Dual-Trajectory Analysis of Resourcefulness and Social Support in Middle- Aged and Young Stroke Patients

Dual-Trajectory Analysis of Resourcefulness and Social Support in Middle- Aged and Young Stroke Patients | 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 Dual-Trajectory Analysis of Resourcefulness and Social Support in Middle- Aged and Young Stroke Patients yali li, ruiqin zhang, lei huang, xiang li, shuang wang, siting chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8453595/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: With the rising incidence of stroke among younger populations and the high prevalence of post-stroke depression (PSD), this study examined dual trajectories of resourcefulness and social support in first-ever stroke patients aged 18–59 years. The aim was to clarify their dynamic interaction and inform precision strategies for PSD prevention. Methods: Using purposive sampling, 324 first-ever stroke patients hospitalized in three tertiary hospitals in Henan Province (November 2024–October 2025) were followed at four time points: during the stable hospitalization phase and at 1-, 3-, and 6-months post-discharge. Group-based trajectory modeling (GBTM, Stata 18) was used to identify distinct trajectories of resourcefulness and social support. Conditional and joint probability analyses, together with multinomial logistic regression, were performed to examine the associations between trajectory groups and influencing factors. Results: Four resourcefulness trajectories were identified: persistently low (31.4%), moderate but declining (30.3%), high but fluctuating recovery (22.1%), and consistently high (16.2%). Social support also demonstrated three trajectories: moderate–low stability (18.1%), moderate–high rapid decline (49.8%), and high fluctuating recovery (32.1%). Strong coupling existed between the two: 58.7% of individuals with persistently low resourcefulness were also in the rapidly declining support group, while 79.6% of those with consistently high resourcefulness belonged to the high-support recovery group. Older age (≥46 years), self-payment, impaired BADL, low income, and low education increased the risk of low-trajectory membership. Conversely, having a college degree or above increased the likelihood of being in the highest trajectories of both resourcefulness and social support by 8.4 and 11.1 times, respectively, compared with primary education. Conclusion: Resourcefulness and social support in middle-aged and young stroke patients demonstrate heterogeneous and dynamic development with clear reciprocal reinforcement. Single-point assessments are insufficient. Stage-specific interventions tailored to trajectory characteristics should be prioritized, especially for “dual-low” individuals, to disrupt negative spirals, lower PSD risk, and improve recovery outcomes. Stroke Rehabilitation Resourcefulness༛Social support༛Dual-trajectory modeling༛Precision intervention Figures Figure 1 Figure 2 Figure 11 Figure 12 1. Introduction Stroke has become one of the leading causes of death and disability worldwide, with a noticeable trend toward affecting younger individuals in recent years¹. In China, 31% of stroke patients are middle-aged and young adults, and this proportion continues to rise². More critically, approximately one-third of survivors develop post-stroke depression (PSD) after discharge, which severely impedes their recovery and quality of life and significantly increases mortality and suicide risk³. Therefore, identifying and intervening in the key psychosocial mechanisms underlying PSD has become a central focus in contemporary stroke rehabilitation nursing research. Among various psychosocial resources, resourcefulness and social support are widely recognized as crucial factors influencing patients’ psychological adjustment and rehabilitation outcomes. Resourcefulness, as an essential internal coping resource, encompasses both the cognitive and behavioral abilities required to accomplish tasks independently (personal resourcefulness) and the ability to actively seek external assistance when independent completion is not feasible (social resourcefulness). Prior studies have demonstrated its effectiveness in buffering the transformation of stress into depressive symptoms⁴,⁵. Social support, by providing emotional comfort, informational guidance, and material assistance, can significantly enhance patients’ psychological resilience and confidence in recovery⁶. Existing research has consistently identified a strong positive correlation between resourcefulness and social support⁷. Enhancing resourcefulness may help individuals utilize social support more effectively, while high-quality social support can further strengthen resourcefulness through emotional reinforcement and information exchange, forming a positive feedback loop that mitigates stress responses⁸,⁹. For instance, a cross-sectional study by Chai et al.