by":"auto","created_at":"2026-04-14 07:42:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168977,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized path coefficients of the chain mediation model. All coefficients are significant (p \u0026lt; .05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9074591/v1/041265a5a89fead82abcc9f0.png"},{"id":106857454,"identity":"14126d80-619b-4c38-9b9a-a0d6ae98e2f8","added_by":"auto","created_at":"2026-04-14 07:42:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":390458,"visible":true,"origin":"","legend":"\u003cp\u003eRegularized partial correlation network of 23 health-related items (N = 390). Nodes represent individual items, colored by theoretical community (PSC, HPB, HIL, SCA). Green edges indicate positive regularized partial correlations; red edges indicate negative correlations. Edge thickness reflects the magnitude of the correlation. Network estimated with EBICglasso (γ = 0.5); only edges with |weight| \u0026gt; 0.05 are shown. For complete item labels and descriptions.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9074591/v1/49ea951e8652b585417249bc.png"},{"id":106857437,"identity":"395fc58e-9bb8-4b2c-ba38-8d3b20a95d0b","added_by":"auto","created_at":"2026-04-14 07:42:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":134960,"visible":true,"origin":"","legend":"\u003cp\u003eCommunity Structure and Bridge Strength\u003c/p\u003e\n\u003cp\u003eNote: (A) Community structure strength: within-community (colored) and between-community (gray) connections; dashed lines separate the four communities. (B) Bridge strength ranking: values represent the sum of absolute weights of edges connecting a node to nodes in other communities; red diamonds (◇) indicate the top three bridge nodes. The dashed vertical line marks the overall mean bridge strength (M = 0.255).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9074591/v1/50675feda204c8e21d1d8bcd.png"},{"id":106857453,"identity":"b4d2a403-a725-4411-84d1-6dc8ba7c58aa","added_by":"auto","created_at":"2026-04-14 07:42:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":127572,"visible":true,"origin":"","legend":"\u003cp\u003eBootstrap stability analysis of edge weights.\u003c/p\u003e\n\u003cp\u003eNote: (A) Forest plot of the top 15 edges with 95% bootstrap CIs. (B) Stability distribution of bridge vs. within-community edges; dashed line indicates high stability threshold (0.75).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9074591/v1/2fe6dbbff4747329d9850453.png"},{"id":106963374,"identity":"cb7dff57-2cd8-43f3-9602-fd21a4d71383","added_by":"auto","created_at":"2026-04-15 09:44:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1817733,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9074591/v1/f5d87e1f-72d2-407b-b380-10539c688ace.pdf"},{"id":106857438,"identity":"01d7623e-3690-4c76-9a45-bf9af2637c4b","added_by":"auto","created_at":"2026-04-14 07:42:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27381,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses reported in this manuscript are fully reproducible using these materials. Supplementary materials include: Table S1 (full scale items with response anchors and source references), Table S2 (descriptive statistics, corrected item-total correlations, and Cronbach's α if item deleted), Table S3 (discriminant validity: Fornell–Larcker criterion), Table S4 (discriminant validity: Heterotrait–monotrait (HTMT) ratios), Table S5 (comprehensive node centrality indices: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence), and Table S6 (sensitivity analysis results across EBICglasso tuning parameters γ = 0.25, 0.50, and 0.75). The supplementary materials are available on the journal's website.\u003c/p\u003e","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9074591/v1/45a4b77f19931162d53f6097.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multiple Mediating Pathways from Psychosocial Competence to Self-Care Skills in University Students: A Cross-Validation Study Using Structural Equation Modeling and Network Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe transition from adolescence to adulthood represents a critical developmental window during which health beliefs, behavioral patterns, and self-management skills are formed, profoundly shaping lifelong health(Fleary et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Arnett, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). University students face specific challenges and opportunities as they move toward independent living and assume greater responsibility for their health (Ros\u0026aacute;rio et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, a considerable proportion of young adults exhibit inadequate self-care skills, manifesting as low health information literacy (Bao et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), a lack of sustained engagement in health-promoting behaviors (S. Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and limited capacity for autonomous disease prevention (Odunsi \u0026amp; Farris, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, a thorough understanding of the underlying mechanisms shaping self-care skills development in this population is crucial for designing targeted early intervention strategies aimed at mitigating chronic disease risk and promoting lifelong health (Alosaimi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe development of self-care skills is rooted in psychosocial competence, which itself develops through repeated health behaviors and the active acquisition of health knowledge. This study is grounded in social cognitive theory (Bandura, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), the integrated model of health literacy (S\u0026oslash;rensen et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and Orem\u0026apos;s self-care theory (Orem et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). These frameworks guide a serial mediation model: \u0026quot;competence \u0026rarr; behavior \u0026rarr; cognition \u0026rarr; skill\u0026quot;. Social cognitive theory explains how competence drives behavior, emphasizing psychosocial capacities, especially self-efficacy, in motivating health actions (Bandura, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This study\u0026apos;s psychosocial competence construct builds on the health action process approach (Schwarzer \u0026amp; Luszczynska, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and serves as a multidimensional resource for health challenges. The construct is broader than general self-efficacy. encompassing life values, social participation, and psychological resilience in health contexts (Chen \u0026amp; Xiao, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lv et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Individuals with higher psychosocial competence are more likely to initiate and sustain health-promoting behaviors such as regular exercise, a balanced diet, and effective stress management ((Guo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This line of reasoning supports our proposed pathway.\u003c/p\u003e\n\u003cp\u003eHowever, building stable self-care skills requires more than behavioral engagement; it also necessitates a cognitive shift. The Health Literacy Integration Model posits that engaging in health behaviors generates the need for health information. which prompts individuals to seek, appraise, and use health information, thereby developing health information literacy. Habiba and Koli (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) define health information literacy as a higher-order cognitive ability that includes accessing, understanding, evaluating, and applying health information for health-related decisions (Habiba \u0026amp; Koli, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Health-promoting behaviors are not merely outcomes; they also create opportunities for frequent engagement with health information. Repeated practice further consolidates health information literacy. Orem\u0026apos;s self-care theory and other research\u0026mdash;although demonstrated in professional groups such as nurses (C. Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u0026mdash;concur that advanced self-care skills, such as health monitoring and risk identification, require deep health knowledge\u0026mdash;not just surface learning. Notably, different theories offer divergent perspectives on the temporal order of cognition and behavior. Social Cognitive Theory posits that cognition (e.g., self-efficacy) drives behavior, whereas the Health Literacy Integration Model emphasizes the role of behavior in shaping cognition (Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Our \u0026quot;competence \u0026rarr; behavior \u0026rarr; cognition \u0026rarr; skill\u0026quot; chain mediation model integrates these sequences, positioning them as mutually reinforcing rather than mutually exclusive. This pathway underscores the behavior-to-cognition link. Practice in real-world health situations scaffolds health information literacy (Stormacq et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Real-world challenges provide the necessary context for learning; abstract learning lacks both context and motivation (Tang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Research on embodied cognition supports this view: health knowledge is best acquired in authentic scenarios (Tempone-Wiltshire \u0026amp; Matthews, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBased on this foundation, we propose a core hypothesis: psychosocial competence influences self-care skills through mediating mediations. The mediators are health-promoting behaviors and health information literacy, encompassing both parallel mediation and a chain mediation pathway: competence \u0026rarr; behavior \u0026rarr; cognition \u0026rarr; skill. To test this, we pose three questions. (1) Do health-promoting behaviors and health information literacy together mediate the relationship between psychosocial competence and self-care skills? (2) Does a significant chain mediation effect occur along the competence \u0026rarr; behavior \u0026rarr; cognition \u0026rarr; skill pathway? (3) At the indicator level, which variables serve as bridges between psychosocial competence, health behavior, health information literacy, and self-care skills? We employ a combined strategy: structural equation modeling (Kline, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and network analysis (Borsboom et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). SEM tests relationships among latent variables and their pathways, including direct, indirect, and chain effects. Network analysis deconstructs constructs into empirical networks among indicators and reveals high-bridge centrality nodes that integrate domains. Together, these methods provide a broad and detailed understanding of how psychosocial competence shapes self-care skills. The theoretical model is presented in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants and Procedure\u003c/h2\u003e \u003cp\u003eThis cross-sectional study recruited first- and second-year undergraduate students from a comprehensive university. Sample size was estimated using two criteria. G*Power software (f\u0026sup2; = 0.15, α\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;0.95, predictors\u0026thinsp;=\u0026thinsp;3) indicated a minimum of 119 participants (Cohen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Simulations by Epskamp (2018) suggested that network stability (CS coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.5) with 23 nodes requires at least 345 participants. Our final sample was 390 participants. This met the structural equation modeling threshold (\u0026gt;\u0026thinsp;200 cases) (Wolf et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and network analysis requirements. Data were collected via the Questionnaire Star platform during spring 2025 using convenience sampling. Participants completed anonymous surveys within 15\u0026ndash;20 minutes. As items related to self-care skills might involve traumatic health experiences, a trigger warning was provided at the beginning. The warning told participants they could skip sensitive items or withdraw at any time. This study used only anonymous