Social Support and Decision Fatigue in Breast Cancer Patients: A Chain Mediation Model of Psychological Resilience and Decision Self-Efficacy

Social Support and Decision Fatigue in Breast Cancer Patients: A Chain Mediation Model of Psychological Resilience and Decision Self-Efficacy | 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 Social Support and Decision Fatigue in Breast Cancer Patients: A Chain Mediation Model of Psychological Resilience and Decision Self-Efficacy Hui-Xia Wu, Ying-Jie Yao, Xiang-Yu Liu, yujia Fan, Si-Jie Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7250769/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study aimed to examine the chain-mediating roles of psychological resilience and decision self-efficacy in the relationship between social support and decision fatigue among breast cancer patients, providing evidence for targeted interventions. Methods A convenience sample of 452 breast cancer patients was recruited from one hospital in China.Data were collected from January to May 2025 using Self-report questionnaires,including the demographic and clinical characteristics, Decision Fatigue Scale (DFS),Decision Self-Efficacy Scale (DSES) ,Social Support Rating Scale (SSRS), andConnor-Davidson Resilience Scale (CD-RISC). Data were analyzed using IBM SPSS 26.0 for descriptive and correlational analyses and AMOS 26.0 for mediation analysis. Results Decision fatigue was negatively correlated with social support (ρ = -0.280*), decision self-efficacy (ρ = -0.511), and psychological resilience (ρ = -0.533). Both psychological resilience and decision self-efficacy independently mediated the relationship between social support and decision fatigue, with a significant sequential mediation effect. The chain-mediating pathway accounted for 36.7% of the total effect of social support on decision fatigue. Conclusion Psychological resilience and decision self-efficacy serve as sequential mediators between social support and decision fatigue, highlighting their critical roles in mitigating decision-related exhaustion. These findings underscore the importance of integrated psychosocial support in clinical management. Breast cancer patients Decision fatigue Psychological resilience Social support Decision self-efficacy Figures Figure 1 What is already known Decision fatigue is an understudied but critical issue in breast cancer patients, associated with impaired decision quality and psychological distress . Social support demonstrates protective effects against decision-related stress in chronic illness populations. Psychological resilience and decision self-efficacy independently mitigate cognitive depletion in medical decision-making What this paper adds First evidence confirming psychological resilience and decision self-efficacy as sequential mediators linking social support to reduced decision fatigue in breast cancer patients. Empirical validation of the Conservation of Resources Theory in explaining decision fatigue, demonstrating how external support (social support) transforms into internal resources (resilience → self-efficacy). Practical identification of modifiable targets (social support interventions, resilience training) for clinical decision-support systems. Background Breast cancer remains the most commonly diagnosed malignancy among women worldwide, with 2.3 million new cases reported in 2022 [1] . Advances in medical technologies and treatment modalities have significantly improved prognosis; however, these developments have also introduced increasing complexity into the clinical decision-making processes faced by patients [2] . Throughout the disease trajectory, individuals are frequently required to make multifaceted treatment decisions, including choices about surgical procedures, chemotherapy and radiotherapy regimens, targeted and immunotherapy options, as well as long-term follow-up and survivorship planning [3, 4] . This sustained high-intensity decision-making load may deplete cognitive resources, ultimately leading to a specific psychological phenomenon known as decision fatigue . Decision fatigue refers to a state of diminished cognitive capacity resulting from repeated decision-making, characterized by decision avoidance, impulsive choices, or reduced decision quality [5] . Existing research has primarily focused on healthcare providers (e.g., clinicians, nurses) [6–8] , studies on patients are limited.However, the impact of decision fatigue on cancer patients warrants attention, as it may not only alter decision-making behaviors and compromise decision quality but also contribute to adverse psychological outcomes, including decision regret, heightened anxiety symptoms, and an increased risk of post-traumatic stress disorder [3–5] .Given these potential consequences, a deeper understanding of the mechanisms underlying decision fatigue is critical for optimizing clinical decision-support systems and improving patient outcomes. Further research is needed to explore the prevalence, predictors, and interventions for decision fatigue in cancer patients to mitigate its negative effects. According to the conceptual framework of decision fatigue proposed by Pignatiello et al. (2021) [5] , decision fatigue is influenced by three main dimensions: repeated decision-making, self-regulatory capacity, and contextual factors. Among these, social support is considered a key contextual factor, referring to both emotional and instrumental assistance derived from one’s social network [9] .Empirical evidence from the Ottawa Decision Support Framework indicates that adequate social support significantly improves decision-making behaviors [10] . Furthermore, clinical studies have demonstrated its stress-buffering effects in oncology populations [11] . Emerging research further supports these findings, revealing that higher levels of social support correlate with increased psychological resilience [12] , greater decision self-efficacy [13] and improved quality of life [14] . Despite these insights, the precise mechanisms through which social support influences decision fatigue remain underexplored, highlighting a critical gap in the current literature. The influence of social support on decision fatigue may operate through complex psychological mechanisms. According to the Conservation of Resources (COR) theory [15] , external resources must be internalized as psychological resources to exert their protective effects. Psychological resilience—defined as an individual’s capacity to adapt and thrive in the face of adversity [16] —may serve as a critical mediator in this process. A systematic review indicates that robust social support enhances resilience [17] , and individuals with higher resilience exhibit superior emotion regulation and more efficient cognitive recovery [18] . These adaptive capacities may mitigate the cognitive depletion characteristic of decision fatigue, which arises from prolonged decision-making demands.While prior research has established the association between resilience and Self-regulation [19] , its specific role in buffering decision fatigue within oncology populations remains underexplored. In parallel, decision self-efficacy—defined as an individual's confidence in making sound and effective decisions [20] —is also a crucial psychological factor influencing decision fatigue [21] . Patients with low self-efficacy often experience anxiety and uncertainty when confronted with treatment decisions, leading to delayed decision-making or excessive reliance on others, which increases cognitive burden and fatigue [22] . Current evidence indicates that social support can enhance self-efficacy, consequently improving psychological adaptation [13] . Furthermore, multiple empirical studies have demonstrated that decision self-efficacy effectively alleviates decision conflict in patients [23, 24] .However, while theoretical associations between social support, decision self-efficacy and decision fatigue have received partial empirical support, the mediating pathway remains insufficiently investigated in breast cancer populations, warranting further rigorous examination. Taken together, existing evidence has demonstrated associations between social support, resilience, decision self-efficacy, and decision fatigue among patients with cancer and other chronic conditions. However, it remains unclear how these factors collectively influence decision fatigue in breast cancer patients. Moreover, the potential serial mediating roles of resilience and decision self-efficacy in the relationship between social support and decision fatigue have not been systematically investigated. Understanding these psychological mechanisms is critically important, as it could provide healthcare professionals with evidence-based strategies to reduce decision fatigue and improve decision-making outcomes during breast cancer treatment. Therefore, based on the Conservation of Resources Theory and the existing literature, this study aimed to examine the psychosocial factors associated with decision fatigue and to elucidate the serial mediating roles of resilience and decision self-efficacy between social support and decision fatigue among breast cancer patients. Our theoretical hypotheses were as follows: social support, resilience and decision self-efficacy are significantly associated with decision fatigue (H1); resilience mediates the relationship between social support and decision fatigue (H2); decision self-efficacy mediates the relationship between social support and decision fatigue (H3); and resilience and decision self-efficacy have a serial mediating effect between social support and decision fatigue (H4). 1. Methods This cross-sectional study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology( STROBE) guidelines (see Appendix S1for observational research [25] . 1.1 Setting and participants The study was conducted in the breast cancer ward of one Cancer Hospital, a tertiary care facility specializing in oncology services and serving as a regional referral center for cancer diagnosis and treatment in Hunan Province, China. The hospital operates under the provincial health administration and adheres to national standards for oncology care, with multidisciplinary teams managing patient treatment. Eligible participants were recruited via convenience sampling from hospitalized breast cancer patients between January and May 2025. Inclusion criteria were: (a) age ≥ 18 years; (b) histopathologically confirmed breast cancer; (c) current inpatient status; (d) awareness of their cancer diagnosis; (e) being the primary decision-maker or playing a dominant role in treatment decisions; and (f) voluntary participation with signed informed consent. Exclusion criteria included: (a) severe psychiatric or cognitive disorders; (b) comorbid with other malignancies or severe physical illnesses; or (c) inability to comprehend the questionnaire after guidance. A priori power analysis was performed using GPower 3.1 [26] . Based on a moderate effect size (f*²=0.15), α = 0.05 (two-tailed), 80% power, and 8 predictors (3 dimensions each for social support and resilience, plus 1 each for decision self-efficacy and decision fatigue), the minimum required sample size was 128.Ultimately, 452 participants were enrolled. 1.2 Survey Procedure Before data collection, the study objectives were explained to potential participants. The protocol was approved by the Medical Ethics Review Committee of Hunan Cancer Hospital. Surveys were administered via paper-based questionnaires. After obtaining informed consent, trained researchers provided standardized instructions to participants, emphasizing anonymity and confidentiality. Participants could withdraw at any time without penalty. Questionnaires were distributed in the ward during designated periods. Research assistants collected completed surveys and monitored participation rates. To minimize missing data, clarifications were offered if participants had questions. All responses were de-identified prior to analysis. 