¹⁰ revealed that diverse social networks improved self-rated health by enhancing resilience, leading to higher resourcefulness levels and attenuating the negative effects of high stress. Similarly, Seok and Lee¹¹ reported that greater caregiver preparedness and competence contributed to improvements in patients’ psychological resilience and health-related quality of life, suggesting that a mutually reinforcing cycle of resourcefulness and support can effectively alleviate disease-related stress. However, most existing studies, based on cross-sectional or conventional longitudinal designs, have struggled to capture the bidirectional and dynamic relationship between these two constructs over time. Research investigating their synergistic temporal evolution remains limited, offering insufficient understanding of the co-developmental pathways of resourcefulness and social support¹²,¹³. In contrast, Group-Based Trajectory Modeling (GBTM) employs a semiparametric mixture model to classify heterogeneous populations into a finite number of latent subgroups, each with distinct developmental trajectories¹⁴,¹⁵. By assigning individuals to the most probable group based on maximum posterior probability, GBTM enables the identification of nonlinear temporal patterns and the exploration of bidirectional interaction mechanisms. Additionally, conditional and joint probability analyses allow visualization of overlapping and transitional relationships between trajectories, providing quantitative evidence for determining optimal intervention timing and targets¹⁶. Therefore, this study aimed to construct a dual-trajectory model to capture the heterogeneous trajectories of resourcefulness and social support among middle-aged and young stroke patients. This approach offers a new paradigm and evidence-based support for understanding their dynamic, bidirectional causal mechanisms, facilitating the identification of high-risk individuals with “dual deficits,” and informing the development of precise and tiered intervention strategies. Specifically, our aims were to: (1) identify and validate the patterns and quantities of both resourcefulness and social support trajectories; (2) explore the bidirectional associations and conversion probabilities between these trajectories; (3) examine shared risk factors influencing trajectory membership; and (4) provide theoretical foundations and practical targets for interrupting negative spirals and reducing the incidence of PSD. 2. Method 2.1 Participants and Procedures Using purposive sampling, a 6-month longitudinal follow-up study was conducted from November 2024 to October 2025 among 324 middle-aged and young stroke patients hospitalized in the Department of Neurology at a Grade III Class A hospital in Xinxiang City. The inclusion criteria were: ① aged 18–60 years; ② first-ever stroke confirmed by CT or MRI; ③ in the stable inpatient phase following the acute stage, having entered rehabilitation and free of severe organ diseases such as cardiac, pulmonary, or renal dysfunction; and ④ possessing intact communication abilities and voluntarily agreeing to participate. The exclusion criteria included: ① inability to cooperate due to critical condition or impaired consciousness; ② recurrent stroke or development of major illness during follow-up; and ③ voluntary withdrawal or participation in other research projects during the study period. According to previous studies¹⁷, the correlation coefficient between the two variables was 0.38. Following Monte Carlo simulation principles, a structural equation model was constructed using pwrSEM; with a sample size of 200 and 5000 iterations, a statistical power of 0.80 could be achieved. Considering a 25% attrition rate, a minimum of ≥ 267 participants was required. Ultimately, 324 valid participants were included, meeting the sample size requirements for trajectory identification and parameter estimation accuracy¹⁴. This study adheres to the principles of the Declaration of Helsinki and has been approved by the Medical Ethics Committee of Xinxiang Medical University (XYLL—20240341). 