survey procedures. It qualified for exemption from formal ethical review under Article 32 of the Chinese \"Ethical Review Measures for Life Science and Medical Research Involving Humans\" (National Health Commission of the PRC et al., 2023). The study followed the Declaration of Helsinki (World Medical Association., 2001). The study protocol was reviewed internally, and permission to conduct the research was granted by the Department of Physical Education at Changzhou Vocational Institute of Textile and Garment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cp\u003eGiven the lack of existing scales fully aligning with the integrated constructs of this study, a scale was developed following standardized procedures (DeVellis \u0026amp; Thorpe, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Domain definitions were established through literature review and expert panel discussions (comprising 3 health psychology professors, 2 nursing professors, and 2 undergraduate student representatives), generating an initial item pool of 26 items (Carpenter, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Expert content validity assessment yielded item-level content validity indices (I-CVIs) ranging from 0.87 to 0.93, leading to the deletion of irrelevant or redundant items (Yusoff, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). During pilot testing (n\u0026thinsp;=\u0026thinsp;48), item analysis and exploratory factor analysis (EFA) were conducted: item analysis eliminated poorly discriminating items (critical ratio, i.e., t-value, \u0026lt; 3.0 or item-total correlation\u0026thinsp;\u0026lt;\u0026thinsp;0.40) (Watkins, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); EFA using principal component analysis with varimax rotation extracted a four-factor structure, and items with factor loadings\u0026thinsp;\u0026lt;\u0026thinsp;0.50 or cross-loadings\u0026thinsp;\u0026gt;\u0026thinsp;0.40 were removed, resulting in 3 deleted items (Goretzko et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Confirmatory factor analysis (CFA) was performed on the remaining 23 items using the formal sample (N\u0026thinsp;=\u0026thinsp;390) to evaluate the four-factor model fit (McNeish \u0026amp; Wolf, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), retaining all 23 items (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for full items). The scale employed a 5-point Likert scoring system (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree).\u003c/p\u003e \u003cp\u003eAll scales demonstrated satisfactory structural validity via CFA, with fit indices as follows:\u003c/p\u003e \u003cp\u003ePsychosocial Competence (6 items) assessed comprehensive capacities in self-awareness, life values, social engagement, cross-cultural competence, resource utilization, and psychological resilience (Schwarzer \u0026amp; Luszczynska, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). CFA indicated good fit: χ\u0026sup2;(9)\u0026thinsp;=\u0026thinsp;13.52, p\u0026thinsp;=\u0026thinsp;0.141, CFI\u0026thinsp;=\u0026thinsp;0.994, TLI\u0026thinsp;=\u0026thinsp;0.989, RMSEA\u0026thinsp;=\u0026thinsp;0.036, SRMR\u0026thinsp;=\u0026thinsp;0.015; Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.946.\u003c/p\u003e \u003cp\u003eHealth-Promoting Behaviors (5 items) evaluated engagement in nutrition, sleep, screen time, and physical activity (Chao, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). CFA results: χ\u0026sup2;(5)\u0026thinsp;=\u0026thinsp;12.47, p\u0026thinsp;=\u0026thinsp;0.029, CFI\u0026thinsp;=\u0026thinsp;0.985, TLI\u0026thinsp;=\u0026thinsp;0.969, RMSEA\u0026thinsp;=\u0026thinsp;0.062, SRMR\u0026thinsp;=\u0026thinsp;0.019; Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.921.\u003c/p\u003e \u003cp\u003eHealth Information Literacy (6 items) measured cognitive abilities in accessing, evaluating, communicating, and applying health information (Nutbeam, 2008; S\u0026oslash;rensen et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). CFA: χ\u0026sup2;(9)\u0026thinsp;=\u0026thinsp;23.91, p\u0026thinsp;=\u0026thinsp;0.005, CFI\u0026thinsp;=\u0026thinsp;0.978, TLI\u0026thinsp;=\u0026thinsp;0.964, RMSEA\u0026thinsp;=\u0026thinsp;0.065, SRMR\u0026thinsp;=\u0026thinsp;0.022; Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.951.\u003c/p\u003e \u003cp\u003eSelf-Care Skills (6 items) assessed practical competencies in health monitoring, first aid, risk identification, and emergency equipment use ((Orem et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). CFA: χ\u0026sup2;(9)\u0026thinsp;=\u0026thinsp;22.31, p\u0026thinsp;=\u0026thinsp;0.008, CFI\u0026thinsp;=\u0026thinsp;0.981, TLI\u0026thinsp;=\u0026thinsp;0.969, RMSEA\u0026thinsp;=\u0026thinsp;0.062, SRMR\u0026thinsp;=\u0026thinsp;0.020; Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.948. Item analysis indicated that all items across the scales exhibited good discriminability, with corrected item-total correlations ranging from 0.76 to 0.89 (see Supplementary Table S2 for detailed results). Cronbach's α values for all dimensions exceeded 0.92, demonstrating high internal consistency reliability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Analysis\u003c/h2\u003e \u003cp\u003eStructural Equation Modeling (SEM): Psychosocial competence was specified as the independent variable, self-care skills as the dependent variable, and health-promoting behaviors, along with health information literacy as mediating variables. Model estimation was performed using Mplus 8.3 (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) with maximum likelihood (ML) estimation. Mediation effects were tested using the bias-corrected bootstrap method (5,000 resamples) (Hayes, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Model fit was evaluated based on the following criteria: CFI\u0026thinsp;\u0026ge;\u0026thinsp;0.90, TLI\u0026thinsp;\u0026ge;\u0026thinsp;0.90, RMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.08, and SRMR\u0026thinsp;\u0026le;\u0026thinsp;0.08 (Xia \u0026amp; Yang, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Model fit superiority was assessed by comparing AIC/BIC values between the serial mediation model and a constrained parallel mediation model in which the HPB \u0026rarr; HIL path was fixed to zero (i.e., a parallel mediation model). Network Analysis: Conducted using R 4.1.3 (Team, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with the qgraph 1.9.5 and bootnet 1.5 packages. The network included 23 observed variables as nodes. Based on the Spearman correlation matrix, a regularized partial correlation network was estimated via the EBICglasso algorithm (γ\u0026thinsp;=\u0026thinsp;0.50, nlambda\u0026thinsp;=\u0026thinsp;100, lambda.min.ratio\u0026thinsp;=\u0026thinsp;0.1, with no penalization of diagonal elements) (Borsboom et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Missing data were handled using pairwise deletion. In the network visualization, positive edges were represented in green and negative edges in red; only edges with absolute weights\u0026thinsp;\u0026gt;\u0026thinsp;0.05 were retained, and node layout was determined using the Fruchterman\u0026ndash;Reingold algorithm. Bridge strength was quantified using the Bridge Strength index (Jones et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and community structure was defined a priori based on four latent variables corresponding to four communities. Edge weight stability was assessed via 1,000 nonparametric bootstrap samples, and the correlation stability (CS) coefficient was computed. To evaluate the robustness of the network estimation, sensitivity analyses were conducted using alternative tuning parameters (γ\u0026thinsp;=\u0026thinsp;0.25, 0.75).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Descriptive Statistics and Correlation Analysis\u003c/h2\u003e\n \u003cp\u003eAs shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the absolute values of skewness (0.003\u0026ndash;0.196) and kurtosis (0.119\u0026ndash;0.439) for all observed variables were below the empirical thresholds (skewness\u0026thinsp;\u0026lt;\u0026thinsp;2, kurtosis\u0026thinsp;\u0026lt;\u0026thinsp;7) (Kamath et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Data points in the Q-Q plots were approximately distributed along the theoretical diagonal, satisfying the prerequisites for parametric tests. The four core latent variables exhibited significant pairwise positive correlations, with correlation coefficients ranging from 0.636 to 0.825 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Common method bias was assessed using the unmeasured latent method factor approach (Podsakoff et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The results indicated minimal changes in model fit after introducing the method factor (\u0026Delta;CFI\u0026thinsp;=\u0026thinsp;0.002, \u0026Delta;TLI = -0.002, \u0026Delta;RMSEA\u0026thinsp;=\u0026thinsp;0.001), suggesting no substantial common method bias in this study (McNeish,\u0026nbsp;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics and Correlation Matrix (N\u0026thinsp;=\u0026thinsp;390)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1. PSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e2.2145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.59392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2. HPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e2.3831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.61950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.636**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3. HIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e2.1949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.59194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.825**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.663**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e4. SCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e2.2919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.60930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.774**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.661**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.805**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNote: PSC\u0026thinsp;=\u0026thinsp;mean score of psychosocial competence; HPB\u0026thinsp;=\u0026thinsp;mean score of health-promoting behavior; HIL\u0026thinsp;=\u0026thinsp;mean score of health information literacy; SCA\u0026thinsp;=\u0026thinsp;mean score of Self-Care Skills. Bolded coefficients indicate correlations among variables within the same theoretical community. According to Cohen (1988), r\u0026thinsp;\u0026ge;\u0026thinsp;0.5 is considered a strong correlation (Cohen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). \u0026lowast;p\u0026lt;.05, \u0026lowast;\u0026lowast;p\u0026lt;.01, \u0026lowast;\u0026lowast;\u0026lowast;p\u0026lt;.001 (American Psychological Association, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), same below.