2.3 Measures The survey instruments used in this study were validated Chinese versions of internationally established scales. Measurement tools were selected based on a systematic review of breast cancer patient decision-making literature. Decision fatigue and related factors 2.3.1. General characteristics questionnaire The self-designed demographic questionnaire included: (a) sociodemographic data (gender, age, marital status, education level, occupation, residence, household income, payment method); (b) clinical characteristics (pathological stage, disease duration, hospitalization frequency, treatment modality, and decision-making frequency). All items were developed through comprehensive literature review of factors influencing decision fatigue in breast cancer patients. 2.3.2. Decision Fatigue Scale (DFS) Decision fatigue was measured using the Chinese version of DFS originally developed by Hickman et al. (2018) [27] and cross-culturally adapted by Pan et al. This unidimensional 9-item instrument uses a 4-point Likert scale (0 = "strongly disagree" to 3 = "strongly agree"), with total scores ranging 0–27. Higher scores indicate greater decision fatigue. The original scale demonstrated excellent reliability (Cronbach's α = 0.87, test-retest reliability = 0.90), while the Chinese version showed α = 0.854 and test-retest reliability = 0.863. In our study, the Cronbach's α was 0.899. 2.3.3. Decision Self-Efficacy Scale (DSES) The Chinese version of DSES [28] (O'Connor, 1995; Wang's adaptation) assessed patients' confidence in medical decision-making. This 11-item unidimensional scale employs a 5-point Likert scale (0 = "not at all confident" to 4 = "very confident"), with total scores ranging 0–44. Higher scores reflect greater decision self-efficacy. The Chinese version reported α = 0.918. Our study obtained α = 0.959. 2.3.4. Connor-Davidson Resilience Scale (CD-RISC) Psychological resilience was evaluated using the Chinese CD-RISC [29] , containing 25 items across three subscales: tenacity (13 items), strength (8 items), and optimism (4 items). Using a 5-point Likert scale (0 = "never" to 4 = "always"), total scores range 0-100 with clinical cutoffs: ≤60 (poor), 61–69 (moderate), 70–79 (good), ≥ 80 (excellent). The Chinese version reported α = 0.933. Our study showed α = 0.957. 2.3.5. Social Support Rating Scale (SSRS) The SSRS [30] was used to measure the level of social support an individual received.It measured three dimensions: subjective support (4 items), objective support (3 items), and support utilization (3 items). Items 1–4 and 8–10 employed a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree), while items 6–7 used binary scoring (0 = no support, 1 = support available per source). Total scores ranged from 0 to 66, with higher scores indicating greater social support. The scale demonstrated excellent reliability in the original study (Cronbach's α = 0.920) and acceptable internal consistency in our study (α = 0.795). 2.4 Data analysis Data were analyzed using IBM SPSS 26.0 for descriptive and correlational analyses and AMOS 26.0 for structural equation modeling (SEM). Descriptive statistics were used to describe participants' characteristics and study variables, with continuous variables presented as medians (M) and interquartile ranges (P25, P75) due to non-normal distributions, and categorical variables as frequencies and percentages (%). Spearman’s correlation analysis was used to examine bivariate relationships among variables. To assess potential common method bias, Harman’s single-factor test was conducted, with the criterion that the first unrotated factor should explain < 40% of the total variance [31] . The serial mediating effects of resilience (mediator 1) and decision self-efficacy (mediator 2) between social support and decision fatigue were analyzed using a bias-corrected bootstrapping analysis (5000 resamples) in AMOS. In the mediation model, the total effect referred to the effect of social support (independent variable) on decision fatigue (dependent variable), comprising a direct effect and indirect effects through resilience alone, decision self-efficacy alone, and the sequential pathway (social support → resilience → decision self-efficacy → decision fatigue). Standardized regression coefficients (β) and 95% confidence intervals (CIs) were reported for direct, indirect, and total effects. The mediating effect was considered statistically significant if the 95% CI of the indirect effect did not include zero. A significance level of .05 (two-sided) was used for all statistical tests. 2.5 Ethical considerations Ethics approval was obtained from the Medical Ethics Review Committee of Hunan Cancer Hospital (reference number: 2025 Research Simplified Procedure Review [08]). All methods were carried out in accordance with the Declaration of Helsinki and relevant guidelines. Informed consent was obtained from all participants. As stated in the written consent form included in the questionnaire packet, consent was confirmed by participants’ completion and return of the survey. Participants were informed of their right to refuse or withdraw from the study at any time without negative consequences. 2. RESULT 3.1 Participants characteristics Among 480 eligible breast cancer patients, 452 (94.1%) participated, with non-participation primarily due to logistical constraints (time/inconvenience). The cohort comprised women aged 18–>60 years (45–60 years: 56.9%), predominantly married (96.9%), and residing in non-urban areas (79.4%). Occupational distribution showed 20.1% farmers, 16.6% unemployed, and 13.7% manual laborers. Education levels included 50.4% junior high school and 11.5% ≥bachelor's degree. Most households (66.9%) earned < 5,000 CNY/month. Clinically, 84.1% had < 1-year disease duration, with stages II/IV representing 70% of cases.Details on medical payment methods, decision frequency, and treatment modalities were also collected but are not the focus of the current analysis.Participants' characteristics are shown in Table 1 . Table 1 Sociodemographic Characteristics of Breast Cancer Patients (n = 452) Characteristic n (%) Age (years) 18 ~ 44 144 (31.9) 45 ~ 59 257 (56.9) ≥ 60 51 (11.3) Occupation Unemployed 75 (16.6) Government employee 54 (11.9) Self-employed 52 (11.5) Worker 62 (13.7) Farmer 91 (20.1) Other 83 (18.4) Retired 35 (7.7) Place of Residence Urban 93 (20.6) Town 194 (42.9) Rural 165 (36.5) Household Monthly Income (CNY) < 2000 202 (44.7) 2000–4999 101 (22.3) 5000–9999 120 (26.5) 10000–19999 29 (6.4) Marital Status Unmarried 5 (1.1) Married 438 (96.9) Divorced/Widowed 9 (2.0) Education Level Primary or below 74 (16.4) Junior high school 228 (50.4) High school/Technical school 51 (11.3) Junior college 47 (10.4) Bachelor’s degree or above 52 (11.5) Medical Payment Method Employee medical insurance 55 (12.2) Urban Resident Basic Medical Insurance 127 (28.1) New Rural Cooperative Medical Scheme 206 (45.6) Self-pay 64 (14.2) Disease Duration (years) 2–5 35 (7.7) ≥ 5 32 (7.1) Pathological Stage Stage I 42 (9.3) Stage II 223 (49.3) Stage III 85 (18.8) Stage IV 92 (20.4) Uncertain 10 (2.2) Treatment Modalities 1 80 (17.7) 2 167 (36.9) 3 100 (22.1) ≥ 4 105 (23.2) 3.2 Descriptive statistics of variables Table 2 presents the descriptive statistics (median and interquartile range) of key study variables among 452 breast cancer patients. Median scores were as follows: decision self-efficacy = 31.00, social support = 43.00, decision fatigue = 16.00, and psychological resilience = 63.00. The subdimensions of social support and resilience further reflect their multidimensional constructs. Table 2 Scores of various scales in breast cancers Variable Median (P25, P75) Decision Self-efficacy 31.00 (22.00, 37.00) Social Support 43.00 (37.00, 46.50) Objective Support 10.00 (8.00, 11.00) Subjective Support 26.00 (22.00, 28.00) Support Utilization 7.00 (6.00, 9.00) Decision Fatigue 16.00 (14.00, 21.00) Psychological Resilience 63.00 (49.00, 73.50) Tenacity 31.00 (24.00, 38.00) Strength 22.00 (17.00, 25.00) Optimism 9.00 (7.00, 11.00) 3.3 Correlation analysis between variables Spearman’s rank-order correlation analysis (Table 3 ) revealed statistically significant associations between variables (all p < .001). Decision self-efficacy was strongly correlated with psychological resilience (ρ = 0.641) and moderately with social support (ρ = 0.349). Psychological resilience and social support were both negatively correlated with decision fatigue (ρ = -0.533 and ρ = -0.280, respectively). Additionally, decision self-efficacy showed a moderate negative correlation with decision fatigue (ρ = -0.511). These correlations provide empirical justification for testing a serial mediation model in which social support influences decision fatigue through psychological resilience and decision self-efficacy. Table 3 Bivariate Correlations Among Study Variables (N = 452) Variable 1 2 3 4 1. Decision self-efficacy — 2. Social support .349** — 3. Resilience .641** .347** — 4. Decision fatigue − .511** − .280** − .533** — Note: **p < .001. N = 452. 3.4 Common Method Variance Assessment To mitigate potential common method bias arising from self-reported data, several procedural remedies were implemented during data collection, including emphasizing the anonymity and confidentiality of the questionnaires to breast cancer patients, as well as clarifying that the data would be used solely for scientific research purposes. Furthermore, Harman's single-factor test was conducted to assess common method variance, revealing 12 factors with eigenvalues greater than 1. The first factor accounted for 33.156% of the total variance, which falls below the critical threshold of 40%, indicating that common method bias did not substantially affect the study findings [31] . 3.5 Serial Mediation Analysis A chain mediation model was constructed using AMOS 26.0. In this model, social support was specified as the independent variable, psychological resilience and decision self-efficacy served as sequential mediators, and decision fatigue was the dependent variable. The hypothesized model is depicted in Fig. 1 . Maximum likelihood estimation was used for model fitting. Model fit indices indicated acceptable to good fit(Table 4 ): χ²/df = 3.926, GFI = 0.966, AGFI = 0.923, CFI = 0.971, TLI = 0.949, NFI = 0.962, RMSEA = 0.081 (90% CI: 0.060–0.102), and AIC = 102.816. Although RMSEA slightly exceeded the ideal threshold (0.90), indicating that the proposed serial mediation model fits the data adequately. χ²/df: Chi-square to degrees of freedom ratio. GFI/AGFI: Goodness-of-Fit Index/Adjusted Goodness-of-Fit Index. RMSEA: Root Mean Square Error of Approximation (90% confidence interval). CFI/NFI/TLI: Comparative Fit Index, Normed Fit Index, Tucker–Lewis Index. RMR: Root Mean Square Residual (standardized values preferred; absolute RMR > 0.08 may indicate poor fit). Table 4 Model Path Analysis Results of the Structural Equation Model (N = 452) Fit Index Value Recommended Threshold χ²/df 3.926 < 5 (acceptable), 0.90 AGFI 0.923 > 0.90 RMSEA (90% CI) 0.081 [0.060–0.102] < 0.08 (good), 0.90 NFI 0.962 > 0.90 TLI 0.949 > 0.90 RMR 1.207 < 0.08 AIC 102.816 Lower = better Notes. χ²/df: Chi-square to degrees of freedom ratio. GFI/AGFI: Goodness-of-Fit Index/Adjusted Goodness-of-Fit Index. RMSEA: Root Mean Square Error of Approximation (90% confidence interval). CFI/NFI/TLI: Comparative Fit Index, Normed Fit Index, Tucker–Lewis Index. RMR: Root Mean Square Residual (standardized values preferred; absolute RMR > 0.08 may indicate poor fit). As shown in Table 5 , social support positively predicted psychological resilience (β = 0.503, p < .001) and decision self-efficacy (β = 0.221, p < .001), but did not have a significant direct effect on decision fatigue (β = -0.072, p = .237). Psychological resilience significantly predicted both decision self-efficacy (β = 0.536, p < .001) and negatively predicted decision fatigue (β = -0.372, p < .001). Decision self-efficacy also negatively predicted decision fatigue (β = -0.210, p < .001). These results support a serial mediation mechanism where social support reduces decision fatigue via increased resilience and improved self-efficacy. Table 5 Path Analysis Results of the Structural Equation Model (N=452) Path R² β SE CR p 95% CI Social Support → Psychological Resilience 0.253 0.503*** 0.154 6.965 <.001 [0.397, 0.610] Decision Self-efficacy 0.455 0.221*** 0.548 3.883 <.001 [0.109, 0.332] Decision Fatigue 0.330 -0.072 0.281 -1.183 0.237 [-0.214, 0.061] Psychological Resilience → Decision Self-efficacy 0.536*** 0.236 10.218 <.001 [0.429, 0.631] Decision Fatigue -0.372*** 0.129 -6.218 <.001 [-0.510, -0.235] Decision Self-efficacy → Decision Fatigue -0.210*** 0.026 -3.837 <.001 [-0.341, -0.081] Notes. ***p < .001; **p < .01; *p < .05 (two-tailed). β = Standardized path coefficient; SE = Standard Error; CR = Critical Ratio (z-score); CI = Confidence Interval. Table 6 presents the standardized regression coefficients for the direct, indirect, and total effects of social support on decision fatigue through resilience and decision self-efficacy. The mediation model analysis revealed that the total standardized effect of social support on decision fatigue was − 0.363 (95% CI: -0.473 to -0.240). Within this total effect, the direct effect was − 0.072 (95% CI: -0.214 to 0.061, p = 0.294), accounting for 19.83% of the total effect, while the combined indirect effects were − 0.291 (95% CI: -0.381 to -0.218), representing 80.17% of the total effect. Specifically, the independent mediating effect through resilience was − 0.187 (95% CI: -0.276 to -0.119), contributing 51.52% of the total effect; the independent mediating effect through decision self-efficacy was − 0.047 (95% CI: -0.090 to -0.019), accounting for 12.95%; and the chain mediating effect through both resilience and decision self-efficacy was − 0.057 (95% CI: -0.107 to -0.022), contributing 15.70%. All indirect effects were statistically significant (ps < 0.001). Table 6 Mediation Analysis Results Using Bootstrapping (N = 452) Effect Pathway β Boot SE 95% BCa CI p Effect Proportion 1. Indirect Effects Social Support → Resilience → Decision Fatigue − .187 0.039 [-0.276, -0.119] < .001 51.52% Social Support →Decision Self-efficacy → Decision Fatigue − .047 0.018 [-0.090, -0.019] < .001 12.95% Social Support → Resilience →Decision Self-efficacy → Decision Fatigue − .057 0.021 [-0.107, -0.022] < .001 15.70% Total Indirect Effect − .291 0.042 [-0.381, -0.218] < .001 80.17% 2. Direct Effect Social Support → Decision Fatigue (Direct) − .072 0.070 [-0.214, 0.061] .294 19.83% 3. Total Effect − .363 0.059 [-0.473, -0.240] < .001 100% Notes. β = Standardized indirect/direct effect; Boot SE = Bootstrap standard error; BCa CI = Bias-corrected and accelerated confidence interval (5,000 resamples). Effect proportions calculated as |indirect/total effect|×100%. Bolded pathways indicate statistical significance (p < .05). 3. Discussion This study provides groundbreaking insights by systematically verifying the serial mediation mechanism through which social support influences decision fatigue in breast cancer patients via psychological resilience and decision self-efficacy. To our knowledge, this is the first empirical investigation that clarifies this pathway, thereby extending current theoretical frameworks on cancer-related decision-making processes. Our findings offer novel empirical support and practical implications for understanding the psychological mechanisms underlying decision fatigue. The results fully support all four of our proposed hypotheses (H1–H4) and further confirm the applicability of Conservation of Resources (COR) theory [15] in this context. This study demonstrates that breast cancer patients experience a moderate level of decision fatigue, consistent with previous literature. Given the complexity of breast cancer treatment regimens, patients are often required to make multiple critical medical decisions within a limited timeframe [4] , which may exacerbate their decision fatigue. The sample in this study primarily consisted of individuals from vulnerable socioeconomic backgrounds, exhibiting the “three-lows” pattern: low educational attainment (only 11.5% held a bachelor's degree or higher), low household income (66.9% reported monthly income < RMB 5000), and low urbanization (a high proportion with rural residency). Additionally, 84.1% of participants were within the first year post-diagnosis, and nearly 70% were diagnosed at stage II or IV, indicating high decision-making burden. These characteristics align with the high-risk profiles previously identified in the literature [32] , underscoring the urgent need for tailored decision support interventions for patients with low socioeconomic status. We recommend the development of customized decision aids for disadvantaged populations in clinical practice [33] , such as low-cognitive-load decision booklets, extended consultation time policies, staged decision guidance, and community-based health education programs. Future research should also investigate the influence of sociodemographic factors on the formation of decision fatigue and explore the optimization of decision support systems for rural breast cancer patients under the tiered healthcare model. 4.1 The Effect of Social Support on Decision Fatigue We further revealed a significant indirect-only relationship between social support and decision fatigue, with the direct path being non-significant (β = -0.072, p = 0.294), and the total indirect effect accounting for 80.17% of the total effect (β = -0.291). This finding supports Hypothesis 1 and validates a central tenet of COR theory: external resources must be internalized to alleviate psychological exhaustion effectively [15] . Social support not only offers emotional, informational, and instrumental resources but also fosters confidence in disease knowledge and self-management [34] .In the context of breast cancer, family members, as primary caregivers and decision partners, play a crucial supportive role [35] . Effective integration of family, healthcare, and community resources can alleviate both psychological and financial stress for patients and surrogate decision-makers, thereby enhancing engagement and decisional congruence. Moreover, shared decision-making (SDM) has been recognized as a core strategy for improving decision quality and reducing fatigue [2] . Clinical nursing should enhance the assessment and intervention of patients’ social support and promote a hospital-community-family collaborative care model, enabling better resource integration and information flow. For breast cancer, context-specific decision aids should be developed to improve decision preparedness and participation. 4.2 Serial Mediation by Psychological Resilience and decision self-efficacy The results supported Hypothesis 2 by identifying a significant mediating role of psychological resilience, which independently accounted for 51.52% of the total effect. This suggests that social support alleviates decision fatigue by enhancing resilience, a key internal resource. Individuals with high resilience demonstrate better emotional regulation, adaptability, and resource management, thus coping more effectively with complex medical decisions [14] . Previous studies have confirmed the mediating role of resilience between social support and self-care ability in breast cancer patients [12] . Importantly, this study is the first to empirically confirm the protective role of resilience against decision fatigue in breast cancer patients, offering theoretical guidance for resilience-based interventions. Resilient patients are more likely to adopt proactive coping strategies and conserve self-control resources, thereby reducing mental burden [36, 37] . Given its plasticity, we recommend integrating standardized programs such as Mindfulness-Based Stress Reduction (MBSR) and implementing stratified interventions tailored to baseline resilience levels. In line with Hypothesis 3, decision self-efficacy also significantly mediated the relationship between social support and decision fatigue, accounting for 12.95% of the total effect. Positive support from social networks may boost confidence, perceived control, and decision competence, helping patients handle complex medical choices more effectively and reducing negative emotional responses such as anxiety and confusion [13] . Patients with higher decision self-efficacy are more likely to actively seek health information, engage in shared treatment decision-making, pose questions to physicians, and share concerns with oncology nurses, thereby mitigating decision fatigue [38] .This finding aligns with studies in critically ill [23] and Colorectal Cancer [24] , which have demonstrated the protective role of decision self-efficacy. Most importantly, our findings validate Hypothesis 4: social support indirectly reduces decision fatigue by sequentially enhancing resilience and decision self-efficacy. This serial mediation pathway accounted for 15.7% of the total effect, revealing a comprehensive psychological mechanism:Social support → Psychological resilience → decision self-efficacy → Decision fatigue Specifically, social support creates an enabling environment for developing resilience [14] , which in turn strengthens confidence in decision-making [39] . Decision self-efficacy, as a cognitive mediator, buffers the impact of complex treatment decisions by reducing helplessness, cognitive conflict, and information overload [23, 24] . This model suggests that relieving decision fatigue requires not only sufficient external support but also systematic interventions to convert external resources into internal psychological mechanisms. Future interventions should focus on the “social support–resilience–decision self-efficacy” triadic mechanism, with the development of multi-level, context-adaptive support tools such as resilience training programs, decision-making simulations, and family-inclusive communication strategies, to improve preparedness and active patient participation in healthcare decisions. 4. Limitations This study has several limitations. First, the cross-sectional design may constrain the interpretation of causal relationships between the associated factors and decision fatigue, as existing literature suggests that social support and resilience may mutually influence each other [40] , and reverse causality cannot be ruled out. Future research could employ a longitudinal design to more comprehensively elucidate these relationships over time. Second, participants were recruited from a single center in China using convenience sampling, which may limit the generalizability of the findings. Future studies involving multicenter samples could further validate the current results. Third, other internal and external factors—such as the quality of doctor-patient communication—might influence patients' decision fatigue, and subsequent research could explore additional potential contributing factors. Finally, data were collected via self-reported questionnaires, which may introduce social desirability or recall bias. 5.1 Implications to clinical practice Based on Hobfoll's Conservation of Resources theory framework, this study elucidates the intrinsic mechanisms linking social support, psychological resilience, decision self-efficacy, and decision fatigue in breast cancer patients, providing significant implications for clinical decision support. The findings suggest that an integrated intervention approach combining social support enhancement, resilience training, and decision self-efficacy improvement may effectively alleviate patients' decision fatigue symptoms. In clinical practice, we recommend healthcare teams to: (1) conduct comprehensive assessments of patients' social support networks and individualized needs, subsequently developing personalized support plans to optimize social resource utilization; (2) incorporate standardized resilience training programs (e.g., mindfulness-based stress reduction) into routine decision support protocols; and (3) implement evidence-based interventions such as decision-making skills training and scenario simulations to enhance patients' decision self-efficacy. This systematic intervention strategy, by simultaneously strengthening external support resources and internal psychological capital, can significantly improve the quality of patients' decision-making processes while mitigating the negative impacts of decision fatigue. The study provides empirical evidence supporting the development of comprehensive decision support systems for breast cancer patients facing complex treatment choices. 