2.2 Measures 2.2.1 Research Instruments The General Information Questionnaire , designed by the research team, collected demographic and clinical details including age, gender, education level, occupation, marital status, number of chronic comorbidities, and caregiving situation. The Resourcefulness Scale (RS) was developed by Zauszniewski et al.¹⁸ and translated/adapted into Chinese by Wang Shumi et al.¹⁹. It has demonstrated good applicability in diverse populations, including those with chronic diseases. The scale contains 28 items comprising two dimensions: personal resourcefulness (16 items) and social resourcefulness (12 items), scored on a 6-point Likert scale. Total scores range from 0 to 140, with higher scores indicating stronger resourcefulness. The Cronbach’s α coefficient of the Chinese version is 0.85, and the correlation coefficient between dimensions is 0.41²⁰. The Social Support Rating Scale (SSRS) was developed by Xiao Shuiyuan²¹ and includes 10 items across three dimensions: objective support, subjective support, and the utilization of social support. Scores range from 12 to 66, with higher scores indicating better support levels. The Cronbach’s α coefficient ranges from 0.825 to 0.896. 2.2.2 Data Collection Previous studies²² - ²⁴ have shown that the three-month period following hospital discharge is critical for stroke progression. Considering expert recommendations and available human and material resources, follow-up assessments were conducted at four time points: during hospitalization (baseline, T0), one-month post-discharge (T1), three months post-discharge (T2), and six months post-discharge (T3) (± 1 week). T0 assessments were conducted in quiet hospital areas by the principal investigator and two trained researchers through face-to-face surveys. Before data collection, the purpose, significance, and instructions for the questionnaire were fully explained, and written informed consent was obtained. Contact information and home addresses were recorded for follow-up tracking. Participants completed the questionnaires independently when possible; for those with physical limitations, items were read aloud and answers recorded objectively by the researchers, without guiding responses. Completed questionnaires were checked immediately for completeness and accuracy, and any unclear items were clarified directly with the participant. Telephone or WeChat follow-ups were conducted at T1, T2, and T3 using designated stroke-center communication channels. Participants unreachable at ≥ 3 follow-ups were categorized as lost to follow-up, and those missing ≥ 2 follow-up points were considered dropouts. The Last Observation Carried Forward (LOCF) method²⁵ was used for missing data, which has demonstrated favorable performance in longitudinal stroke rehabilitation data with short follow-up intervals²⁶,²⁷ and aligns with the sensitivity analysis recommendations of Sterne et al.²⁸. 2.2.3 Data Analysis Data were double-entered using EpiData 3.1 and analyzed with Stata MP 18. First, separate single-trajectory models for resourcefulness and social support were fitted, beginning with 2–6 potential trajectory groups. Polynomial orders from cubic to zero were tested. The optimal model was selected based on the lowest Bayesian Information Criterion (BIC), average posterior probability (AvePP) ≥ 0.70, subgroup membership ≥ 5%, and clinical interpretability. The best-fitting parameters for each variable were then applied to the dual-trajectory model. Conditional and joint probabilities were calculated to assess the developmental relationship between the two variables. Categorical data were expressed as n (%), and normally distributed continuous data were reported as mean ± standard deviation. Multinomial logistic regression was used to identify factors associated with trajectory group membership. A two-tailed α of 0.05 was considered statistically significant. 