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Measurement Model\u003c/h2\u003e\n \u003cp\u003eConfirmatory factor analysis supported the four-factor measurement model, indicating an acceptable to good fit: \u0026chi;\u0026sup2;(224)\u0026thinsp;=\u0026thinsp;730.329, CFI\u0026thinsp;=\u0026thinsp;0.946, TLI\u0026thinsp;=\u0026thinsp;0.939, RMSEA\u0026thinsp;=\u0026thinsp;0.076 (90% CI [0.071, 0.081]), SRMR\u0026thinsp;=\u0026thinsp;0.035. All constructs demonstrated adequate internal consistency and convergent validity, with composite reliability (CR) values ranging from 0.921 to 0.950 and average variance extracted (AVE) values from 0.700 to 0.759, both exceeding recommended thresholds (Hair et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Discriminant validity was further established via the Fornell\u0026ndash;Larcker criterion (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Cheung et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (see Table S3, with bolded diagonal elements representing the square root of AVE exceeding off-diagonal correlations) and by examining heterotrait\u0026ndash;monotrait (HTMT) ratios (Roemer et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), all of which were below 0.85 (see Supplementary Material Table S4). Residual analysis revealed no systematic misfit: over 95% of standardized residuals fell within \u0026plusmn;\u0026thinsp;2.58, and all latent variable residual variances were positive\u0026mdash;indicating the absence of estimation anomalies such as Heywood cases (Kline, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Chain Mediation Model Test\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, psychosocial competence exerted a significant total effect on self-care skills (\u0026beta;\u0026thinsp;=\u0026thinsp;0.885, 95% CI [0.760, 1.014], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The direct effect remained statistically significant after accounting for mediators (\u0026beta;\u0026thinsp;=\u0026thinsp;0.269, 95% CI [0.045, 0.498], p\u0026thinsp;=\u0026thinsp;0.021), and the full model accounted for 76.7% of the variance in self-care skills (R\u0026sup2; = 0.767). Three distinct indirect pathways emerged: (1) health-promoting behavior as an independent mediator (\u0026beta;\u0026thinsp;=\u0026thinsp;0.126, p\u0026thinsp;=\u0026thinsp;0.020); (2) health information literacy as an independent mediator (\u0026beta;\u0026thinsp;=\u0026thinsp;0.385, p\u0026thinsp;=\u0026thinsp;0.001); and (3) the chain pathway through \u0026quot;health-promoting behavior \u0026rarr; health information literacy\u0026rdquo; (\u0026beta;\u0026thinsp;=\u0026thinsp;0.083, p\u0026thinsp;=\u0026thinsp;0.002). The total indirect effect was 0.594 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), representing 67.1% of the total effect. Of this, the chain mediation effect constituted 9.4% of the total effect and 14.0% of the total indirect effect. A nested model comparison strongly favored the chain mediation specification over the constrained parallel model (\u0026Delta;\u0026chi;\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;29.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026Delta;AIC\u0026thinsp;=\u0026thinsp;27.841), providing robust statistical support for the hypothesized directional sequence (Burnham \u0026amp; Anderson,\u0026nbsp;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePath Coefficients and Effect Decomposition for the Chain Mediation Model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirect effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePSC \u0026rarr; HPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.560, 0.769]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePSC \u0026rarr; HIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.590, 0.835]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHPB \u0026rarr; HIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.105, 0.368]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHPB \u0026rarr; SCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.058, 0.326]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHIL \u0026rarr; SCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.267, 0.727]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePSC \u0026rarr; SCA (direct)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.045, 0.498]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndirect effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePSC \u0026rarr; HPB \u0026rarr; SCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.041, 0.253]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePSC \u0026rarr; HIL \u0026rarr; SCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.188, 0.634]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePSC \u0026rarr; HPB \u0026rarr; HIL \u0026rarr; SCA (chain)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.041, 0.150]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal indirect effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.375, 0.847]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.760, 1.014]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eProportion mediated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.421, 0.946]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eProportion chain mediation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e[0.045, 0.168]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: \u0026beta;\u0026thinsp;=\u0026thinsp;standardized coefficient; SE\u0026thinsp;=\u0026thinsp;standard error; CI\u0026thinsp;=\u0026thinsp;confidence interval. Proportion mediated\u0026thinsp;=\u0026thinsp;total indirect effect / total effect. Proportion chain mediation\u0026thinsp;=\u0026thinsp;chain indirect effect / total effect. \u0026mdash; = not applicable.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Network Structure Analysis\u003c/h2\u003e\n \u003cp\u003eNetwork estimation was conducted using the EBICglasso algorithm applied to Spearman rank-order correlations (tuning parameter \u0026gamma;\u0026thinsp;=\u0026thinsp;0.5), yielding an initial network comprising 23 nodes and 118 edges (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Edge pruning was performed using a conservative weight threshold of |\u0026rho;| \u0026gt; 0.05, retaining 63 edges and resulting in a network density of 0.249. Of these, 59 edges were positive and 4 were negative; the mean node predictability\u0026mdash;quantifying the proportion of variance in each node explained by its neighbors\u0026mdash;was 0.729, indicating moderate-to-strong local stability. Structural inspection revealed that 14 of the top 15 strongest edges (ranked by absolute weight) represented intra-community connections, underscoring cohesive substructures within latent constructs. The strongest edge was SCA3\u0026ndash;SCA5 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.380), followed by HPB1\u0026ndash;HPB2 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.346), HIL2\u0026ndash;HIL4 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.332), and HIL5\u0026ndash;HIL6 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.331). Among inter-community edges, the strongest positive association was observed between PSC5 and HIL3 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.187). All four negative edges occurred exclusively between the Psychosocial Competence (PSC) and Self-Care Skills (SCA) communities, with absolute weights ranked as follows: PSC2\u0026ndash;SCA3 (\u0026rho; = \u0026minus;0.157), PSC4\u0026ndash;SCA1 (\u0026rho; = \u0026minus;0.132), PSC5\u0026ndash;SCA2 (\u0026rho; = \u0026minus;0.096), and PSC2\u0026ndash;SCA5 (\u0026rho; = \u0026minus;0.088). These cross-construct inhibitory associations warrant theoretical and clinical attention, particularly given their consistent directionality and localization.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Bridge Centrality Analysis\u003c/h2\u003e\n \u003cp\u003eCommunity detection (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) identified four distinct communities corresponding to the theoretical constructs: Psychosocial Competence (PSC), Self-Care Skills (SCA), Health-Promoting Behavior (HPB), and Health Information Literacy (HIL). Bridge strength\u0026mdash;the sum of absolute edge weights connecting a node to nodes in *other* communities\u0026mdash;was computed for all 23 nodes. Mean bridge strength was higher in non-mediating communities (PSC and SCA: M\u0026thinsp;=\u0026thinsp;0.275, SD\u0026thinsp;=\u0026thinsp;0.128) than in mediating communities (HPB and HIL: M\u0026thinsp;=\u0026thinsp;0.234, SD\u0026thinsp;=\u0026thinsp;0.124), suggesting that nodes representing antecedent and outcome constructs serve as relatively stronger structural bridges across the network. As shown in Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, the three nodes with the highest bridge strength were SCA1 (\u0026ldquo;Interpret fitness test data\u0026rdquo;, bridge strength\u0026thinsp;=\u0026thinsp;0.572), HIL4 (\u0026ldquo;Make decisions based on information\u0026rdquo;, bridge strength\u0026thinsp;=\u0026thinsp;0.480), and SCA3 (\u0026ldquo;Handle common injuries/emergencies\u0026rdquo;, bridge strength\u0026thinsp;=\u0026thinsp;0.383). These findings were robust across centrality metrics: one-step bridge expected influence\u0026mdash;a directional measure capturing the net influence a node exerts on nodes outside its own community\u0026mdash;exhibited near-perfect convergence with bridge strength (r\u0026thinsp;=\u0026thinsp;0.94); the same three nodes ranked first (SCA1: 0.541), second (HIL4: 0.463), and third (SCA3: 0.372). Full bridge centrality indices\u0026mdash;including bridge strength, bridge betweenness, bridge closeness, and bridge expected influence\u0026mdash;are reported in Supplementary Table S5.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Network Robustness Assessment\u003c/h2\u003e\n \u003cp\u003eTo evaluate the stability and reliability of the estimated network structure, we conducted a nonparametric bootstrap procedure (1,000 resamples) on the original network (118 edges). Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA displays the 15 strongest edges alongside their 95% bootstrapped confidence intervals (CIs); narrow CIs reflect greater estimation precision and replicability. As shown in Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, within-community edges demonstrated markedly higher stability (mean stability coefficient\u0026thinsp;=\u0026thinsp;0.915, SD\u0026thinsp;=\u0026thinsp;0.122) than bridge edges (mean\u0026thinsp;=\u0026thinsp;0.684, SD\u0026thinsp;=\u0026thinsp;0.172), with this difference reaching statistical significance (Welch\u0026rsquo;s t(113.9)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;8.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The overall network stability\u0026mdash;quantified by the correlation stability (CS) coefficient\u0026mdash;was 0.75, exceeding the recommended threshold of CS\u0026thinsp;\u0026gt;\u0026thinsp;0.5 for \u0026ldquo;moderate\u0026rdquo; robustness (Epskamp et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Further inspection revealed that 86.8% of within-community edges achieved stability\u0026thinsp;\u0026ge;\u0026thinsp;0.75, compared to only 36.9% of bridge edges\u0026mdash;highlighting the relative fragility of cross-community connections. Among the four negative edges, only PSC2\u0026ndash;SCA3 exhibited high stability (CS\u0026thinsp;=\u0026thinsp;0.952); the remaining three (PSC4\u0026ndash;SCA1, PSC5\u0026ndash;SCA2, PSC2\u0026ndash;SCA5) fell below the 0.5 threshold and were thus deemed insufficiently stable for confident interpretation. Finally, sensitivity analyses varying the EBICglasso tuning parameter (\u0026gamma;\u0026thinsp;=\u0026thinsp;0.25 and \u0026gamma;\u0026thinsp;=\u0026thinsp;0.75) confirmed structural consistency: the community partition, global topology, and top 10 strongest edges\u0026mdash;including all key intra- and inter-community associations reported in Sections \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e and \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e3.5\u003c/span\u003e\u0026mdash;remained invariant across specifications (see Supplementary Material Table S6). Collectively, these results provide strong empirical support for the reliability and generalizability of the network architecture.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIntegrating structural equation modeling and network psychometrics, this study elucidates a multilayered mechanism through which psychosocial competence shapes self-care skills among college students. Results robustly support a theoretically grounded chain mediation pathway\u0026mdash;\u0026ldquo;competence \u0026rarr; behavior \u0026rarr; cognition \u0026rarr; skill\u0026rdquo;\u0026mdash;in which psychosocial competence exerts both direct and indirect influences: (1) independent mediation via health promotion behavior and health information literacy, and (2) sequential mediation through the \u0026ldquo;health promotion behavior \u0026rarr; health information literacy\u0026rdquo; pathway. Complementing these macro-level causal inferences, network analysis identified SCA1 (\u0026ldquo;Interpret fitness test data\u0026rdquo;) and HIL4 (\u0026ldquo;Make decisions based on information\u0026rdquo;) as high-bridge-strength nodes\u0026mdash;functioning as critical micro-level connectors across latent communities. Together, these findings advance an integrated process model that bridges macro-theoretical frameworks with micro-structural dynamics.