5. CONCLUSION Our study demonstrates that breast cancer patients experience moderate-to-high levels of decision fatigue. Grounded in Hobfoll’s Conservation of Resources (COR) theory, we found that social support, psychological resilience, and decision self-efficacy exert both direct and indirect effects on decision fatigue. Importantly, psychological resilience and decision self-efficacy serve as sequential mediators in the association between social support and decision fatigue, revealing a chain mediation pathway.Future psychosocial interventions aimed at improving decision fatigue should highlight the important roles of social support, resilience and decision self-efficacy.. Declarations CONFLICT OF INTEREST STATEMENT None. FUNDING INFORMATION This work was partly supported by the Scientific Research Project of Hunan Health Commission(Grant number: w20243281) and the Graduate Independent Exploration and Innovation Project of Central South University(Grant number: 2025ZZTS0985). Author Contribution Hui-Xia Wu: Conception, data acquisition, analysis, interpretation, manuscript drafting.Ying-Jie Yao: Conception, data acquisition, manuscript revision.Xiang-Yu Liu: Conception, methodology, data analysis/interpretation, manuscript revision.Yu-Jia Fan, Si-Jie Wu, Rui-Hong Zeng: Conception, data interpretation, manuscript revision. ACKNOWLEDGEMENTS We are grateful to all patients for their participation in the survey. Data Availability Data Availability StatementThe datasets generated and analyzed during this study are not publicly available due to [reason: e.g., patient privacy/ethical restrictions] but are available from the corresponding author on reasonable request. The de-identified data supporting the findings can be shared for research purposes after signing a data access agreement and approval from the Ethics Committee of Hunan Cancer Hospital. References BRAY F, LAVERSANNE M, SUNG H, et al. 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AIZPURUA-PEREZ I, PEREZ-TEJADA J. Resilience in women with breast cancer: A systematic review [J]. Eur J Oncol Nurs, 2020, 49: 101854. PALAMARCHUK I S, VAILLANCOURT T. Mental Resilience and Coping With Stress: A Comprehensive, Multi-level Model of Cognitive Processing, Decision Making, and Behavior [J]. Front Behav Neurosci, 2021, 15: 719674. ARTUCH-GARDE R, GONZáLEZ-TORRES M D C, DE LA FUENTE J, et al. Relationship between Resilience and Self-regulation: A Study of Spanish Youth at Risk of Social Exclusion [J]. Front Psychol, 2017, 8: 612. NOLAN M T, HUGHES M T, KUB J, et al. Development and validation of the Family Decision-Making Self-Efficacy Scale [J]. Palliat Support Care, 2009, 7(3): 315-21. PIGNATIELLO G A, IRANI E, TAHIR S, et al. A psychometric evaluation of the Family Decision-Making Self-Efficacy Scale among surrogate decision-makers of the critically ill [J]. Palliat Support Care, 2020, 18(5): 537-43. ABU-ELENIN M M, MOUNIR R M, SHEHATA W M. Effects of mindfulness and sleep quality on self-efficacy of clinical decision making among resident physicians: an observational study [J]. Postgrad Med J, 2025. LU S J, KU S C, LIU K F, et al. Decision Self-Efficacy and Decisional Conflict on Reintubation among Surrogates of Ventilated Patients Undergoing Planned Extubation [J]. Asian Nurs Res (Korean Soc Nurs Sci), 2023, 17(5): 235-44. LEE M K, BRYANT-LUKOSIUS D. Information Provision, Decision Self-efficacy, and Decisional Conflict in Adopting Health Behaviors Among Patients Treated for Colorectal Cancer: A Cross-sectional Study [J]. Cancer Nurs, 2023, 46(1): 45-56. VON ELM E, ALTMAN D G, EGGER M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies [J]. Lancet, 2007, 370(9596): 1453-7. FAUL F, ERDFELDER E, LANG A G, et al. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences [J]. Behav Res Methods, 2007, 39(2): 175-91. HICKMAN R L, JR., PIGNATIELLO G A, TAHIR S. Evaluation of the Decisional Fatigue Scale Among Surrogate Decision Makers of the Critically Ill [J]. West J Nurs Res, 2018, 40(2): 191-208. BUNN H, O'CONNOR A. Validation of client decision-making instruments in the context of psychiatry [J]. Can J Nurs Res, 1996, 28(3): 13-27. CONNOR K M, DAVIDSON J R. Development of a new resilience scale: the Connor-Davidson Resilience Scale (CD-RISC) [J]. Depress Anxiety, 2003, 18(2): 76-82. HOU T, ZHANG T, CAI W, et al. Social support and mental health among health care workers during Coronavirus Disease 2019 outbreak: A moderated mediation model [J]. PLoS One, 2020, 15(5): e0233831. PODSAKOFF P M, MACKENZIE S B, LEE J Y, et al. Common method biases in behavioral research: a critical review of the literature and recommended remedies [J]. J Appl Psychol, 2003, 88(5): 879-903. NEWMAN A B, MARTIN A R, HUGHES M E, et al. Patterns of presentation, treatment, and survival among older adults with metastatic breast cancer: Results from a large prospective registry [J]. J Geriatr Oncol, 2025, 16(5): 102261. PROENçA-PORTUGAL M, HELENO B, DIAS S, et al. General practitioners' perceptions on decision aids in healthcare: a qualitative study in Portugal [J]. BMC Med Inform Decis Mak, 2025, 25(1): 202. SøRENSEN H L, SCHJøLBERG T K, SMåSTUEN M C, et al. Social support in early-stage breast cancer patients with fatigue [J]. BMC Womens Health, 2020, 20(1): 243. VEENSTRA C M, WALLNER L P, ABRAHAMSE P H, et al. Understanding the engagement of key decision support persons in patient decision making around breast cancer treatment [J]. Cancer, 2019, 125(10): 1709-16. ABDOLLAHI A, ALSAIKHAN F, NIKOLENKO D A, et al. Self-care behaviors mediates the relationship between resilience and quality of life in breast cancer patients [J]. BMC Psychiatry, 2022, 22(1): 825. JIN Y, BHATTARAI M, KUO W C, et al. Relationship between resilience and self-care in people with chronic conditions: A systematic review and meta-analysis [J]. J Clin Nurs, 2023, 32(9-10): 2041-55. BASS S B, RUZEK S B, GORDON T F, et al. Relationship of Internet health information use with patient behavior and self-efficacy: experiences of newly diagnosed cancer patients who contact the National Cancer Institute's Cancer Information Service [J]. J Health Commun, 2006, 11(2): 219-36. QIN L L, PENG J, SHU M L, et al. The Fully Mediating Role of Psychological Resilience between Self-Efficacy and Mental Health: Evidence from the Study of College Students during the COVID-19 Pandemic [J]. Healthcare (Basel), 2023, 11(3). LIU Q, HE F, JIANG M, et al. [Longitudinal study on adolescents' psychological resilience and its impact factors in 5.12 earthquake-hit areas] [J]. Wei Sheng Yan Jiu, 2013, 42(6): 950-4, 9. Additional Declarations No competing interests reported. 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15:12:23","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98077,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7250769/v1/5c6443f0f2da5e9a21ababca.html"},{"id":93246241,"identity":"be5d4580-fdca-4b3a-ba0a-53bc1f3568fd","added_by":"auto","created_at":"2025-10-10 15:12:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95732,"visible":true,"origin":"","legend":"\u003cp\u003eSerial Mediation Model of Social Support on Decision Fatigue Through Psychological Resilience and Decision Self-Efficacy\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7250769/v1/e1fca70626c6896ec59a0f2c.png"},{"id":96245249,"identity":"8b594565-ceff-4cbf-8f0a-c2b5f5f6ee4e","added_by":"auto","created_at":"2025-11-19 07:20:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1158471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7250769/v1/e835278a-5ea4-490a-8e5b-5f6be66e47fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social Support and Decision Fatigue in Breast Cancer Patients: A Chain Mediation Model of Psychological Resilience and Decision Self-Efficacy","fulltext":[{"header":"What is already known","content":"\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDecision fatigue is an understudied but critical issue in breast cancer patients, associated with impaired decision quality and psychological distress .\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSocial support demonstrates protective effects against decision-related stress in chronic illness populations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePsychological resilience and decision self-efficacy independently mitigate cognitive depletion in medical decision-making\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eWhat this paper adds\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFirst evidence confirming psychological resilience and decision self-efficacy as sequential mediators linking social support to reduced decision fatigue in breast cancer patients.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEmpirical validation of the Conservation of Resources Theory in explaining decision fatigue, demonstrating how external support (social support) transforms into internal resources (resilience \u0026rarr; self-efficacy).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePractical identification of modifiable targets (social support interventions, resilience training) for clinical decision-support systems.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eBreast cancer remains the most commonly diagnosed malignancy among women worldwide, with 2.3\u0026nbsp;million new cases reported in 2022\u003csup\u003e[1]\u003c/sup\u003e. Advances in medical technologies and treatment modalities have significantly improved prognosis; however, these developments have also introduced increasing complexity into the clinical decision-making processes faced by patients\u003csup\u003e[2]\u003c/sup\u003e. Throughout the disease trajectory, individuals are frequently required to make multifaceted treatment decisions, including choices about surgical procedures, chemotherapy and radiotherapy regimens, targeted and immunotherapy options, as well as long-term follow-up and survivorship planning\u003csup\u003e[3, 4]\u003c/sup\u003e. This sustained high-intensity decision-making load may deplete cognitive resources, ultimately leading to a specific psychological phenomenon known as decision fatigue .\u003c/p\u003e\u003cp\u003eDecision fatigue refers to a state of diminished cognitive capacity resulting from repeated decision-making, characterized by decision avoidance, impulsive choices, or reduced decision quality \u003csup\u003e[5]\u003c/sup\u003e. Existing research has primarily focused on healthcare providers (e.g., clinicians, nurses) \u003csup\u003e[6\u0026ndash;8]\u003c/sup\u003e, studies on patients are limited.However, the impact of decision fatigue on cancer patients warrants attention, as it may not only alter decision-making behaviors and compromise decision quality but also contribute to adverse psychological outcomes, including decision regret, heightened anxiety symptoms, and an increased risk of post-traumatic stress disorder\u003csup\u003e[3\u0026ndash;5]\u003c/sup\u003e.Given these potential consequences, a deeper understanding of the mechanisms underlying decision fatigue is critical for optimizing clinical decision-support systems and improving patient outcomes. Further research is needed to explore the prevalence, predictors, and interventions for decision fatigue in cancer patients to mitigate its negative effects.\u003c/p\u003e\u003cp\u003eAccording to the conceptual framework of decision fatigue proposed by Pignatiello et al. (2021)\u003csup\u003e[5]\u003c/sup\u003e, decision fatigue is influenced by three main dimensions: repeated decision-making, self-regulatory capacity, and contextual factors. Among these, social support is considered a key contextual factor, referring to both emotional and instrumental assistance derived from one\u0026rsquo;s social network\u003csup\u003e[9]\u003c/sup\u003e.Empirical evidence from the Ottawa Decision Support Framework indicates that adequate social support significantly improves decision-making behaviors \u003csup\u003e[10]\u003c/sup\u003e. Furthermore, clinical studies have demonstrated its stress-buffering effects in oncology populations \u003csup\u003e[11]\u003c/sup\u003e. Emerging research further supports these findings, revealing that higher levels of social support correlate with increased psychological resilience\u003csup\u003e[12]\u003c/sup\u003e, greater decision self-efficacy\u003csup\u003e[13]\u003c/sup\u003e and improved quality of life\u003csup\u003e[14]\u003c/sup\u003e. Despite these insights, the precise mechanisms through which social support influences decision fatigue remain underexplored, highlighting a critical gap in the current literature.