3. Results From November 2024 to April 2025, 338 middle-aged and young stroke patients meeting the inclusion/exclusion criteria were initially enrolled. By October 2025, all follow-up assessments were completed. At T1, 1 participant refused follow-up and 3 experienced recurrences; at T2, 5 refused follow-up and 5 experienced recurrences; and at T3, 4 patients were lost to follow-up (unreachable or disconnected) and 4 withdrew from the study. The Last Observation Carried Forward method²⁶ was used to impute 8 missing values at T3, resulting in a total of 14 dropout cases. Ultimately, 324 patients successfully completed all follow-ups. The loss-to-follow-up rate at each point was 1.2% (T1) and 3.0% (T2), with an overall rate of 4.2%. The final participation rate reached 98.2%, and the questionnaire validity rate was 100% at all time points. General demographic and clinical characteristics are shown in Tables 2 and 3 . 3.1 Resourcefulness Trajectory As shown in Table 1 , with the increasing number of resourcefulness trajectory subgroups, the absolute values of BIC continuously decreased, with AvePP consistently ≥ 0.70. However, when the number exceeded four, one subgroup demonstrated a small sample size and a highly similar developmental pattern to adjacent types. Considering both interpretability and practical significance, a four-group model with trajectory orders of 3-3-3-3 was selected as the optimal solution. The trajectory patterns are illustrated in Fig. 1 . All groups showed slight improvement in resourcefulness during the first month after discharge, followed by divergence. Group 1 (31.4%) displayed minimal change and persistently low levels, defined as the Low Resourcefulness–Persistent Vulnerability Group. Group 2 (30.3%) showed moderate levels but a sustained decline with the steepest drop between T1 and T3, designated as the Moderate Resourcefulness–Rapid Decline Group. Group 3 (22.1%) increased from T0 to T1, declined at T2, but remained above T0 levels through T3, identified as the High Resourcefulness–Fluctuating Recovery Group. Group 4 (16.2%) maintained consistently high levels after an initial increase, labeled as the High Resourcefulness–Stable Excellence Group. Table 1 Parameters for resourcefulness Trajectory Fitting Number of Trajectories BIC AIC Entropy AvePP Group Member Ratio (%) 3 3 -4580.58 -4561.68 0.964 0.984, 0.993 36.58, 63.42 3 3 3 -4147.83 -4119.47 0.977 0.998, 0.984, 0.991 33.55, 48.21, 18.24 3 3 3 3 -3980.05 -3942.24 0.968 0.999, 0.976, 0.975, 0.985 31.40, 30.30, 22.10, 16.20 3 3 3 3 3 -3895.04 -3847.78 0.966 0.998, 0.917, 0.985, 0.950, 0.995 28.92, 8.09, 25.98, 20.88, 16.13 3 3 3 3 3 3 -3762.33 -3705.62 0.975 0.987, 0.991, 0.958, 0.977, 0.964, 0.998 7.63, 22.56, 7.53, 25.20, 20.90, 16.18 3.2 Logistic Regression for Resourcefulness Trajectory Groups Using the Low Resourcefulness–Persistent Vulnerability Group as the reference and controlling for age, gender, and educational level, multinomial logistic regression was performed. Given that this reference group remained at the lowest level across all time points, RRR > 1 in the remaining groups signifies protective effects facilitating upward resourcefulness development. The results are shown in Table 2 . Middle-aged patients (46–60 years) were less likely than younger patients to be classified into the High Resourcefulness–Fluctuating Recovery Group or the High Resourcefulness–Stable Excellence Group (RRR = 0.148 and 0.051, both P < 0.01). Educational attainment was a significant protective factor. Individuals with high school/vocational education or college/university degrees had significantly greater odds of entering higher resourcefulness groups than those with primary education or below. For example, the RRR for the college/university education category entering the High Resourcefulness–Stable Excellence Group was 8.422 (P = 0.001). Moreover, no family history of stroke, unimpaired BADL function, and higher income also increased the likelihood of belonging to higher resourcefulness trajectories, indicating the contributions of functional status and socioeconomic support to resourcefulness development. Table 2 Logistic Regression Results for Baseline Factors Influencing Resourcefulness Trajectories Variable* Examples n (%) moderate resourcefulness-rapidly declining group VS low resourcefulness-persistently weak group high resourcefulness-fluctuating recovery group VS low resourcefulness-persistently weak group high resourcefulness-stable excellence group VS low resourcefulness-persistently weak group RRR (95%CI) P RRR (95%CI) P RRR (95%CI) P Age (young adults18ཞ45) 78(24.07) middle-aged 46ཞ59岁 246(75.93) 0.271(0.772 ~ 0.952) 0.042 0.148(0.039 ~ 0.562) 0.005 0.051(0.012 ~ 0.216) 0.001 Gender (man) 196(60.49) woman 128(39.51) 1.222(0.638 ~ 2.341) 0.546 0.899(0.426 ~ 1.898) 0.780 1.250(0.531 ~ 2.944) 0.609 Education (primary and junior high) 84(25.39) High or vocational 165(50.93) 4.527(2.125 ~ 9.646) 0.001 4.623(1.851 ~ 11.544) 0.001 2.979(0.981 ~ 9.046) 0.054 College or higher 75(23.15) 4.749(1.705 ~ 13.226) 0.003 7.874(2.536 ~ 24.444) 0.001 8.422(2.308 ~ 30.732) 0.001 Spouse (none) 74(22.84) yes 250(77.16) 1.416(0.268 ~ 7.490) 0.682 0.796(0.106 ~ 5.969) 0.824 0.453(0.044 ~ 4.625) 0.504 Occupation (worker) 142(43.83) farmer 99(30.56) 0.809(0.248 ~ 2.638) 0.726 1.123(0.274 ~ 4.609) 0.872 1.418(0.276 ~ 7.295) 0.676 Self-employed/freelancer 83(25.62) 1.403(0.395 ~ 4.984) 0.600 3.560(0.858 ~ 14.771) 0.080 2.405(0.455 ~ 12.714) 0.302 Employment status (currently employed) 223(68.83) retired 101(31.17) 1.011(0.365 ~ 2.804) 0.983 1.103(0.345 ~ 3.526) 0.869 1.882(0.472 ~ 7.501) 0.370 Chronic diseases (1–2 types) 220(67.90) 3–4 types 104(32.10) 1.099(0.527 ~ 2.292) 0.801 1.673(0.756 ~ 3.699) 0.204 3.823(1.563 ~ 9.351) 0.003 Health insurance type (Employee medical insurance) 107(33.02) Resident medical insurance 173(53.40) 1.056(0.411 ~ 2.715) 0.909 0.547(0.186 ~ 1.611) 0.274 0.660(0.191 ~ 2.286) 0.512 Out-of-pocket 44(13.58) 0.378(0.107 ~ 1.339) 0.132 0.042(0.079 ~ 0.222) 0.000 0.088(0.015 ~ 0.523) 0.008 Income (low level: 3000~) 79(24.38) Medium level: 5000~ 162(50.00) 0.983(0.471 ~ 2.053) 0.963 1.354(0.566 ~ 3.237) 0.496 2.256(0.720 ~ 7.072) 0.163 High level: 7000~ 83(25.62) 2.099(0.848 ~ 5.195) 0.109 2.960(1.054 ~ 8.311) 0.039 3.614(1.009 ~ 12.943) 0.048 Family history (yes) 121(37.35) none 203(62.65) 0.766(0.382 ~ 1.536) 0.453 0.440(0.205 ~ 0.942) 0.035 0.165(0.067 ~ 0.407) 0.001 Living arrangements (spouse) 145(44.75) Spouse and children 90(27.78) 1.676(0.716 ~ 3.925) 0.234 2.350(0.915 ~ 6.038) 0.076 2.423(0.816 ~ 7.191) 0.111 other 89(27.47) 2.736(0.594 ~ 12.611) 0.197 1.512(0.230 ~ 9.933) 0.667 0.874(0.962 ~ 7.936) 0.905 Caregiver (spouse) 135(41.67) Children 106(32.72) 1.307(0.617 ~ 2.769) 0.484 1.399(0.586 ~ 3.342) 0.450 1.354(0.471 ~ 3.892) 0.574 Other 83(25.62) 0.409(0.144 ~ 1.163) 0.094 0.901(0.230 ~ 2.705) 0.852 0.613(0.170 ~ 2.211) 0.454 BADL (barrier) 85(26.23) Free barrier 239(73.77) 0.565(0.252 ~ 1.265) 0.165 0.725(0.296 ~ 1.776) 0.482 0.355(0.135 ~ 0.937) 0.036 Note: *Variable brackets indicate reference groups; BADL includes bathing, dressing, eating, toileting, grooming, and walking. No difficulty in any item indicates no disability; difficulty in any item indicates disability. 3.3 Social Support Trajectory As shown in Table 3 , increasing the number of subgroups yielded progressively smaller BIC absolute values, with AvePP ≥ 0.70. However, beyond three groups, the p-values became insignificant, making further subgrouping unjustified. Therefore, a three-group model with polynomial order 2-2-2 was identified as the optimal fit. Figure 2 shows the developmental trends. Overall, stroke patients exhibited relatively high social support after discharge (36.40 ± 7.43). All patients reported the highest support level at T0, followed by varying degrees of decline. Group 1 (18.1%) maintained low-to-moderate but stable levels, referred to as the Low-to-Moderate Social Support–Stable Group. Group 2 (49.8%) remained at moderate-to-high support but experienced continuous decline over six months, labeled the Moderate-to-High Social Support–Declining Group. Group 3 (32.1%) had high baseline levels, a slight decrease by T2, and a rapid rise by T3, maintaining overall high support, identified as the High Social Support–Gradual Recovery Group. Table 3 Fitting Parameters for Social Support Trajectories Number of Trajectories BIC AIC Entropy AvePP Group Member Ratio (%) 2 2 -4113.59 -4098.47 0.924 0.983, 0.975 64.59, 34.41 2 2 2 -3897.94 -3875.26 0.922 0.931, 0.967, 0.977 18.10, 49.80, 32.10 2 2 2 2 -3843.22 -3816.75 0.910 0.964, 0.895, 0.957, 0.970 6.13, 19.80, 42.83, 31.24 2 2 2 2 2 -3721.61 -3683.80 0.923 0.972, 0.912, 0.959, 0.956, 0.948 6.45, 19.28, 38.45, 22.99, 12.82 3.4 Logistic Regression for Social Support Trajectory Groups As shown in Table 4 . With the Low-to-Moderate Social Support–Stable Group as the reference