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Theoretical Framework of Multiple Mediation Paths\u003c/h2\u003e \u003cp\u003eThe influence of psychosocial capabilities on self-care skills operates through three distinct pathways, forming a theoretical framework characterized by cognitive dominance, behavioral supplementation, and chain-like mediation. The mediating effect of health information literacy accounts for the largest share, with its standardized coefficient approximately three times that of health promotion behavior (β\u0026thinsp;=\u0026thinsp;0.385 vs. 0.126). This difference in magnitude indicates the pivotal role of the cognitive processing stage in resource transformation (McAnally \u0026amp; Hagger, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the relative advantage of the effect size reflects only the static strength of association. The temporal relationship between behavior and cognition\u0026mdash;whether health promotion behavior constitutes a necessary prerequisite for the development of health information literacy\u0026mdash;involves a deep tension between social cognitive theory and the health literacy model (R\u0026uuml;egg, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The above findings engage in an interesting dialogue with previous research. Liu et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported a mediating effect of psychological capital (β\u0026thinsp;=\u0026thinsp;0.099), comparable in magnitude to the health promotion behavior path in this study. However, they did not distinguish the relative contributions of cognitive and behavioral channels (X. Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The effect of decomposition in this study reveals that when both health information literacy and behavioral variables are included, the dominance of the cognitive path becomes more pronounced. This difference may stem from differences in health outcome types: the development of emotional management skills may rely more on accumulating behavioral experience (Zhu et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), whereas the construction of self-care skills may rely more on information processing and situational judgment (Zeng et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The presence of a chain mediation path (β\u0026thinsp;=\u0026thinsp;0.083) suggests that the behavior-cognition sequence may have a distinct theoretical function rather than merely an additive effect. Although this path has a smaller effect size, its model fit advantage implies that the \"behavior \u0026rarr; cognition\" directionality may reveal the black box of the mechanism\u0026mdash;that is, how behavioral practice triggers cognitive needs and is then internalized as stable skills (Suri et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The theoretical implications of this directionality issue require clarification through the boundary condition that \"ability level moderates the necessity of the behavior-cognition sequence.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Directionality of the Behavior-Cognition Sequence and Its Theoretical Implications\u003c/h2\u003e \u003cp\u003eThe statistical significance of the chain path (β\u0026thinsp;=\u0026thinsp;0.083, p\u0026thinsp;=\u0026thinsp;0.002) supports the existence of the \"behavior \u0026rarr; cognition\" direction. However, its effect size is substantially smaller than the independent mediating effect of health information literacy, suggesting that the strength of this sequence is highly context-dependent (Murakami et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bourke et al.'s (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) longitudinal study further found that among adolescents transitioning to adulthood, behavioral changes preceding cognitive adaptation tend to predict a more stable health trajectory (Bourke et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Notably, this pattern was statistically significant only in the group with low psychosocial ability. These findings collectively point to a moderating mechanism: when psychosocial ability is high, individuals can directly activate cognitive strategies; when ability is low, they must rely on behavioral practice to elicit cognitive processes. Odunsi and Farris (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) further confirmed that cognitive variables have significantly higher predictive power for preventive behaviors than behavioral variables, supporting the efficiency advantage of the \"cognition-first\" strategy in resource-rich contexts (Odunsi \u0026amp; Farris, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Shoji et al.'s (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) cross-national study provided external support for the cross-contextual stability of this path, identifying health literacy as a stable driver of health behaviors (Shoji et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In summary, the chain path identified in this study does not merely represent a compromise between the two theoretical perspectives; rather, it reveals the boundary conditions under which \"ability level moderates the necessity of the behavior-cognition sequence.\" Based on the directionality observed, a dynamic reciprocal relationship may exist between health literacy and behavior (Osborne et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the cross-sectional design precludes confirmation of the temporal sequence within this cycle, and future studies should employ longitudinal designs to test this possibility (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Cross-disciplinary Integration Function of Bridge Nodes in the Network\u003c/h2\u003e \u003cp\u003eThe structural equation model revealed the dominant position of the cognitive hub at the latent variable level, while network analysis further identified key connection points at the observed indicator level. The bridge strength between SCA1 (\"Interpret fitness test data\") and HIL4 (\"Make decisions based on information\") significantly exceeded that of other nodes, establishing them as core hubs connecting the four theoretical communities. The high bridge strength of SCA1 indicates that the ability to interpret health data operates at the interface of information internalization, psychological resource mobilization, and practical application. This finding aligns with research showing that eHealth literacy influences health behavior through multiple pathways (Kim \u0026amp; Oh, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); however, network analysis more precisely identifies \"data interpretation\"\u0026mdash;rather than general \"health information literacy\"\u0026mdash;as an intervention target. The high bridge strength of HIL4 echoes Tian et al.'s (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) emphasis on critical health literacy, suggesting that the ability to evaluate information and make decisions serves as a core hub connecting cognition and action, beyond mere information acquisition skills (Tian et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Network analysis also revealed micro-level associations not apparent at the latent variable level. Although psychosocial competence and self-care skills were generally strongly positively correlated, negative edges emerged at the network level, with the most stable negative correlation observed between PSC2 (\"Value life and health\") and SCA3 (\"Handle common injuries/emergencies\"). This counterintuitive finding may reflect a cognitive conflict between values and behavior: individuals who deeply reflect on the meaning of life may assess their first-aid abilities more cautiously. This interpretation remains post-hoc speculation and warrants verification through qualitative methods (L. Wang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The finding engages with research on psychological first aid training, which suggests that such training not only enhances objective skills but also reshapes learners' cognitive reference systems regarding their own abilities (Mtiraoui et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; \u0026Ouml;zt\u0026uuml;rk et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations and Future Research Directions\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the cross-sectional design precludes causal inference. Although the chain mediation model is theoretically grounded, it cannot rule out reverse causality; future research should employ longitudinal designs to test the directionality of the paths. Second, the sample representativeness is limited. As participants were restricted to freshmen and sophomores from a single comprehensive university, caution is warranted when generalizing the findings to other populations. Third, the study relied on a single measurement method\u0026mdash;self-report\u0026mdash;for all variables. The negative correlation between PSC2 and SCA3 may therefore reflect self-assessment bias among high-ability individuals rather than a genuine ability-behavior dissociation. Future studies should incorporate objective indicators to validate these findings. Fourth, the network analysis was exploratory in nature. The cross-sectional network cannot capture the dynamic evolution of relationships among variables, and future research should test the replicability of the observed network structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Practical Implications\u003c/h2\u003e \u003cp\u003eBased on these findings, this study proposes a stratified intervention strategy. The first layer is general education, which integrates health information literacy into core curricula, with a focus on cultivating the information evaluation and decision-making ability represented by HIL4\u0026mdash;the weakest skill among college students in an information-overloaded environment (Nakayama et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The second layer involves targeted training. This includes developing health data visualization tools for SCA1 to translate abstract indicators into personalized action suggestions and designing contradictory information decision-making simulations for HIL4 to train evidence evaluation skills on controversial topics. Assessment should employ node-specific outcome indicators: accuracy of data interpretation for SCA1 and quality of decision-making reasoning for HIL4, rather than a general health literacy score (Saldert et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The third layer focuses on capacity building, enhancing psychosocial capabilities through modules on psychological resilience and emotion regulation, while adopting a parallel strategy to simultaneously activate cognitive and behavioral goals.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study, through cross-method validation using structural equation modeling and network analysis, has revealed multiple pathways through which psychosocial abilities influence self-care skills. Health information literacy is the core mediating variable, with the cognitive path being more efficient than the behavioral path. The chain path has verified the situational-driven effect of behavior on cognition, but the pattern of effect sizes suggests that a \"cognition-first\" strategy may be more cost-effective. SCA1 and HIL4, as bridge nodes, provide operational targets for precise intervention. The above findings offer a stratified design basis for health intervention among college students in the digital age and simultaneously expand the dialogue boundary between social cognitive theory and the integrated model of health literacy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key Funding Project of the Jiangsu Province Education Science \"14th Five-Year Plan\" [Grant Number B/2023/02/90], titled \"Assessment and Promotion Pathways of Health Literacy among University Students in the Context of 'Healthy China'.