\u003c/p\u003e\u003cp\u003eThe influence of social support on decision fatigue may operate through complex psychological mechanisms. According to the Conservation of Resources (COR) theory \u003csup\u003e[15]\u003c/sup\u003e, external resources must be internalized as psychological resources to exert their protective effects. Psychological resilience\u0026mdash;defined as an individual\u0026rsquo;s capacity to adapt and thrive in the face of adversity\u003csup\u003e[16]\u003c/sup\u003e\u0026mdash;may serve as a critical mediator in this process. A systematic review indicates that robust social support enhances resilience\u003csup\u003e[17]\u003c/sup\u003e, and individuals with higher resilience exhibit superior emotion regulation and more efficient cognitive recovery \u003csup\u003e[18]\u003c/sup\u003e. These adaptive capacities may mitigate the cognitive depletion characteristic of decision fatigue, which arises from prolonged decision-making demands.While prior research has established the association between resilience and Self-regulation \u003csup\u003e[19]\u003c/sup\u003e, its specific role in buffering decision fatigue within oncology populations remains underexplored.\u003c/p\u003e\u003cp\u003eIn parallel, decision self-efficacy\u0026mdash;defined as an individual's confidence in making sound and effective decisions\u003csup\u003e[20]\u003c/sup\u003e\u0026mdash;is also a crucial psychological factor influencing decision fatigue\u003csup\u003e[21]\u003c/sup\u003e. Patients with low self-efficacy often experience anxiety and uncertainty when confronted with treatment decisions, leading to delayed decision-making or excessive reliance on others, which increases cognitive burden and fatigue \u003csup\u003e[22]\u003c/sup\u003e. Current evidence indicates that social support can enhance self-efficacy, consequently improving psychological adaptation\u003csup\u003e[13]\u003c/sup\u003e. Furthermore, multiple empirical studies have demonstrated that decision self-efficacy effectively alleviates decision conflict in patients\u003csup\u003e[23, 24]\u003c/sup\u003e.However, while theoretical associations between social support, decision self-efficacy and decision fatigue have received partial empirical support, the mediating pathway remains insufficiently investigated in breast cancer populations, warranting further rigorous examination.\u003c/p\u003e\u003cp\u003eTaken together, existing evidence has demonstrated associations between social support, resilience, decision self-efficacy, and decision fatigue among patients with cancer and other chronic conditions. However, it remains unclear how these factors collectively influence decision fatigue in breast cancer patients. Moreover, the potential serial mediating roles of resilience and decision self-efficacy in the relationship between social support and decision fatigue have not been systematically investigated. Understanding these psychological mechanisms is critically important, as it could provide healthcare professionals with evidence-based strategies to reduce decision fatigue and improve decision-making outcomes during breast cancer treatment.\u003c/p\u003e\u003cp\u003eTherefore, based on the Conservation of Resources Theory and the existing literature, this study aimed to examine the psychosocial factors associated with decision fatigue and to elucidate the serial mediating roles of resilience and decision self-efficacy between social support and decision fatigue among breast cancer patients. Our theoretical hypotheses were as follows: social support, resilience and decision self-efficacy are significantly associated with decision fatigue (H1); resilience mediates the relationship between social support and decision fatigue (H2); decision self-efficacy mediates the relationship between social support and decision fatigue (H3); and resilience and decision self-efficacy have a serial mediating effect between social support and decision fatigue (H4).\u003c/p\u003e"},{"header":"1. Methods","content":"\u003cp\u003eThis cross-sectional study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology( STROBE) guidelines (see Appendix S1for observational research \u003csup\u003e[25]\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Setting and participants\u003c/h2\u003e\u003cp\u003e The study was conducted in the breast cancer ward of one Cancer Hospital, a tertiary care facility specializing in oncology services and serving as a regional referral center for cancer diagnosis and treatment in Hunan Province, China. The hospital operates under the provincial health administration and adheres to national standards for oncology care, with multidisciplinary teams managing patient treatment.\u003c/p\u003e\u003cp\u003eEligible participants were recruited via convenience sampling from hospitalized breast cancer patients between January and May 2025. Inclusion criteria were: (a) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (b) histopathologically confirmed breast cancer; (c) current inpatient status; (d) awareness of their cancer diagnosis; (e) being the primary decision-maker or playing a dominant role in treatment decisions; and (f) voluntary participation with signed informed consent. Exclusion criteria included: (a) severe psychiatric or cognitive disorders; (b) comorbid with other malignancies or severe physical illnesses; or (c) inability to comprehend the questionnaire after guidance.\u003c/p\u003e\u003cp\u003eA priori power analysis was performed using GPower 3.1 \u003csup\u003e[26]\u003c/sup\u003e. Based on a moderate effect size (f*\u0026sup2;=0.15), α\u0026thinsp;=\u0026thinsp;0.05 (two-tailed), 80% power, and 8 predictors (3 dimensions each for social support and resilience, plus 1 each for decision self-efficacy and decision fatigue), the minimum required sample size was 128.Ultimately, 452 participants were enrolled.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Survey Procedure\u003c/h2\u003e\u003cp\u003eBefore data collection, the study objectives were explained to potential participants. The protocol was approved by the Medical Ethics Review Committee of Hunan Cancer Hospital. Surveys were administered via paper-based questionnaires. After obtaining informed consent, trained researchers provided standardized instructions to participants, emphasizing anonymity and confidentiality. Participants could withdraw at any time without penalty.\u003c/p\u003e\u003cp\u003eQuestionnaires were distributed in the ward during designated periods. Research assistants collected completed surveys and monitored participation rates. To minimize missing data, clarifications were offered if participants had questions. All responses were de-identified prior to analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Measures\u003c/h2\u003e\u003cp\u003eThe survey instruments used in this study were validated Chinese versions of internationally established scales. Measurement tools were selected based on a systematic review of breast cancer patient decision-making literature.\u003c/p\u003e\u003cp\u003eDecision fatigue and related factors\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1. General characteristics questionnaire\u003c/h2\u003e\u003cp\u003eThe self-designed demographic questionnaire included: (a) sociodemographic data (gender, age, marital status, education level, occupation, residence, household income, payment method); (b) clinical characteristics (pathological stage, disease duration, hospitalization frequency, treatment modality, and decision-making frequency). All items were developed through comprehensive literature review of factors influencing decision fatigue in breast cancer patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2. Decision Fatigue Scale (DFS)\u003c/h2\u003e\u003cp\u003eDecision fatigue was measured using the Chinese version of DFS originally developed by Hickman et al. (2018) \u003csup\u003e[27]\u003c/sup\u003eand cross-culturally adapted by Pan et al. This unidimensional 9-item instrument uses a 4-point Likert scale (0 = \"strongly disagree\" to 3 = \"strongly agree\"), with total scores ranging 0\u0026ndash;27. Higher scores indicate greater decision fatigue. The original scale demonstrated excellent reliability (Cronbach's α\u0026thinsp;=\u0026thinsp;0.87, test-retest reliability\u0026thinsp;=\u0026thinsp;0.90), while the Chinese version showed α\u0026thinsp;=\u0026thinsp;0.854 and test-retest reliability\u0026thinsp;=\u0026thinsp;0.863. In our study, the Cronbach's α was 0.899.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3. Decision Self-Efficacy Scale (DSES)\u003c/h2\u003e\u003cp\u003eThe Chinese version of DSES\u003csup\u003e[28]\u003c/sup\u003e (O'Connor, 1995; Wang's adaptation) assessed patients' confidence in medical decision-making. This 11-item unidimensional scale employs a 5-point Likert scale (0 = \"not at all confident\" to 4 = \"very confident\"), with total scores ranging 0\u0026ndash;44. Higher scores reflect greater decision self-efficacy. The Chinese version reported α\u0026thinsp;=\u0026thinsp;0.918. Our study obtained α\u0026thinsp;=\u0026thinsp;0.959.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4. Connor-Davidson Resilience Scale (CD-RISC)\u003c/h2\u003e\u003cp\u003ePsychological resilience was evaluated using the Chinese CD-RISC\u003csup\u003e[29]\u003c/sup\u003e, containing 25 items across three subscales: tenacity (13 items), strength (8 items), and optimism (4 items). Using a 5-point Likert scale (0 = \"never\" to 4 = \"always\"), total scores range 0-100 with clinical cutoffs: \u0026le;60 (poor), 61\u0026ndash;69 (moderate), 70\u0026ndash;79 (good), \u0026ge;\u0026thinsp;80 (excellent). The Chinese version reported α\u0026thinsp;=\u0026thinsp;0.933. Our study showed α\u0026thinsp;=\u0026thinsp;0.957.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.5. Social Support Rating Scale (SSRS)\u003c/h2\u003e\u003cp\u003eThe SSRS \u003csup\u003e[30]\u003c/sup\u003ewas used to measure the level of social support an individual received.It measured three dimensions: subjective support (4 items), objective support (3 items), and support utilization (3 items). Items 1\u0026ndash;4 and 8\u0026ndash;10 employed a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree to 4\u0026thinsp;=\u0026thinsp;strongly agree), while items 6\u0026ndash;7 used binary scoring (0\u0026thinsp;=\u0026thinsp;no support, 1\u0026thinsp;=\u0026thinsp;support available per source). Total scores ranged from 0 to 66, with higher scores indicating greater social support. The scale demonstrated excellent reliability in the original study (Cronbach's α\u0026thinsp;=\u0026thinsp;0.920) and acceptable internal consistency in our study (α\u0026thinsp;=\u0026thinsp;0.795).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using IBM SPSS 26.0 for descriptive and correlational analyses and AMOS 26.0 for structural equation modeling (SEM). Descriptive statistics were used to describe participants' characteristics and study variables, with continuous variables presented as medians (M) and interquartile ranges (P25, P75) due to non-normal distributions, and categorical variables as frequencies and percentages (%). Spearman\u0026rsquo;s correlation analysis was used to examine bivariate relationships among variables. To assess potential common method bias, Harman\u0026rsquo;s single-factor test was conducted, with the criterion that the first unrotated factor should explain\u0026thinsp;\u0026lt;\u0026thinsp;40% of the total variance\u003csup\u003e[31]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe serial mediating effects of resilience (mediator 1) and decision self-efficacy (mediator 2) between social support and decision fatigue were analyzed using a bias-corrected bootstrapping analysis (5000 resamples) in AMOS. In the mediation model, the total effect referred to the effect of social support (independent variable) on decision fatigue (dependent variable), comprising a direct effect and indirect effects through resilience alone, decision self-efficacy alone, and the sequential pathway (social support \u0026rarr; resilience \u0026rarr; decision self-efficacy \u0026rarr; decision fatigue). Standardized regression coefficients (β) and 95% confidence intervals (CIs) were reported for direct, indirect, and total effects. The mediating effect was considered statistically significant if the 95% CI of the indirect effect did not include zero. A significance level of .05 (two-sided) was used for all statistical tests.