and controlling for major covariates, multinomial logistic regression showed that middle-aged patients were more likely than younger patients to be categorized into the Moderate-to-High Social Support–Declining Group (RRR = 4.274, P = 0.036). Educational attainment remained a significant positive factor: individuals with high school/vocational diplomas, associate degrees, or higher education were far more likely to be assigned to the High Social Support–Gradual Recovery Group (RRR = 11.064, P = 0.001). Additionally, absence of family history, non-self-paying medical insurance, intact ADL function, and having caregivers other than spouses significantly increased the likelihood of entering higher-support trajectories, suggesting that social structural and functional resources jointly influence social support development. Table 4 Logistic Regression Results for Baseline Factors Influencing Social Support Variable* Examples n (%) Moderate-High Social Support - Rapid Decline Group VS Moderate-Low Social Support - Persistent Stability Group High Social Support - Fluctuating Recovery Group VS Moderate-Low Social Support - Persistent Stability Group RRR (95%CI) P RRR (95%CI) P Age (young adults18ཞ45) 78(24.07) middle-aged 46ཞ59 246(75.93) 4.274(1.102 ~ 16.577) 0.036 1.483(0.356 ~ 6.172) 0.588 Gender (man) 196(60.49) woman 128(39.51) 0.978(0.399 ~ 2.399) 0.962 1.281(0.493 ~ 3.329) 0.611 Education (primary and junior high) 84(25.39) High or vocational 165(50.93) 1.483(0.548 ~ 4.011) 0.438 4.525(1.397 ~ 14.661) 0.012 College or higher 75(23.15) 1.760(0.477 ~ 6.500) 0.396 11.064(2.616 ~ 46.797) 0.001 Spouse (none) 74(22.84) yes 250(77.16) 2.568(0.059 ~ 112.302) 0.625 2.006(0.044 ~ 92.298) 0.722 Occupation (worker) 142(43.83) farmer 99(30.56) 0.554(0.322 ~ 1.020) 0.494 0.530(0.085 ~ 3.320) 0.497 Self-employed/freelancer 83(25.62) 0.181(0.102 ~ 3.004) 0.053 0.189(0.030 ~ 1.212) 0.079 Employment status (currently employed) 223(68.83) retired 101(31.17) 0.017(0.218 ~ 4.60) 0.982 2.109(0.400 ~ 11.117) 0.379 Chronic diseases (1–2 types) 220(67.90) 3–4 types 104(32.10) 0.987(0.382 ~ 2.553) 0.979 0.893(0.323 ~ 2.472) 0.828 Health insurance type (Employee medical insurance) 107(33.02) Resident medical insurance 173(53.40) 1.669(0.484 ~ 5.755) 0.418 2.392(0.621 ~ 9.217) 0.205 Out-of-pocket 44(13.58) 0.093(0.021 ~ 0.409) 0.002 0.154(0.031 ~ 0.773) 0.023 Income (low level: 3000~) 79(24.38) Medium level: 5000~ 162(50.00) 1.984(0.748 ~ 5.260) 0.168 1.453(0.489 ~ 4.314) 0.501 High level: 7000~ 83(25.62) 1.122(0.367 ~ 3.428) 0.840 2.055(0.627 ~ 6.733) 0.234 Family history (yes) 121(37.35) none 203(62.65) 0.311(0.116 ~ 0.828) 0.019 0.265(0.093 ~ 0.758) 0.013 Living arrangements (spouse) 145(44.75) Spouse and children 90(27.78) 1.925(0.555 ~ 6.676) 0.302 2.755(0.744 ~ 10.202) 0.129 other 89(27.47) 4.707(0.108 ~ 205.434) 0.421 6.797(0.149 ~ 310.422) 0.326 Caregiver (spouse) 135(41.67) Children 106(32.72) 0.270(0.081 ~ 0.893) 0.032 0.186(0.052 ~ 0.669) 0.010 Other 83(25.62) 0.098(0.027 ~ 0.361) 0.001 0.029(0.007 ~ 0.124) 0.001 BADL (barrier) 85(26.23) Free barrier 239(73.77) 0.074(0.015 ~ 0.365) 0.001 0.034(0.007 ~ 0.171) 0.001 Note: *Variable brackets indicate reference groups; BADL includes bathing, dressing, eating, toileting, grooming, and walking. No difficulty in any item indicates no disability; difficulty in any item indicates disability. 3.5 Dual Trajectories of Resourcefulness and Social Support GBTM revealed notable associations between the resourcefulness and social support trajectory groups (Table 5 ), with each column totaling 100%. Among individuals in the Low Resourcefulness–Persistent Vulnerability Group, 33.2% belonged to the Low-to-Moderate Social Support–Stable Group and 58.7% to the Moderate-to-High Social Support–Declining Group (totaling 91.9%), while only 8.1% were classified into the High Social Support–Gradual Recovery Group. The proportion assigned to the Moderate-to-High Social Support–Declining Group decreased progressively across the four resourcefulness types (58.7%, 55.8%, 49.3%, 18.1%). Table 6 demonstrates that the Low Resourcefulness–Persistent Vulnerability Group accounted for the highest proportion in the Low-to-Moderate Social Support–Stable Group (57.5% > 38.0% > 8.0%). Meanwhile, the High Resourcefulness–Stable Excellence Group comprised 2.1%, 5.9%, and 39.6% of social support trajectories 1, 2, and 