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChen Jianfeng¹, Pan Yikang¹, Yin Huaigang²\u003c/p\u003e\n\u003cp\u003e¹\u0026nbsp;Department of Physical Education, Changzhou Vocational Institute of Textile and Garment, Changzhou 213164, China;\u003c/p\u003e\n\u003cp\u003e² Nanjing Sport Institute, Nanjing, Jiangsu 210014, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChen Jianfeng was responsible for study design, data analysis, manuscript writing, and final approval. Pan Yikang participated in data collection, literature collation, and initial draft writing. Yin Huaigang participated in study design, result interpretation, and manuscript revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to: Chen Jianfeng (Department of Physical Education, Changzhou Vocational Institute of Textile and Garment, Changzhou 213164, China; [email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. According to Article 32 of the Chinese \"Ethical Review Measures for Life Science and Medical Research Involving Humans\" (National Health Commission of the PRC et al., 2023), this study qualifies for exemption from formal ethical review because it involves only anonymous questionnaire procedures with no collection of personally identifiable information. The study protocol was reviewed internally, and permission to conduct the research was granted by the Department of Physical Education at Changzhou Vocational Institute of Textile and Garment. All participants were informed of the study purpose, the anonymous nature of their participation, and their right to withdraw at any time before the survey. Completion and submission of the questionnaire were considered implied informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during this study are not publicly available but can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlosaimi, N., Sherar, L. B., Griffiths, P., \u0026amp; Pearson, N. (2023). Clustering of diet, physical activity and sedentary behaviour and related physical and mental health outcomes: A systematic review. \u003cem\u003eBMC Public Health\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 1572. https://doi.org/10.1186/s12889-023-16372-6\u003c/li\u003e\n \u003cli\u003eAmerican Psychological Association. (2020). \u003cem\u003ePublication manual of the American Psychological Association (7th ed.).\u003c/em\u003e American Psychological Association. https://doi.org/10.1037/0000165-000\u003c/li\u003e\n \u003cli\u003eArnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. \u003cem\u003eAmerican Psychologist\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(5), 469\u0026ndash;480. https://doi.org/10.1037/0003-066X.55.5.469\u003c/li\u003e\n \u003cli\u003eBandura, A. (1986). \u003cem\u003eSocial foundations of thought and action: A social cognitive theory\u003c/em\u003e. Prentice-hall.\u003c/li\u003e\n \u003cli\u003eBandura, A. (1997). \u003cem\u003eSelf-efficacy: The exercise of control\u003c/em\u003e. Macmillan.\u003c/li\u003e\n \u003cli\u003eBao, X., Chen, D., Shi, L., Xia, Y., Shi, Z., \u0026amp; Wang, D. (2022). The relationship between COVID-19-related prevention cognition and healthy lifestyle behaviors among university students: Mediated by e-health literacy and self-efficacy. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e309\u003c/em\u003e, 236\u0026ndash;241. https://doi.org/10.1016/j.jad.2022.04.044\u003c/li\u003e\n \u003cli\u003eBorsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., Van Borkulo, C. D., Van Bork, R., \u0026amp; Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. \u003cem\u003eNature Reviews Methods Primers\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 58. https://doi.org/10.1038/s43586-021-00055-w\u003c/li\u003e\n \u003cli\u003eBourke, M., Brown, D., \u0026amp; Kwan, M. Y. W. (2025). Lifestyle Behavior Patterns During the Transition From Adolescence to Emerging Adulthood: Associations With Mental Health and Wellbeing. \u003cem\u003eEmerging Adulthood\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(6), 1381\u0026ndash;1394. https://doi.org/10.1177/21676968251376750\u003c/li\u003e\n \u003cli\u003eBurnham, K. P., \u0026amp; Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC in Model Selection. \u003cem\u003eSociological Methods \u0026amp; Research\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(2), 261\u0026ndash;304. https://doi.org/10.1177/0049124104268644\u003c/li\u003e\n \u003cli\u003eCarpenter, S. (2018). Ten Steps in Scale Development and Reporting: A Guide for Researchers. \u003cem\u003eCommunication Methods and Measures\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 25\u0026ndash;44. https://doi.org/10.1080/19312458.2017.1396583\u003c/li\u003e\n \u003cli\u003eChao, D.-P. (2023). Health-promoting lifestyle and its predictors among health-related and non-health-related university students in Taiwan: A cross-sectional quantitative study. \u003cem\u003eBMC Public Health\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 827. https://doi.org/10.1186/s12889-023-15760-2\u003c/li\u003e\n \u003cli\u003eChen, X., \u0026amp; Xiao, H. (2025). E-health literacy and health-promoting behaviors among nursing students in China: The mediating role of self-efficacy. \u003cem\u003ePsychology, Health \u0026amp; Medicine\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(8), 1757\u0026ndash;1767. https://doi.org/10.1080/13548506.2025.2481193\u003c/li\u003e\n \u003cli\u003eCheung, G. W., Cooper-Thomas, H. D., Lau, R. S., \u0026amp; Wang, L. C. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. \u003cem\u003eAsia Pacific Journal of Management\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(2), 745\u0026ndash;783. https://doi.org/10.1007/s10490-023-09871-y\u003c/li\u003e\n \u003cli\u003eCohen, J. (2013). \u003cem\u003eStatistical power analysis for the behavioral sciences\u003c/em\u003e. routledge.\u003c/li\u003e\n \u003cli\u003eDeVellis, R. F., \u0026amp; Thorpe, C. T. (2021). \u003cem\u003eScale development: Theory and applications\u003c/em\u003e. Sage publications.\u003c/li\u003e\n \u003cli\u003eFleary, S. A., Joseph, P., \u0026amp; Pappagianopoulos, J. E. (2018). Adolescent health literacy and health behaviors: A systematic review. \u003cem\u003eJournal of Adolescence\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(1), 116\u0026ndash;127. https://doi.org/10.1016/j.adolescence.2017.11.010\u003c/li\u003e\n \u003cli\u003eFornell, C., \u0026amp; Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. \u003cem\u003eJournal of Marketing Research,\u0026nbsp;\u003c/em\u003e\u003cem\u003e无\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 39\u0026ndash;50.\u003c/li\u003e\n \u003cli\u003eGoretzko, D., Pham, T. T. H., \u0026amp; B\u0026uuml;hner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. \u003cem\u003eCurrent Psychology\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(7), 3510\u0026ndash;3521. https://doi.org/10.1007/s12144-019-00300-2\u003c/li\u003e\n \u003cli\u003eGuo, S., Fu, H., \u0026amp; Guo, K. (2024). Effects of physical activity on subjective well-being: The mediating role of social support and self-efficacy. \u003cem\u003eFrontiers in Sports and Active Living\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 1362816. https://doi.org/10.3389/fspor.2024.1362816\u003c/li\u003e\n \u003cli\u003eHabiba, U., \u0026amp; Koli, F. S. (2024).\u0026nbsp;The Mediating Role of Students\u0026rsquo; Health Information Literacy Skills: Exploring the Relationship Between Web Resource Utilization and Health Information Evaluation Proficiency. \u003cem\u003eHealth Expectations\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), e14176. https://doi.org/10.1111/hex.14176\u003c/li\u003e\n \u003cli\u003eHair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., \u0026amp; Tatham, R. L. (2010). \u003cem\u003eMultivariate data analysis, 7th edn. Hoboken\u003c/em\u003e. NJ: Prentice hall.\u003c/li\u003e\n \u003cli\u003eHayes, A. F. (2017). \u003cem\u003eIntroduction to mediation, moderation, and conditional process analysis: A regression-based approach\u003c/em\u003e. Guilford publications.\u003c/li\u003e\n \u003cli\u003eJones, P. J., Ma, R., \u0026amp; McNally, R. J. (2021). Bridge Centrality: A Network Approach to Understanding Comorbidity. \u003cem\u003eMultivariate Behavioral Research\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(2), 353\u0026ndash;367. https://doi.org/10.1080/00273171.2019.1614898\u003c/li\u003e\n \u003cli\u003eKamath, A., Poojari, S., \u0026amp; Varsha, K. (2025). Assessing the robustness of normality tests under varying skewness and kurtosis: A practical checklist for public health researchers. \u003cem\u003eBMC Medical Research Methodology\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 206. https://doi.org/10.1186/s12874-025-02641-y\u003c/li\u003e\n \u003cli\u003eKim, S., \u0026amp; Oh, J. (2021). The Relationship between E-Health Literacy and Health-Promoting Behaviors in Nursing Students: A Multiple Mediation Model. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(11), 5804. https://doi.org/10.3390/ijerph18115804\u003c/li\u003e\n \u003cli\u003eKline, R. B. (2023). \u003cem\u003ePrinciples and practice of structural equation modeling\u003c/em\u003e. Guilford publications.\u003c/li\u003e\n \u003cli\u003eLi, S., Cui, G., Zhou, F., Liu, S., Guo, Y., Yin, Y., \u0026amp; Xu, H. (2022). The longitudinal relationship between eHealth literacy, health-promoting lifestyles, and health-related quality of life among college students: A cross-lagged analysis. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 868279.\u003c/li\u003e\n \u003cli\u003eLiu, C., Chen, Z., Qin, H., Yang, Y., Cui, X., Lei, R., \u0026amp; Li, B. (2025). Health Knowledge Acquisition and Health Communication Ability of Nurses: A Chain Mediating Model of Health Literacy and Health Education Competence. \u003cem\u003eJournal of Advanced Nursing\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e(9), 5903\u0026ndash;5913. https://doi.org/10.1111/jan.16728\u003c/li\u003e\n \u003cli\u003eLiu, X., Wang, Z., Zhang, C., Xu, J., Shen, Z., Peng, L., Mi, Y., \u0026amp; Xu, H. (2024). Psychological Capital and Its Factors as Mediators Between Interpersonal Sensitivity and Depressive Symptoms Among Chinese Undergraduates. \u003cem\u003ePsychology Research and Behavior Management\u003c/em\u003e, \u003cem\u003eVolume 17\u003c/em\u003e, 429\u0026ndash;441. https://doi.org/10.2147/PRBM.S452993\u003c/li\u003e\n \u003cli\u003eLv, C., Wang, Z., Cui, H., Zhang, K., Wang, X., Wang, X., Alsudais, T. A., \u0026amp; Duan, S. (2025). Investigating the influence of adolescents\u0026rsquo; social and emotional skills on health behavior: A moderated mediation analysis. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 1712176. https://doi.org/10.3389/fpsyg.2025.1712176\u003c/li\u003e\n \u003cli\u003eMcAnally, K., \u0026amp; Hagger, M. S. (2023). Health literacy, social cognition constructs, and health behaviors and outcomes: A meta-analysis. \u003cem\u003eHealth Psychology\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(4), 213\u0026ndash;234. https://doi.org/10.1037/hea0001266\u003c/li\u003e\n \u003cli\u003eMcNeish, D. (2025). Dynamic measurement invariance cutoffs for two-group fit index differences. \u003cem\u003ePsychological Methods\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eMcNeish, D., \u0026amp; Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. \u003cem\u003ePsychological Methods\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 61\u0026ndash;88. https://doi.org/10.1037/met0000425\u003c/li\u003e\n \u003cli\u003eMtiraoui, A., Mahjoubi, H., Achour, A., Ghardallou, M., \u0026amp; Nakhli, J. (2025). The Impact of Psychological First Aid Training (RAPID‐PFA) on Self‐Efficacy, Perceived Competencies and Disaster Preparedness of Nursing Students in Tunisian Public Institutions: A Randomized Controlled Trial. \u003cem\u003eJournal of Contingencies and Crisis Management\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), e70019. https://doi.org/10.1111/1468-5973.70019\u003c/li\u003e\n \u003cli\u003eMurakami, K., Kuriyama, S., \u0026amp; Hashimoto, H. (2023). Economic, cognitive, and social paths of education to health-related behaviors: Evidence from a population-based study in Japan. \u003cem\u003eEnvironmental Health and Preventive Medicine\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e, 9\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eMuth\u0026eacute;n, B., \u0026amp; Muth\u0026eacute;n, L. (2017). Mplus. In \u003cem\u003eHandbook of item response theory\u003c/em\u003e (pp. 507\u0026ndash;518). Chapman and Hall/CRC.