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Ethical considerations\u003c/h2\u003e\u003cp\u003eEthics approval was obtained from the Medical Ethics Review Committee of Hunan Cancer Hospital (reference number: 2025 Research Simplified Procedure Review [08]). All methods were carried out in accordance with the Declaration of Helsinki and relevant guidelines. Informed consent was obtained from all participants. As stated in the written consent form included in the questionnaire packet, consent was confirmed by participants\u0026rsquo; completion and return of the survey. Participants were informed of their right to refuse or withdraw from the study at any time without negative consequences.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"2. RESULT","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Participants characteristics\u003c/h2\u003e\u003cp\u003eAmong 480 eligible breast cancer patients, 452 (94.1%) participated, with non-participation primarily due to logistical constraints (time/inconvenience). The cohort comprised women aged 18\u0026ndash;\u0026gt;60 years (45\u0026ndash;60 years: 56.9%), predominantly married (96.9%), and residing in non-urban areas (79.4%). Occupational distribution showed 20.1% farmers, 16.6% unemployed, and 13.7% manual laborers. Education levels included 50.4% junior high school and 11.5% \u0026ge;bachelor's degree. Most households (66.9%) earned\u0026thinsp;\u0026lt;\u0026thinsp;5,000 CNY/month. Clinically, 84.1% had\u0026thinsp;\u0026lt;\u0026thinsp;1-year disease duration, with stages II/IV representing 70% of cases.Details on medical payment methods, decision frequency, and treatment modalities were also collected but are not the focus of the current analysis.Participants' characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic Characteristics of Breast Cancer Patients (n\u0026thinsp;=\u0026thinsp;452)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026thinsp;~\u0026thinsp;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e144 (31.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026thinsp;~\u0026thinsp;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e257 (56.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (11.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75 (16.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment employee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54 (11.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52 (11.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62 (13.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e91 (20.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83 (18.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35 (7.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace of Residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93 (20.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e194 (42.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e165 (36.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Monthly Income (CNY)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e202 (44.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u0026ndash;4999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e101 (22.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5000\u0026ndash;9999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120 (26.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10000\u0026ndash;19999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29 (6.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (1.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e438 (96.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced/Widowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (2.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74 (16.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e228 (50.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school/Technical school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (11.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47 (10.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u0026rsquo;s degree or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52 (11.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Payment Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployee medical insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55 (12.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban Resident Basic Medical Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e127 (28.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew Rural Cooperative Medical Scheme\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e206 (45.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (14.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease Duration (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e380 (84.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5 (1.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35 (7.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32 (7.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42 (9.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e223 (49.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85 (18.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92 (20.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUncertain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (2.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment Modalities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80 (17.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e167 (36.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100 (22.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105 (23.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Descriptive statistics of variables\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics (median and interquartile range) of key study variables among 452 breast cancer patients. Median scores were as follows: decision self-efficacy\u0026thinsp;=\u0026thinsp;31.00, social support\u0026thinsp;=\u0026thinsp;43.00, decision fatigue\u0026thinsp;=\u0026thinsp;16.00, and psychological resilience\u0026thinsp;=\u0026thinsp;63.00. The subdimensions of social support and resilience further reflect their multidimensional constructs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eScores of various scales in breast cancers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian (P25, P75)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecision Self-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.00 (22.00, 37.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.00 (37.00, 46.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObjective Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.00 (8.00, 11.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubjective Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.00 (22.00, 28.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSupport Utilization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.00 (6.00, 9.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecision Fatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.00 (14.00, 21.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological Resilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.00 (49.00, 73.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTenacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.00 (24.00, 38.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrength\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.00 (17.00, 25.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOptimism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.00 (7.00, 11.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Correlation analysis between variables\u003c/h2\u003e\u003cp\u003eSpearman\u0026rsquo;s rank-order correlation analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed statistically significant associations between variables (all p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Decision self-efficacy was strongly correlated with psychological resilience (ρ\u0026thinsp;=\u0026thinsp;0.641) and moderately with social support (ρ\u0026thinsp;=\u0026thinsp;0.349). Psychological resilience and social support were both negatively correlated with decision fatigue (ρ = -0.533 and ρ = -0.280, respectively). Additionally, decision self-efficacy showed a moderate negative correlation with decision fatigue (ρ = -0.511). These correlations provide empirical justification for testing a serial mediation model in which social support influences decision fatigue through psychological resilience and decision self-efficacy.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBivariate Correlations Among Study Variables (N\u0026thinsp;=\u0026thinsp;452)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Decision self-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Social support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.349**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Resilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.641**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.347**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. Decision fatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.511**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.280**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.533**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: **p\u0026thinsp;\u0026lt;\u0026thinsp;.001. N\u0026thinsp;=\u0026thinsp;452.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Common Method Variance Assessment\u003c/h2\u003e\u003cp\u003eTo mitigate potential common method bias arising from self-reported data, several procedural remedies were implemented during data collection, including emphasizing the anonymity and confidentiality of the questionnaires to breast cancer patients, as well as clarifying that the data would be used solely for scientific research purposes. Furthermore, Harman's single-factor test was conducted to assess common method variance, revealing 12 factors with eigenvalues greater than 1. The first factor accounted for 33.156% of the total variance, which falls below the critical threshold of 40%, indicating that\u003c/p\u003e\u003cp\u003ecommon method bias did not substantially affect the study findings\u003csup\u003e[31]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Serial Mediation Analysis\u003c/h2\u003e\u003cp\u003eA chain mediation model was constructed using AMOS 26.0. In this model, social support was specified as the independent variable, psychological resilience and decision self-efficacy served as sequential mediators, and decision fatigue was the dependent variable. The hypothesized model is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Maximum likelihood estimation was used for model fitting.\u003c/p\u003e\u003cp\u003eModel fit indices indicated acceptable to good fit(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.926, GFI\u0026thinsp;=\u0026thinsp;0.966, AGFI\u0026thinsp;=\u0026thinsp;0.923, CFI\u0026thinsp;=\u0026thinsp;0.971, TLI\u0026thinsp;=\u0026thinsp;0.949, NFI\u0026thinsp;=\u0026thinsp;0.962, RMSEA\u0026thinsp;=\u0026thinsp;0.081 (90% CI: 0.060\u0026ndash;0.102), and AIC\u0026thinsp;=\u0026thinsp;102.816. Although RMSEA slightly exceeded the ideal threshold (\u0026lt;\u0026thinsp;0.08), all other indices met or exceeded conventional criteria (i.e., \u0026gt;0.90), indicating that the proposed serial mediation model fits the data adequately.\u003c/p\u003e\u003cp\u003eχ\u0026sup2;/df: Chi-square to degrees of freedom ratio.\u003c/p\u003e\u003cp\u003eGFI/AGFI: Goodness-of-Fit Index/Adjusted Goodness-of-Fit Index.\u003c/p\u003e\u003cp\u003eRMSEA: Root Mean Square Error of Approximation (90% confidence interval).\u003c/p\u003e\u003cp\u003eCFI/NFI/TLI: Comparative Fit Index, Normed Fit Index, Tucker\u0026ndash;Lewis Index.\u003c/p\u003e\u003cp\u003eRMR: Root Mean Square Residual (standardized values preferred; absolute RMR\u0026thinsp;\u0026gt;\u0026thinsp;0.08 may indicate poor fit).