3, respectively. Table 5 Probability of Social Support Trajectory Groups Under Resourcefulness Trajectory Group Conditions (%) Social Support Trajectory Resourcefulness Trajectory low resourcefulness-persistently weak group moderate resourcefulness-rapidly declining group high resourcefulness-fluctuating recovery group high resourcefulness-stable excellence group Moderate-Low Social Support - Persistent Stability Group 33.2% 12.5% 16.7% 2.3% Moderate-High Social Support - Rapid Decline Group 58.7% 55.8% 49.3% 18.1% High Social Support - Fluctuating Recovery Group 8.1% 31.7% 34.0% 79.6% Table 6 Probability of the Resourcefulness Group under Social Support Trajectory Conditions (%) Resourcefulness Trajectory Social Support Trajectory Moderate-Low Social Support - Persistent Stability Group Moderate-High Social Support - Rapid Decline Group High Social Support - Fluctuating Recovery Group low resourcefulness-persistently weak group 57.5% 38.0% 8.0% moderate resourcefulness-rapidly declining group 20.5% 34.1% 29.4% high resourcefulness-fluctuating recovery group 19.9% 22.0% 23.0% high resourcefulness-stable excellence group 2.1% 5.9% 39.6% Table 7 shows joint probabilities, with total probability summing to 100%. Notably, 10.6% of patients were simultaneously in the Low Resourcefulness–Persistent Vulnerability and Low-to-Moderate Social Support–Stable Groups; 16.8% in the Moderate Resourcefulness–Rapid Decline and Moderate-to-High Social Support–Declining Groups; 7.4% in the High Resourcefulness–Fluctuating Recovery and High Social Support–Gradual Recovery Groups; and 12.8% in the High Resourcefulness–Stable Excellence and High Social Support–Gradual Recovery Groups. Table 7 Joint Probability of the Resourcefulness Trajectory Group and the Social Support Trajectory Group (%) Social Support Trajectory Resourcefulness Trajectory low resourcefulness-persistently weak group moderate resourcefulness-rapidly declining group high resourcefulness-fluctuating recovery group high resourcefulness-stable excellence group Moderate-Low Social Support - Persistent Stability Group 10.6% 3.8% 3.7% 0.4% Moderate-High Social Support - Rapid Decline Group 18.7% 16.8% 10.8% 2.9% High Social Support - Fluctuating Recovery Group 2.6% 9.5% 7.4% 12.8% 4. Discussion This study, based on a GBTM framework, is the first to simultaneously characterize the heterogeneous developmental trajectories of resourcefulness and social support among middle-aged and young stroke patients. The findings reveal multidimensional evolutionary patterns in both trajectories, demonstrating the complexity and individual variability in resource dynamics during rehabilitation. Consistent with Zauszniewski’s observations²⁹, resourcefulness among individuals with chronic illnesses undergoing the same intervention does not follow a single linear distribution but instead presents stratified trajectories. Ignoring such heterogeneity can dilute the effectiveness of interventions. Similarly, Tian³⁰ showed through longitudinal analysis that the co-evolution of “social support–psychological resources” is heterogeneous and differentiates over time. This suggests that resourcefulness and social support during post-stroke rehabilitation are not static but dynamic psychosocial resources with continuous transformations and mutual influence. Thus, trajectory characteristics can serve as crucial indicators for personalized intervention strategies. However, traditional approaches often rely solely on baseline assessments and overlook dynamic changes³¹. This study demonstrates that even patients with similar baseline resourcefulness levels may diverge significantly in recovery depending on their trajectory type. For example, the “Moderate Resourcefulness–Rapid Decline” and “High Resourcefulness–Fluctuating Recovery” types exhibited no significant differences at T0, yet by T3 the former dropped to low levels while the latter rebounded above baseline. This indicates that resilience and recovery patterns—not initial levels—are key determinants of long-term outcomes. Guo et al.