\u003c/li\u003e\n \u003cli\u003eNakayama, K., Yonekura, Y., Danya, H., \u0026amp; Hagiwara, K. (2022). Associations between health literacy and information-evaluation and decision-making skills in Japanese adults. \u003cem\u003eBMC Public Health\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 1473. https://doi.org/10.1186/s12889-022-13892-5\u003c/li\u003e\n \u003cli\u003eNational Health Commission of the PRC, Ministry of Education of the PRC, Ministry of Science and Technology of the PRC, \u0026amp; National Administration of Traditional Chinese Medicine of the PRC. (2023). \u003cem\u003eEthical Review Measures for Life Science and Medical Research Involving Humans (No. 4 document of the National Health Commission)\u003c/em\u003e. http://big5.www.gov.cn/gate/big5/www.gov.cn/zhengce/zhengceku/2023-02/28/content_5743658.htm (in Chinese)\u003c/li\u003e\n \u003cli\u003eOdunsi, I. A., \u0026amp; Farris, K. L. (2025). Predicting College Students\u0026rsquo; Preventative Behavior During a Pandemic: The Role of the Health Belief Model, Source Credibility, and Health Literacy. \u003cem\u003eAmerican Behavioral Scientist\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e(12), 1516\u0026ndash;1533. https://doi.org/10.1177/00027642231164044\u003c/li\u003e\n \u003cli\u003eOrem, D. E., Taylor, S. G., \u0026amp; Renpenning, K. M. (1995). \u003cem\u003eNursing: Concepts of practice\u003c/em\u003e (5th ed). Mosby.\u003c/li\u003e\n \u003cli\u003eOsborne, R. H., Elmer, S., Hawkins, M., Cheng, C. C., Batterham, R. W., Dias, S., Good, S., Monteiro, M. G., Mikkelsen, B., Nadarajah, R. G., \u0026amp; others. (2022). Health literacy development is central to the prevention and control of non-communicable diseases. \u003cem\u003eBMJ Global Health\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(12).\u003c/li\u003e\n \u003cli\u003e\u0026Ouml;zt\u0026uuml;rk, M. H., Yeşilyaprak, T., \u0026amp; Kuday, A. D. (2025). Enhancing students\u0026rsquo; knowledge and self-efficacy through integrated first aid and psychological first aid training. \u003cem\u003ePsychology, Health \u0026amp; Medicine\u003c/em\u003e, 1\u0026ndash;14. https://doi.org/10.1080/13548506.2025.2506017\u003c/li\u003e\n \u003cli\u003ePodsakoff, P. M., Podsakoff, N. P., Williams, L. J., Huang, C., \u0026amp; Yang, J. (2024). Common Method Bias: It\u0026rsquo;s Bad, It\u0026rsquo;s Complex, It\u0026rsquo;s Widespread, and It\u0026rsquo;s Not Easy to Fix. \u003cem\u003eAnnual Review of Organizational Psychology and Organizational Behavior\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 17\u0026ndash;61. https://doi.org/10.1146/annurev-orgpsych-110721-040030\u003c/li\u003e\n \u003cli\u003eRoemer, E., Schuberth, F., \u0026amp; Henseler, J. (2021). HTMT2\u0026ndash;an improved criterion for assessing discriminant validity in structural equation modeling. \u003cem\u003eIndustrial Management \u0026amp; Data Systems\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(12), 2637\u0026ndash;2650.\u003c/li\u003e\n \u003cli\u003eRos\u0026aacute;rio, J., Pires, J. C., Dias, S., \u0026amp; Pedro, A. R. (2025).\u0026nbsp;Exploring perceptions of health literacy, healthcare access, and utilisation among higher education students in Alentejo, Southern Portugal: A qualitative study. \u003cem\u003ePLOS One\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(6), e0326575. https://doi.org/10.1371/journal.pone.0326575\u003c/li\u003e\n \u003cli\u003eR\u0026uuml;egg, R. (2022).\u0026nbsp;Decision-making ability: A missing link between health literacy, contextual factors, and health. \u003cem\u003eHLRP: Health Literacy Research and Practice\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), e213\u0026ndash;e223.\u003c/li\u003e\n \u003cli\u003eSaldert, C., Jensen, L. R., Blom Johansson, M., \u0026amp; Simmons-Mackie, N. (2018). Complexity in measuring outcomes after communication partner training: Alignment between goals of intervention and methods of evaluation. \u003cem\u003eAphasiology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(10), 1167\u0026ndash;1193. https://doi.org/10.1080/02687038.2018.1470317\u003c/li\u003e\n \u003cli\u003eSchwarzer, R., \u0026amp; Luszczynska, A. (2008). How to Overcome Health-Compromising Behaviors: The Health Action Process Approach. \u003cem\u003eEuropean Psychologist\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), 141\u0026ndash;151. https://doi.org/10.1027/1016-9040.13.2.141\u003c/li\u003e\n \u003cli\u003eShoji, Y., Irwan, A. M., Ochiai, R., Syahrul, S., Shinohara, E., Fiqri, A. M., Takeuchi, S., Erfina, E., Iida, M., Saleh, A., Moriguchi, F., Nakamura, S., \u0026amp; Kanoya, Y. (2025). The Impact of eHealth Literacy on Health Behaviors for Non-communicable Disease Prevention Among University Students in Japan and Indonesia. \u003cem\u003eCureus\u003c/em\u003e. https://doi.org/10.7759/cureus.78450\u003c/li\u003e\n \u003cli\u003eS\u0026oslash;rensen, K., Van Den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., Slonska, Z., Brand, H., \u0026amp; (HLS-EU) Consortium Health Literacy Project European. (2012). Health literacy and public health: A systematic review and integration of definitions and models. \u003cem\u003eBMC Public Health,\u0026nbsp;\u003c/em\u003e\u003cem\u003e无\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 80. Q2 (医学3区). https://doi.org/10.1186/1471-2458-12-80\u003c/li\u003e\n \u003cli\u003eStormacq, C., Oulevey Bachmann, A., Van Den Broucke, S., \u0026amp; Bodenmann, P. (2023). How socioeconomically disadvantaged people access, understand, appraise, and apply health information: A qualitative study exploring health literacy skills. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(8), e0288381. https://doi.org/10.1371/journal.pone.0288381\u003c/li\u003e\n \u003cli\u003eSuri, V. R., Majid, S., Foo, S., Dumaual-Sibal, H. T., \u0026amp; Chang, Y.-K. (2019). Understanding Health Literacy Through the Lens of Phronesis: The Case of Coronary Artery Disease Patients. In S. Kurbanoğlu, S. \u0026Scaron;piranec, Y. \u0026Uuml;nal, J. Boustany, M. L. Huotari, E. Grassian, D. Mizrachi, \u0026amp; L. Roy (Eds.), \u003cem\u003eInformation Literacy in Everyday Life\u003c/em\u003e (Vol. 989, pp. 166\u0026ndash;175). Springer International Publishing. https://doi.org/10.1007/978-3-030-13472-3_16\u003c/li\u003e\n \u003cli\u003eTang, Y., Lin, C. T., \u0026amp; Wu, L. (2025). Understanding Health Literacy Through Patients\u0026rsquo; Interpretation of Health Education Leaflets: A Thematic Narrative Review. \u003cem\u003eHealth Expectations\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(6), e70479. https://doi.org/10.1111/hex.70479\u003c/li\u003e\n \u003cli\u003eTaylor, S. E., Way, B. M., \u0026amp; Seeman, T. E. (2011). Early adversity and adult health outcomes. \u003cem\u003eDevelopment and Psychopathology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(3), 939\u0026ndash;954. https://doi.org/10.1017/S0954579411000411\u003c/li\u003e\n \u003cli\u003eTeam, R. C. (2020). RA language and environment for statistical computing, R Foundation for Statistical. \u003cem\u003eComputing. Archives for Scientific Computing\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eTempone-Wiltshire, J., \u0026amp; Matthews, F. (2025). Embodied Minds: An Embodied Cognitivist Understanding of Mindfulness in Public Health. \u003cem\u003eMindfulness\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(3), 725\u0026ndash;737. https://doi.org/10.1007/s12671-024-02423-5\u003c/li\u003e\n \u003cli\u003eTian, C. Y., Ng, C. C. W., Xie, L., Mo, P. K., Dong, D., Nutbeam, D., \u0026amp; Wong, E. L. (2025). Conceptualisation of critical health literacy\u0026mdash;Insights from Western and East Asian perspectives: A scoping review. \u003cem\u003eBMJ Global Health\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(5).\u003c/li\u003e\n \u003cli\u003eWang, L., Norman, I., Xiao, T., Li, Y., Li, X., Liu, T., Wang, J., Zeng, L., Zhong, Z., Jian, C., \u0026amp; others. (2024). Feasibility and acceptability of a culturally adapted psychological first aid training intervention (Preparing Me) to support the mental health and well-being of front-line healthcare workers in China: A feasibility randomized controlled trial. \u003cem\u003eEuropean Journal of Psychotraumatology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 2299195.\u003c/li\u003e\n \u003cli\u003eWang, S., Wei, J., Zhang, P., Song, J., Chen, J., \u0026amp; Li, G. (2025). The chain mediating effect of self-efficacy and health literacy between proactive personality and health-promoting behaviors among Chinese college students. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 16101. https://doi.org/10.1038/s41598-025-00936-0\u003c/li\u003e\n \u003cli\u003eWatkins, M. W. (2021). \u003cem\u003eA Step-by-Step Guide to Exploratory Factor Analysis with SPSS\u003c/em\u003e (1st ed.). Routledge. https://doi.org/10.4324/9781003149347\u003c/li\u003e\n \u003cli\u003eWolf, E. J., Harrington, K. M., Clark, S. L., \u0026amp; Miller, M. W. (2013). Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety. \u003cem\u003eEducational and Psychological Measurement\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(6), 913\u0026ndash;934. https://doi.org/10.1177/0013164413495237\u003c/li\u003e\n \u003cli\u003eWorld Medical Association. (2001). World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. \u003cem\u003eBulletin of the World Health Organization\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e(4), 373\u0026ndash;374.\u003c/li\u003e\n \u003cli\u003eXia, Y., \u0026amp; Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. \u003cem\u003eBehavior Research Methods\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(1), 409\u0026ndash;428. https://doi.org/10.3758/s13428-018-1055-2\u003c/li\u003e\n \u003cli\u003eYusoff, M. S. B. (2019). ABC of content validation and content validity index calculation. \u003cem\u003eEducation in Medicine Journal\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 49\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003eZeng, M., Liu, Y., He, Y., \u0026amp; Huang, W. (2025). Correction: Relationship between stroke knowledge, health information literacy, and health self-management among patients with stroke: Multicenter cross-sectional study. \u003cem\u003eJMIR Medical Informatics\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, e80547.\u003c/li\u003e\n \u003cli\u003eZhang, M. J., Guo, X., Wang, R. S., Mao, X., Xiang, G., Li, W., Zuo, C., Zhou, H., \u0026amp; Xu, D. R. (2025). Health literacy model integrating health education, health behaviors, self-rated health, and socioeconomic status in the Chinese population. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 32320. https://doi.org/10.1038/s41598-025-07094-3\u003c/li\u003e\n \u003cli\u003eZhou, Y., Xu, J., Wang, R., \u0026amp; Guan, X. (2025). Understanding how digital health literacy affects health self-management behaviors: The mediating role of self-efficacy in college students. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 27230. https://doi.org/10.1038/s41598-025-12726-9\u003c/li\u003e\n \u003cli\u003eZhu, W., Liu, J., Lou, H., Mu, F., \u0026amp; Li, B. (2024). The impact of electronic health literacy on emotional management ability among college students: The mediating roles of peer relationships and exercise self-efficacy. \u003cem\u003eBMC Psychology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 747. https://doi.org/10.1186/s40359-024-02276-6\u003c/li\u003e\n \u003cli\u003eAlosaimi, N., Sherar, L. B., Griffiths, P., \u0026amp; Pearson, N. (2023). Clustering of diet, physical activity and sedentary behaviour and related physical and mental health outcomes: A systematic review. \u003cem\u003eBMC Public Health\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 1572. https://doi.org/10.1186/s12889-023-16372-6\u003c/li\u003e\n \u003cli\u003eArnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. \u003cem\u003eAmerican Psychologist\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(5), 469\u0026ndash;480. https://doi.org/10.1037/0003-066X.55.5.469\u003c/li\u003e\n \u003cli\u003eAssociation, A. P. \u0026amp; others. (2020). Publication manual of the American psychological association 2020. \u003cem\u003eAmerican Psychological Association\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eBandura, A. (1986). \u003cem\u003eSocial foundations of thought and action: A social cognitive theory\u003c/em\u003e. Prentice-hall.