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Path Analysis Results of the Structural Equation Model (N\u0026thinsp;=\u0026thinsp;452)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFit Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecommended Threshold\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eχ\u0026sup2;/df\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5 (acceptable), \u0026lt; 3 (good)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSEA (90% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.081 [0.060\u0026ndash;0.102]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.08 (good), \u0026lt; 0.10 (acceptable)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower\u0026thinsp;=\u0026thinsp;better\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cp\u003eNotes.\u003c/p\u003e\n\u003cp\u003e\u0026chi;\u0026sup2;/df: Chi-square to degrees of freedom ratio.\u003c/p\u003e\n\u003cp\u003eGFI/AGFI: Goodness-of-Fit Index/Adjusted Goodness-of-Fit Index.\u003c/p\u003e\n\u003cp\u003eRMSEA: Root Mean Square Error of Approximation (90% confidence interval).\u003c/p\u003e\n\u003cp\u003eCFI/NFI/TLI: Comparative Fit Index, Normed Fit Index, Tucker\u0026ndash;Lewis Index.\u003c/p\u003e\n\u003cp\u003eRMR: Root Mean Square Residual (standardized values preferred; absolute RMR \u0026gt; 0.08 may indicate poor fit).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, social support positively predicted psychological resilience (β\u0026thinsp;=\u0026thinsp;0.503, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and decision self-efficacy (β\u0026thinsp;=\u0026thinsp;0.221, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), but did not have a significant direct effect on decision fatigue (β = -0.072, p\u0026thinsp;=\u0026thinsp;.237). Psychological resilience significantly predicted both decision self-efficacy (β\u0026thinsp;=\u0026thinsp;0.536, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and negatively predicted decision fatigue (β = -0.372, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Decision self-efficacy also negatively predicted decision fatigue (β = -0.210, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). These results support a serial mediation mechanism where social support reduces decision fatigue via increased resilience and improved self-efficacy.\u003c/p\u003e\u003cp\u003eTable 5\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePath Analysis Results of the Structural Equation Model (N=452)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial Support \u0026rarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ePsychological Resilience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.503***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e6.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e[0.397, 0.610]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eDecision Self-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.221***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e[0.109, 0.332]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eDecision Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e[-0.214, 0.061]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePsychological Resilience \u0026rarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eDecision Self-efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.536***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e[0.429, 0.631]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eDecision Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.372***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-6.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e[-0.510, -0.235]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecision Self-efficacy \u0026rarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eDecision Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.210***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-3.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e[-0.341, -0.081]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes.\u003c/p\u003e\n\u003cp\u003e***p \u0026lt; .001; **p \u0026lt; .01; *p \u0026lt; .05 (two-tailed).\u003c/p\u003e\n\u003cp\u003e\u0026beta; = Standardized path coefficient; SE = Standard Error; CR = Critical Ratio (z-score); CI = Confidence Interval.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the standardized regression coefficients for the direct, indirect, and total effects of social support on decision fatigue through resilience and decision self-efficacy. The mediation model analysis revealed that the total standardized effect of social support on decision fatigue was \u0026minus;\u0026thinsp;0.363 (95% CI: -0.473 to -0.240). Within this total effect, the direct effect was \u0026minus;\u0026thinsp;0.072 (95% CI: -0.214 to 0.061, p\u0026thinsp;=\u0026thinsp;0.294), accounting for 19.83% of the total effect, while the combined indirect effects were \u0026minus;\u0026thinsp;0.291 (95% CI: -0.381 to -0.218), representing 80.17% of the total effect. Specifically, the independent mediating effect through resilience was \u0026minus;\u0026thinsp;0.187 (95% CI: -0.276 to -0.119), contributing 51.52% of the total effect; the independent mediating effect through decision self-efficacy was \u0026minus;\u0026thinsp;0.047 (95% CI: -0.090 to -0.019), accounting for 12.95%; and the chain mediating effect through both resilience and decision self-efficacy was \u0026minus;\u0026thinsp;0.057 (95% CI: -0.107 to -0.022), contributing 15.70%. All indirect effects were statistically significant (ps\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation Analysis Results Using Bootstrapping (N\u0026thinsp;=\u0026thinsp;452)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffect Pathway\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBoot SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% BCa CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEffect Proportion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Indirect Effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Support \u0026rarr; Resilience \u0026rarr; Decision Fatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e[-0.276, -0.119]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.52%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Support \u0026rarr;Decision Self-efficacy \u0026rarr; Decision Fatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e[-0.090, -0.019]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.95%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Support \u0026rarr; Resilience \u0026rarr;Decision Self-efficacy \u0026rarr; Decision Fatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e[-0.107, -0.022]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Indirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e[-0.381, -0.218]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.17%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2. Direct Effect\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Support \u0026rarr; Decision Fatigue (Direct)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e[-0.214, 0.061]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.83%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3. Total Effect\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e[-0.473, -0.240]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cp\u003eNotes.\u003c/p\u003e\n\u003cp\u003e\u0026beta; = Standardized indirect/direct effect; Boot SE = Bootstrap standard error; BCa CI = Bias-corrected and accelerated confidence interval (5,000 resamples).\u003c/p\u003e\n\u003cp\u003eEffect proportions calculated as |indirect/total effect|\u0026times;100%.\u003c/p\u003e\n\u003cp\u003eBolded pathways indicate statistical significance (p \u0026lt; .05).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThis study provides groundbreaking insights by systematically verifying the serial mediation mechanism through which social support influences decision fatigue in breast cancer patients via psychological resilience and decision self-efficacy. To our knowledge, this is the first empirical investigation that clarifies this pathway, thereby extending current theoretical frameworks on cancer-related decision-making processes. Our findings offer novel empirical support and practical implications for understanding the psychological mechanisms underlying decision fatigue. The results fully support all four of our proposed hypotheses (H1\u0026ndash;H4) and further confirm the applicability of Conservation of Resources (COR) theory \u003csup\u003e[15]\u003c/sup\u003e in this context.\u003c/p\u003e\u003cp\u003eThis study demonstrates that breast cancer patients experience a moderate level of decision fatigue, consistent with previous literature. Given the complexity of breast cancer treatment regimens, patients are often required to make multiple critical medical decisions within a limited timeframe \u003csup\u003e[4]\u003c/sup\u003e, which may exacerbate their decision fatigue. The sample in this study primarily consisted of individuals from vulnerable socioeconomic backgrounds, exhibiting the \u0026ldquo;three-lows\u0026rdquo; pattern: low educational attainment (only 11.5% held a bachelor's degree or higher), low household income (66.9% reported monthly income\u0026thinsp;\u0026lt;\u0026thinsp;RMB 5000), and low urbanization (a high proportion with rural residency). Additionally, 84.1% of participants were within the first year post-diagnosis, and nearly 70% were diagnosed at stage II or IV, indicating high decision-making burden. These characteristics align with the high-risk profiles previously identified in the literature \u003csup\u003e[32]\u003c/sup\u003e, underscoring the urgent need for tailored decision support interventions for patients with low socioeconomic status.\u003c/p\u003e\u003cp\u003eWe recommend the development of customized decision aids for disadvantaged populations in clinical practice\u003csup\u003e[33]\u003c/sup\u003e, such as low-cognitive-load decision booklets, extended consultation time policies, staged decision guidance, and community-based health education programs. Future research should also investigate the influence of sociodemographic factors on the formation of decision fatigue and explore the optimization of decision support systems for rural breast cancer patients under the tiered healthcare model.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.1 The Effect of Social Support on Decision Fatigue\u003c/h2\u003e\u003cp\u003eWe further revealed a significant indirect-only relationship between social support and decision fatigue, with the direct path being non-significant (β = -0.072, p\u0026thinsp;=\u0026thinsp;0.294), and the total indirect effect accounting for 80.17% of the total effect (β = -0.291). This finding supports Hypothesis 1 and validates a central tenet of COR theory: external resources must be internalized to alleviate psychological exhaustion effectively \u003csup\u003e[15]\u003c/sup\u003e. Social support not only offers emotional, informational, and instrumental resources but also fosters confidence in disease knowledge and self-management \u003csup\u003e[34]\u003c/sup\u003e.In the context of breast cancer, family members, as primary caregivers and decision partners, play a crucial supportive role\u003csup\u003e[35]\u003c/sup\u003e. Effective integration of family, healthcare, and community resources can alleviate both psychological and financial stress for patients and surrogate decision-makers, thereby enhancing engagement and decisional congruence.\u003c/p\u003e\u003cp\u003eMoreover, shared decision-making (SDM) has been recognized as a core strategy for improving decision quality and reducing fatigue\u003csup\u003e[2]\u003c/sup\u003e. Clinical nursing should enhance the assessment and intervention of patients\u0026rsquo; social support and promote a hospital-community-family collaborative care model, enabling better resource integration and information flow. For breast cancer, context-specific decision aids should be developed to improve decision preparedness and participation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Serial Mediation by Psychological Resilience and decision self-efficacy\u003c/h2\u003e\u003cp\u003eThe results supported Hypothesis 2 by identifying a significant mediating role of psychological resilience, which independently accounted for 51.52% of the total effect. This suggests that social support alleviates decision fatigue by enhancing resilience, a key internal resource. Individuals with high resilience demonstrate better emotional regulation, adaptability, and resource management, thus coping more effectively with complex medical decisions\u003csup\u003e[14]\u003c/sup\u003e. Previous studies have confirmed the mediating role of resilience between social support and self-care ability in breast cancer patients \u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImportantly, this study is the first to empirically confirm the protective role of resilience against decision fatigue in breast cancer patients, offering theoretical guidance for resilience-based interventions. Resilient patients are more likely to adopt proactive coping strategies and conserve self-control resources, thereby reducing mental burden\u003csup\u003e[36, 37]\u003c/sup\u003e. Given its plasticity, we recommend integrating standardized programs such as Mindfulness-Based Stress Reduction (MBSR) and implementing stratified interventions tailored to baseline resilience levels.