\u003c/li\u003e\n \u003cli\u003eBandura, A. (1997). \u003cem\u003eSelf-efficacy: The exercise of control\u003c/em\u003e. Macmillan.\u003c/li\u003e\n \u003cli\u003eBao, X., Chen, D., Shi, L., Xia, Y., Shi, Z., \u0026amp; Wang, D. (2022). The relationship between COVID-19-related prevention cognition and healthy lifestyle behaviors among university students: Mediated by e-health literacy and self-efficacy. \u003cem\u003eJournal of Affective Disorders\u003c/em\u003e, \u003cem\u003e309\u003c/em\u003e, 236\u0026ndash;241. https://doi.org/10.1016/j.jad.2022.04.044\u003c/li\u003e\n \u003cli\u003eBorsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., Van Borkulo, C. D., Van Bork, R., \u0026amp; Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. \u003cem\u003eNature Reviews Methods Primers\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 58. https://doi.org/10.1038/s43586-021-00055-w\u003c/li\u003e\n \u003cli\u003eBourke, M., Brown, D., \u0026amp; Kwan, M. Y. W. (2025). Lifestyle Behavior Patterns During the Transition From Adolescence to Emerging Adulthood: Associations With Mental Health and Wellbeing. \u003cem\u003eEmerging Adulthood\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(6), 1381\u0026ndash;1394. https://doi.org/10.1177/21676968251376750\u003c/li\u003e\n \u003cli\u003eBurnham, K. P., \u0026amp; Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC in Model Selection. \u003cem\u003eSociological Methods \u0026amp; Research\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(2), 261\u0026ndash;304. https://doi.org/10.1177/0049124104268644\u003c/li\u003e\n \u003cli\u003eCarpenter, S. (2018). Ten Steps in Scale Development and Reporting: A Guide for Researchers. \u003cem\u003eCommunication Methods and Measures\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 25\u0026ndash;44. https://doi.org/10.1080/19312458.2017.1396583\u003c/li\u003e\n \u003cli\u003eChao, D.-P. (2023). Health-promoting lifestyle and its predictors among health-related and non-health-related university students in Taiwan: A cross-sectional quantitative study. \u003cem\u003eBMC Public Health\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 827. https://doi.org/10.1186/s12889-023-15760-2\u003c/li\u003e\n \u003cli\u003eChen, X., \u0026amp; Xiao, H. (2025). E-health literacy and health-promoting behaviors among nursing students in China: The mediating role of self-efficacy. \u003cem\u003ePsychology, Health \u0026amp; Medicine\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(8), 1757\u0026ndash;1767. https://doi.org/10.1080/13548506.2025.2481193\u003c/li\u003e\n \u003cli\u003eCohen, J. (2013). \u003cem\u003eStatistical power analysis for the behavioral sciences\u003c/em\u003e. routledge.\u003c/li\u003e\n \u003cli\u003eDeVellis, R. F., \u0026amp; Thorpe, C. T. (2021). \u003cem\u003eScale development: Theory and applications\u003c/em\u003e. Sage publications.\u003c/li\u003e\n \u003cli\u003eEpskamp, S., Borsboom, D., \u0026amp; Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. \u003cem\u003eBehavior Research Methods\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(1), 195\u0026ndash;212. https://doi.org/10.3758/s13428-017-0862-1\u003c/li\u003e\n \u003cli\u003eFleary, S. A., Joseph, P., \u0026amp; Pappagianopoulos, J. E. (2018). Adolescent health literacy and health behaviors: A systematic review. \u003cem\u003eJournal of Adolescence\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(1), 116\u0026ndash;127. https://doi.org/10.1016/j.adolescence.2017.11.010\u003c/li\u003e\n \u003cli\u003eFornell, C., \u0026amp; Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. \u003cem\u003eJournal of Marketing Research,\u0026nbsp;\u003c/em\u003e\u003cem\u003e无\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 39\u0026ndash;50.\u003c/li\u003e\n \u003cli\u003eGoretzko, D., Pham, T. T. H., \u0026amp; B\u0026uuml;hner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. \u003cem\u003eCurrent Psychology\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(7), 3510\u0026ndash;3521. https://doi.org/10.1007/s12144-019-00300-2\u003c/li\u003e\n \u003cli\u003eGuo, S., Fu, H., \u0026amp; Guo, K. (2024). Effects of physical activity on subjective well-being: The mediating role of social support and self-efficacy. \u003cem\u003eFrontiers in Sports and Active Living\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 1362816. https://doi.org/10.3389/fspor.2024.1362816\u003c/li\u003e\n \u003cli\u003eHabiba, U., \u0026amp; Koli, F. S. (2024).\u0026nbsp;The Mediating Role of Students\u0026rsquo; Health Information Literacy Skills: Exploring the Relationship Between Web Resource Utilization and Health Information Evaluation Proficiency. \u003cem\u003eHealth Expectations\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), e14176. https://doi.org/10.1111/hex.14176\u003c/li\u003e\n \u003cli\u003eHair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., \u0026amp; Tatham, R. L. (2010). \u003cem\u003eMultivariate data analysis, 7th edn. Hoboken\u003c/em\u003e. NJ: Prentice hall.\u003c/li\u003e\n \u003cli\u003eHayes, A. F. (2017). \u003cem\u003eIntroduction to mediation, moderation, and conditional process analysis: A regression-based approach\u003c/em\u003e. Guilford publications.\u003c/li\u003e\n \u003cli\u003eJones, P. J., Ma, R., \u0026amp; McNally, R. J. (2021). Bridge Centrality: A Network Approach to Understanding Comorbidity. \u003cem\u003eMultivariate Behavioral Research\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(2), 353\u0026ndash;367. https://doi.org/10.1080/00273171.2019.1614898\u003c/li\u003e\n \u003cli\u003eKamath, A., Poojari, S., \u0026amp; Varsha, K. (2025). Assessing the robustness of normality tests under varying skewness and kurtosis: A practical checklist for public health researchers. \u003cem\u003eBMC Medical Research Methodology\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 206. https://doi.org/10.1186/s12874-025-02641-y\u003c/li\u003e\n \u003cli\u003eKim, S., \u0026amp; Oh, J. (2021). The Relationship between E-Health Literacy and Health-Promoting Behaviors in Nursing Students: A Multiple Mediation Model. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(11), 5804. https://doi.org/10.3390/ijerph18115804\u003c/li\u003e\n \u003cli\u003eKline, R. B. (2023). \u003cem\u003ePrinciples and practice of structural equation modeling\u003c/em\u003e. Guilford publications.\u003c/li\u003e\n \u003cli\u003eLi, S., Cui, G., Zhou, F., Liu, S., Guo, Y., Yin, Y., \u0026amp; Xu, H. (2022). The longitudinal relationship between eHealth literacy, health-promoting lifestyles, and health-related quality of life among college students: A cross-lagged analysis. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 868279.\u003c/li\u003e\n \u003cli\u003eLiu, C., Chen, Z., Qin, H., Yang, Y., Cui, X., Lei, R., \u0026amp; Li, B. (2025). Health Knowledge Acquisition and Health Communication Ability of Nurses: A Chain Mediating Model of Health Literacy and Health Education Competence. \u003cem\u003eJournal of Advanced Nursing\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e(9), 5903\u0026ndash;5913. https://doi.org/10.1111/jan.16728\u003c/li\u003e\n \u003cli\u003eLiu, X., Wang, Z., Zhang, C., Xu, J., Shen, Z., Peng, L., Mi, Y., \u0026amp; Xu, H. (2024). Psychological Capital and Its Factors as Mediators Between Interpersonal Sensitivity and Depressive Symptoms Among Chinese Undergraduates. \u003cem\u003ePsychology Research and Behavior Management\u003c/em\u003e, \u003cem\u003eVolume 17\u003c/em\u003e, 429\u0026ndash;441. https://doi.org/10.2147/PRBM.S452993\u003c/li\u003e\n \u003cli\u003eLv, C., Wang, Z., Cui, H., Zhang, K., Wang, X., Wang, X., Alsudais, T. A., \u0026amp; Duan, S. (2025). Investigating the influence of adolescents\u0026rsquo; social and emotional skills on health behavior: A moderated mediation analysis. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 1712176. https://doi.org/10.3389/fpsyg.2025.1712176\u003c/li\u003e\n \u003cli\u003eMcAnally, K., \u0026amp; Hagger, M. S. (2023). Health literacy, social cognition constructs, and health behaviors and outcomes: A meta-analysis. \u003cem\u003eHealth Psychology\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(4), 213.\u003c/li\u003e\n \u003cli\u003eMcNeish, D. (2025). Dynamic measurement invariance cutoffs for two-group fit index differences. \u003cem\u003ePsychological Methods\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eMcNeish, D., \u0026amp; Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. \u003cem\u003ePsychological Methods\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 61\u0026ndash;88. https://doi.org/10.1037/met0000425\u003c/li\u003e\n \u003cli\u003eMtiraoui, A., Mahjoubi, H., Achour, A., Ghardallou, M., \u0026amp; Nakhli, J. (2025). The Impact of Psychological First Aid Training (RAPID‐PFA) on Self‐Efficacy, Perceived Competencies and Disaster Preparedness of Nursing Students in Tunisian Public Institutions: A Randomized Controlled Trial. \u003cem\u003eJournal of Contingencies and Crisis Management\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), e70019. https://doi.org/10.1111/1468-5973.70019\u003c/li\u003e\n \u003cli\u003eMurakami, K., Kuriyama, S., \u0026amp; Hashimoto, H. (2023). Economic, cognitive, and social paths of education to health-related behaviors: Evidence from a population-based study in Japan. \u003cem\u003eEnvironmental Health and Preventive Medicine\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e, 9\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eMuth\u0026eacute;n, B., \u0026amp; Muth\u0026eacute;n, L. (2017). Mplus. In \u003cem\u003eHandbook of item response theory\u003c/em\u003e (pp. 507\u0026ndash;518). Chapman and Hall/CRC.\u003c/li\u003e\n \u003cli\u003eNakayama, K., Yonekura, Y., Danya, H., \u0026amp; Hagiwara, K. (2022). Associations between health literacy and information-evaluation and decision-making skills in Japanese adults. \u003cem\u003eBMC Public Health\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 1473. https://doi.org/10.1186/s12889-022-13892-5\u003c/li\u003e\n \u003cli\u003eOdunsi, I. A., \u0026amp; Farris, K. L. (2025). Predicting College Students\u0026rsquo; Preventative Behavior During a Pandemic: The Role of the Health Belief Model, Source Credibility, and Health Literacy. \u003cem\u003eAmerican Behavioral Scientist\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e(12), 1516\u0026ndash;1533. https://doi.org/10.1177/00027642231164044\u003c/li\u003e\n \u003cli\u003eOrem, D. E., Taylor, S. G., \u0026amp; Renpenning, K. M. (1995). \u003cem\u003eNursing: Concepts of practice\u003c/em\u003e (5th ed). Mosby.\u003c/li\u003e\n \u003cli\u003eOsborne, R. H., Elmer, S., Hawkins, M., Cheng, C. C., Batterham, R. W., Dias, S., Good, S., Monteiro, M. G., Mikkelsen, B., Nadarajah, R. G., \u0026amp; others. (2022). Health literacy development is central to the prevention and control of non-communicable diseases. \u003cem\u003eBMJ Global Health\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(12).\u003c/li\u003e\n \u003cli\u003e\u0026Ouml;zt\u0026uuml;rk, M. H., Yeşilyaprak, T., \u0026amp; Kuday, A. D. (2025). Enhancing students\u0026rsquo; knowledge and self-efficacy through integrated first aid and psychological first aid training. \u003cem\u003ePsychology, Health \u0026amp; Medicine\u003c/em\u003e, 1\u0026ndash;14. https://doi.org/10.1080/13548506.2025.2506017\u003c/li\u003e\n \u003cli\u003ePodsakoff, P. M., Podsakoff, N. P., Williams, L. J., Huang, C., \u0026amp; Yang, J. (2024). Common Method Bias: It\u0026rsquo;s Bad, It\u0026rsquo;s Complex, It\u0026rsquo;s Widespread, and It\u0026rsquo;s Not Easy to Fix. \u003cem\u003eAnnual Review of Organizational Psychology and Organizational Behavior\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 17\u0026ndash;61. https://doi.org/10.1146/annurev-orgpsych-110721-040030\u003c/li\u003e\n \u003cli\u003eRoemer, E., Schuberth, F., \u0026amp; Henseler, J. (2021). HTMT2\u0026ndash;an improved criterion for assessing discriminant validity in structural equation modeling. \u003cem\u003eIndustrial Management \u0026amp; Data Systems\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(12), 2637\u0026ndash;2650.\u003c/li\u003e\n \u003cli\u003eRos\u0026aacute;rio, J., Pires, J. C., Dias, S., \u0026amp; Pedro, A. R. (2025).\u0026nbsp;Exploring perceptions of health literacy, healthcare access, and utilisation among higher education students in Alentejo, Southern Portugal: A qualitative study. \u003cem\u003ePLOS One\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(6), e0326575. https://doi.org/10.1371/journal.pone.0326575\u003c/li\u003e\n \u003cli\u003eR\u0026uuml;egg, R. (2022).\u0026nbsp;Decision-making ability: A missing link between health literacy, contextual factors, and health. \u003cem\u003eHLRP: Health Literacy Research and Practice\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), e213\u0026ndash;e223.\u003c/li\u003e\n \u003cli\u003eSaldert, C., Jensen, L. R., Blom Johansson, M., \u0026amp; Simmons-Mackie, N. (2018). Complexity in measuring outcomes after communication partner training: Alignment between goals of intervention and methods of evaluation. \u003cem\u003eAphasiology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(10), 1167\u0026ndash;1193. https://doi.org/10.1080/02687038.2018.1470317\u003c/li\u003e\n \u003cli\u003eSchwarzer, R., \u0026amp; Luszczynska, A. (2008). How to Overcome Health-Compromising Behaviors: The Health Action Process Approach. \u003cem\u003eEuropean Psychologist\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), 141\u0026ndash;151. https://doi.org/10.1027/1016-9040.13.2.141\u003c/li\u003e\n \u003cli\u003eShoji, Y., Irwan, A. M., Ochiai, R., Syahrul, S., Shinohara, E., Fiqri, A. M., Takeuchi, S., Erfina, E., Iida, M., Saleh, A., Moriguchi, F., Nakamura, S., \u0026amp; Kanoya, Y. (2025). The Impact of eHealth Literacy on Health Behaviors for Non-communicable Disease Prevention Among University Students in Japan and Indonesia. \u003cem\u003eCureus\u003c/em\u003e. https://doi.org/10.7759/cureus.78450\u003c/li\u003e\n \u003cli\u003eS\u0026oslash;rensen, K., Van Den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., Slonska, Z., Brand, H., \u0026amp; (HLS-EU) Consortium Health Literacy Project European. (2012). Health literacy and public health: A systematic review and integration of definitions and models. \u003cem\u003eBMC Public Health,\u0026nbsp;\u003c/em\u003e\u003cem\u003e无\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 80. Q2 (医学3区). https://doi.org/10.1186/1471-2458-12-80\u003c/li\u003e\n \u003cli\u003eStormacq, C., Oulevey Bachmann, A., Van Den Broucke, S., \u0026amp; Bodenmann, P. (2023). How socioeconomically disadvantaged people access, understand, appraise, and apply health information: A qualitative study exploring health literacy skills. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(8), e0288381. https://doi.org/10.1371/journal.pone.0288381\u003c/li\u003e\n \u003cli\u003eSuri, V. R., Majid, S., Foo, S., Dumaual-Sibal, H. T., \u0026amp; Chang, Y.-K. (2019). Understanding Health Literacy Through the Lens of Phronesis: The Case of Coronary Artery Disease Patients. In S. Kurbanoğlu, S. \u0026Scaron;piranec, Y. \u0026Uuml;nal, J. Boustany, M. L. Huotari, E. Grassian, D. Mizrachi, \u0026amp; L. Roy (Eds.), \u003cem\u003eInformation Literacy in Everyday Life\u003c/em\u003e (Vol. 989, pp. 166\u0026ndash;175). Springer International Publishing. https://doi.org/10.1007/978-3-030-13472-3_16\u003c/li\u003e\n \u003cli\u003eTang, Y., Lin, C. T., \u0026amp; Wu, L. (2025). Understanding Health Literacy Through Patients\u0026rsquo; Interpretation of Health Education Leaflets: A Thematic Narrative Review. \u003cem\u003eHealth Expectations\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(6), e70479. https://doi.org/10.1111/hex.70479\u003c/li\u003e\n \u003cli\u003eTaylor, S. E., Way, B. M., \u0026amp; Seeman, T. E. (2011). Early adversity and adult health outcomes. \u003cem\u003eDevelopment and Psychopathology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(3), 939\u0026ndash;954. https://doi.org/10.1017/S0954579411000411\u003c/li\u003e\n \u003cli\u003eTaylor, S. G., \u0026amp; Renpenning, K. M. (2011). \u003cem\u003eSelf-care science, nursing theory and evidence-based practice\u003c/em\u003e. Springer Publishing Company.\u003c/li\u003e\n \u003cli\u003eTeam, R. C. (2020). RA language and environment for statistical computing, R Foundation for Statistical. \u003cem\u003eComputing. Archives for Scientific Computing\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eTempone-Wiltshire, J., \u0026amp; Matthews, F. (2025). Embodied Minds: An Embodied Cognitivist Understanding of Mindfulness in Public Health. \u003cem\u003eMindfulness\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(3), 725\u0026ndash;737. https://doi.org/10.1007/s12671-024-02423-5\u003c/li\u003e\n \u003cli\u003eTian, C. Y., Ng, C. C. W., Xie, L., Mo, P. K., Dong, D., Nutbeam, D., \u0026amp; Wong, E. L. (2025). Conceptualisation of critical health literacy\u0026mdash;Insights from Western and East Asian perspectives: A scoping review. \u003cem\u003eBMJ Global Health\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(5).\u003c/li\u003e\n \u003cli\u003eWang, L., Norman, I., Xiao, T., Li, Y., Li, X., Liu, T., Wang, J., Zeng, L., Zhong, Z., Jian, C., \u0026amp; others. (2024). Feasibility and acceptability of a culturally adapted psychological first aid training intervention (Preparing Me) to support the mental health and well-being of front-line healthcare workers in China: A feasibility randomized controlled trial. \u003cem\u003eEuropean Journal of Psychotraumatology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 2299195.\u003c/li\u003e\n \u003cli\u003eWang, S., Wei, J., Zhang, P., Song, J., Chen, J., \u0026amp; Li, G. (2025). The chain mediating effect of self-efficacy and health literacy between proactive personality and health-promoting behaviors among Chinese college students. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 16101. https://doi.org/10.1038/s41598-025-00936-0\u003c/li\u003e\n \u003cli\u003eWatkins, M. W. (2021). \u003cem\u003eA Step-by-Step Guide to Exploratory Factor Analysis with SPSS\u003c/em\u003e (1st ed.). Routledge. https://doi.org/10.4324/9781003149347\u003c/li\u003e\n \u003cli\u003eWolf, E. J., Harrington, K. M., Clark, S. L., \u0026amp; Miller, M. W. (2013). Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety. \u003cem\u003eEducational and Psychological Measurement\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(6), 913\u0026ndash;934. https://doi.org/10.1177/0013164413495237\u003c/li\u003e\n \u003cli\u003eWorld Medical Association. (2001). World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. \u003cem\u003eBulletin of the World Health Organization\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e(4), 373\u0026ndash;374.\u003c/li\u003e\n \u003cli\u003eWu, Y., Howarth, M., Zhou, C., Ji, X., Ou, J., \u0026amp; Li, X. (2019).\u0026nbsp;Reporting of ethical considerations in clinical trials in Chinese nursing journals. \u003cem\u003eNursing Ethics\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(4), 973\u0026ndash;983. https://doi.org/10.1177/0969733017722191\u003c/li\u003e\n \u003cli\u003eXia, Y., \u0026amp; Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. \u003cem\u003eBehavior Research Methods\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(1), 409\u0026ndash;428. https://doi.org/10.3758/s13428-018-1055-2\u003c/li\u003e\n \u003cli\u003eYusoff, M. S. B. (2019). ABC of content validation and content validity index calculation. \u003cem\u003eEducation in Medicine Journal\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 49\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003eZeng, M., Liu, Y., He, Y., \u0026amp; Huang, W. (2025). Correction: Relationship between stroke knowledge, health information literacy, and health self-management among patients with stroke: Multicenter cross-sectional study. \u003cem\u003eJMIR Medical Informatics\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, e80547.\u003c/li\u003e\n \u003cli\u003eZhang, M. J., Guo, X., Wang, R. S., Mao, X., Xiang, G., Li, W., Zuo, C., Zhou, H., \u0026amp; Xu, D. R. (2025). Health literacy model integrating health education, health behaviors, self-rated health, and socioeconomic status in the Chinese population. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 32320. https://doi.org/10.1038/s41598-025-07094-3\u003c/li\u003e\n \u003cli\u003eZhou, Y., Xu, J., Wang, R., \u0026amp; Guan, X. (2025). Understanding how digital health literacy affects health self-management behaviors: The mediating role of self-efficacy in college students. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 27230. https://doi.org/10.1038/s41598-025-12726-9\u003c/li\u003e\n \u003cli\u003eZhu, W., Liu, J., Lou, H., Mu, F., \u0026amp; Li, B. (2024). The impact of electronic health literacy on emotional management ability among college students: The mediating roles of peer relationships and exercise self-efficacy. \u003cem\u003eBMC Psychology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 747. https://doi.org/10.1186/s40359-024-02276-6\u003c/li\u003e\n\u003c/ol\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-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Psychosocial competence, Self-care skills, Health information literacy, Chain mediation, Network analysis","lastPublishedDoi":"10.21203/rs.3.rs-9074591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9074591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study aimed to elucidate the mechanisms through which psychosocial competence shapes self-care skills in college students by testing a hypothesized chain mediation model: \"psychosocial competence → health-promoting behavior → health information literacy → self-care skills.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A cross-sectional survey was conducted among 390 freshmen and sophomores. The structural equation model (SEM) was used to test the macro-level paths, while the network analysis based on the EBICglasso algorithm was adopted to explore the micro-level item associations, and the bridge strength was calculated to identify the nodes connecting different theoretical communities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e SEM revealed significant indirect effects for health-promoting behavior (β = 0.126, p = 0.020), health information literacy (β = 0.385, p = 0.001), and the chain pathway (β = 0.083, p = 0.002). The total indirect effect accounted for 67.1% of the total effect, and the model explained 76.7% of the variance in self-care skills (R² = 0.767). Network analysis identified two high-bridge-strength nodes: SCA1 (\"Interpret fitness test data\") and HIL4 (\"Make decisions based on information\"), which served as key connectors linking the communities of Psychosocial Competence (PSC), Health-Promoting Behavior (HPB), Health Information Literacy (HIL), and Self-Care Skills (SCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Psychosocial competence influences self-care skills through multiple pathways, with health information literacy playing a central mediating role. The identified bridge nodes, SCA1 and HIL4, represent potential precise targets for health interventions among college students.\u003c/p\u003e","manuscriptTitle":"Multiple Mediating Pathways from Psychosocial Competence to Self-Care Skills in University Students: A Cross-Validation Study Using Structural Equation Modeling and Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 07:42:18","doi":"10.21203/rs.3.rs-9074591/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-08T09:07:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164662404218960413914842582069710028745","date":"2026-04-07T19:57:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T13:58:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T12:18:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T03:29:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-13T12:19:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-03-12T14:08:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9277a11d-61ab-476f-8eec-d63ec4c6b339","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T07:42:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 07:42:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9074591","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9074591","identity":"rs-9074591","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}