\u003c/p\u003e\u003cp\u003eIn line with Hypothesis 3, decision self-efficacy also significantly mediated the relationship between social support and decision fatigue, accounting for 12.95% of the total effect. Positive support from social networks may boost confidence, perceived control, and decision competence, helping patients handle complex medical choices more effectively and reducing negative emotional responses such as anxiety and confusion \u003csup\u003e[13]\u003c/sup\u003e. Patients with higher decision self-efficacy are more likely to actively seek health information, engage in shared treatment decision-making, pose questions to physicians, and share concerns with oncology nurses, thereby mitigating decision fatigue\u003csup\u003e[38]\u003c/sup\u003e.This finding aligns with studies in critically ill \u003csup\u003e[23]\u003c/sup\u003e and Colorectal Cancer\u003csup\u003e[24]\u003c/sup\u003e, which have demonstrated the protective role of decision self-efficacy.\u003c/p\u003e\u003cp\u003eMost importantly, our findings validate Hypothesis 4: social support indirectly reduces decision fatigue by sequentially enhancing resilience and decision self-efficacy. This serial mediation pathway accounted for 15.7% of the total effect, revealing a comprehensive psychological mechanism:Social support \u0026rarr; Psychological resilience \u0026rarr; decision self-efficacy \u0026rarr; Decision fatigue\u003c/p\u003e\u003cp\u003eSpecifically, social support creates an enabling environment for developing resilience \u003csup\u003e[14]\u003c/sup\u003e, which in turn strengthens confidence in decision-making\u003csup\u003e[39]\u003c/sup\u003e. Decision self-efficacy, as a cognitive mediator, buffers the impact of complex treatment decisions by reducing helplessness, cognitive conflict, and information overload\u003csup\u003e[23, 24]\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eThis model suggests that relieving decision fatigue requires not only sufficient external support but also systematic interventions to convert external resources into internal psychological mechanisms. Future interventions should focus on the \u0026ldquo;social support\u0026ndash;resilience\u0026ndash;decision self-efficacy\u0026rdquo; triadic mechanism, with the development of multi-level, context-adaptive support tools such as resilience training programs, decision-making simulations, and family-inclusive communication strategies, to improve preparedness and active patient participation in healthcare decisions.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Limitations","content":"\u003cp\u003eThis study has several limitations. First, the cross-sectional design may constrain the interpretation of causal relationships between the associated factors and decision fatigue, as existing literature suggests that social support and resilience may mutually influence each other \u003csup\u003e[40]\u003c/sup\u003e, and reverse causality cannot be ruled out. Future research could employ a longitudinal design to more comprehensively elucidate these relationships over time. Second, participants were recruited from a single center in China using convenience sampling, which may limit the generalizability of the findings. Future studies involving multicenter samples could further validate the current results. Third, other internal and external factors\u0026mdash;such as the quality of doctor-patient communication\u0026mdash;might influence patients' decision fatigue, and subsequent research could explore additional potential contributing factors. Finally, data were collected via self-reported questionnaires, which may introduce social desirability or recall bias.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Implications to clinical practice\u003c/h2\u003e\u003cp\u003eBased on Hobfoll's Conservation of Resources theory framework, this study elucidates the intrinsic mechanisms linking social support, psychological resilience, decision self-efficacy, and decision fatigue in breast cancer patients, providing significant implications for clinical decision support. The findings suggest that an integrated intervention approach combining social support enhancement, resilience training, and decision self-efficacy improvement may effectively alleviate patients' decision fatigue symptoms.\u003c/p\u003e\u003cp\u003eIn clinical practice, we recommend healthcare teams to: (1) conduct comprehensive assessments of patients' social support networks and individualized needs, subsequently developing personalized support plans to optimize social resource utilization; (2) incorporate standardized resilience training programs (e.g., mindfulness-based stress reduction) into routine decision support protocols; and (3) implement evidence-based interventions such as decision-making skills training and scenario simulations to enhance patients' decision self-efficacy.\u003c/p\u003e\u003cp\u003eThis systematic intervention strategy, by simultaneously strengthening external support resources and internal psychological capital, can significantly improve the quality of patients' decision-making processes while mitigating the negative impacts of decision fatigue. The study provides empirical evidence supporting the development of comprehensive decision support systems for breast cancer patients facing complex treatment choices.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eOur study demonstrates that breast cancer patients experience moderate-to-high levels of decision fatigue. Grounded in Hobfoll\u0026rsquo;s Conservation of Resources (COR) theory, we found that social support, psychological resilience, and decision self-efficacy exert both direct and indirect effects on decision fatigue. Importantly, psychological resilience and decision self-efficacy serve as sequential mediators in the association between social support and decision fatigue, revealing a chain mediation pathway.Future psychosocial interventions aimed at improving decision fatigue should highlight the important roles of social support, resilience and decision self-efficacy..\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCONFLICT OF INTEREST STATEMENT\u003c/h2\u003e\u003cp\u003eNone.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING INFORMATION\u003c/h2\u003e\u003cp\u003eThis work was partly supported by the Scientific Research Project of Hunan Health Commission(Grant number: w20243281) and the Graduate Independent Exploration and Innovation Project of Central South University(Grant number: 2025ZZTS0985).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHui-Xia Wu: Conception, data acquisition, analysis, interpretation, manuscript drafting.Ying-Jie Yao: Conception, data acquisition, manuscript revision.Xiang-Yu Liu: Conception, methodology, data analysis/interpretation, manuscript revision.Yu-Jia Fan, Si-Jie Wu, Rui-Hong Zeng: Conception, data interpretation, manuscript revision.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e\u003cp\u003eWe are grateful to all patients for their participation in the survey.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData Availability StatementThe datasets generated and analyzed during this study are not publicly available due to [reason: e.g., patient privacy/ethical restrictions] but are available from the corresponding author on reasonable request. The de-identified data supporting the findings can be shared for research purposes after signing a data access agreement and approval from the Ethics Committee of Hunan Cancer Hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2024, 74(3): 229-63.\u003c/li\u003e\n\u003cli\u003eJOSFELD L, KEINKI C, PAMMER C, et al. Cancer patients\u0026apos; perspective on shared decision-making and decision aids in oncology [J]. J Cancer Res Clin Oncol, 2021, 147(6): 1725-32.\u003c/li\u003e\n\u003cli\u003eGRIGNOLI N, MANONI G, GIANINI J, et al. Clinical decision fatigue: a systematic and scoping review with meta-synthesis [J]. Fam Med Community Health, 2025, 13(1).\u003c/li\u003e\n\u003cli\u003eKANG S J, KIM B Y, AN H J. 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J Clin Nurs, 2023, 32(9-10): 2041-55.\u003c/li\u003e\n\u003cli\u003eBASS S B, RUZEK S B, GORDON T F, et al. Relationship of Internet health information use with patient behavior and self-efficacy: experiences of newly diagnosed cancer patients who contact the National Cancer Institute\u0026apos;s Cancer Information Service [J]. J Health Commun, 2006, 11(2): 219-36.\u003c/li\u003e\n\u003cli\u003eQIN L L, PENG J, SHU M L, et al. The Fully Mediating Role of Psychological Resilience between Self-Efficacy and Mental Health: Evidence from the Study of College Students during the COVID-19 Pandemic [J]. Healthcare (Basel), 2023, 11(3).\u003c/li\u003e\n\u003cli\u003eLIU Q, HE F, JIANG M, et al. [Longitudinal study on adolescents\u0026apos; psychological resilience and its impact factors in 5.12 earthquake-hit areas] [J]. Wei Sheng Yan Jiu, 2013, 42(6): 950-4, 9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer patients, Decision fatigue, Psychological resilience, Social support, Decision self-efficacy","lastPublishedDoi":"10.21203/rs.3.rs-7250769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7250769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eThis study aimed to examine the chain-mediating roles of psychological resilience and decision self-efficacy in the relationship between social support and decision fatigue among breast cancer patients, providing evidence for targeted interventions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA convenience sample of 452 breast cancer patients was recruited from one hospital in China.Data were collected from January to May 2025 using Self-report questionnaires,including the demographic and clinical characteristics, Decision Fatigue Scale (DFS),Decision Self-Efficacy Scale (DSES) ,Social Support Rating Scale (SSRS), andConnor-Davidson Resilience Scale (CD-RISC). Data were analyzed using IBM SPSS 26.0 for descriptive and correlational analyses and AMOS 26.0 for mediation analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDecision fatigue was negatively correlated with social support (ρ = -0.280*), decision self-efficacy (ρ = -0.511), and psychological resilience (ρ = -0.533). Both psychological resilience and decision self-efficacy independently mediated the relationship between social support and decision fatigue, with a significant sequential mediation effect. The chain-mediating pathway accounted for 36.7% of the total effect of social support on decision fatigue.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePsychological resilience and decision self-efficacy serve as sequential mediators between social support and decision fatigue, highlighting their critical roles in mitigating decision-related exhaustion. These findings underscore the importance of integrated psychosocial support in clinical management.\u003c/p\u003e","manuscriptTitle":"Social Support and Decision Fatigue in Breast Cancer Patients: A Chain Mediation Model of Psychological Resilience and Decision Self-Efficacy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 15:12:19","doi":"10.21203/rs.3.rs-7250769/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b0ea975-5b66-449c-a095-91adaa4e52a6","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-15T19:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 15:12:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7250769","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7250769","identity":"rs-7250769","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}