³² also reported that fluctuating resourcefulness trajectories increase depression risk by 2.4 times compared with stable high levels, independent of baseline NIHSS and mRS scores, suggesting that trajectory grouping has higher predictive validity and clinical intervention value. Thus, trajectory-based screening enables early identification of patients at high risk for PSD and supports precise intervention matching and stratified PSD management. We identified substantial overlap and strong bidirectional associations between trajectory types of resourcefulness and social support. Among patients with persistently low resourcefulness, 58.7% belonged to the “Moderate-to-High Social Support–Marked Decline” trajectory. Meanwhile, 79.6% of those with consistently high resourcefulness belonged to the “High Social Support–Gradual Recovery” group. This alignment indicates that individuals with higher resourcefulness tend to maintain stronger support networks, whereas those with poorer resourcefulness experience rapid social support depletion. Consistent with Gui et al.³³, greater diversity of support sources strengthens coping abilities and delays cognitive decline. Latent class growth modeling research³⁴ further confirms that diverse social networks enhance psychological resilience, and high-resilience individuals maintain broader and more stable social connections, forming a mutually reinforcing loop of strong resourcefulness and high support. This dynamic association suggests that resourcefulness and social support may form either positive or negative feedback cycles throughout rehabilitation³⁵. By applying GBTM and conditional probability analysis, this study reveals temporal synergistic and compensatory mechanisms between internal psychological resources and external social resources. Based on the results, we categorized and proposed intervention strategies³⁶. For patients classified as “low resourcefulness + low support” (10.6%), combined intervention should be initiated before discharge, integrating standardized resourcefulness training³⁷ with digital follow-up platforms³⁸ to strengthen both self-management ability and support network utilization. For those with “high resourcefulness + low support” (e.g., in the High Resourcefulness–Fluctuating Recovery group), the focus should be on reinforcing support utilization through peer support groups and community rehabilitation resources³⁹ to prevent resource depletion. For “low resourcefulness + high support” individuals, cognitive restructuring and problem-solving training⁴⁰ are recommended to prevent excessive reliance on external support and maintain self-efficacy. Importantly, Sun et al.⁴¹ reported that social support needs are stage-specific and shift from emotional companionship to informational empowerment and eventually social reintegration. Therefore, interventions must adopt a “stage–need–training” closed-loop approach, dynamically adjusting content to improve efficiency of resource mobilization and reduce PSD risk. Logistic regression results show that the four-dimensional resource structure of “age–education–function–economy” is the central driving force influencing whether resourcefulness and social support evolve upward or downward. This parallel effect reflects compounded risk from overlapping individual and environmental vulnerabilities. Younger patients (18–44 years) were significantly more likely to enter high resourcefulness trajectories than middle-aged patients. Table 5 indicates that middle-aged patients had a 4.3-fold higher risk of developing the “moderate-to-high support–declining” trajectory, possibly due to heavier life responsibilities such as employment and caregiving⁴², which increase vulnerability to resource depletion despite initial adequate support. Therefore, future interventions should incorporate role demands and family life-cycle factors⁸. Education plays a decisive role in cognitive capital. Individuals with college-level education or above were 8–11 times more likely to enter the highest trajectories than those with primary-school education or below, independent of socioeconomic and functional status. Higher education enhances not only personal resourcefulness but also social resourcefulness and support utilization⁴³. Therefore, for patients with low education, visually intuitive, stepwise, and repeatedly accessible digital educational materials should be prioritized⁴⁴. Functional independence and household economic status form a dual threshold for resource maintenance. BADL impairment significantly reduces the likelihood of entering high trajectories (RRR ≈ 0.35; 0.03–0.07), suggesting functional ability is a prerequisite for upward psychosocial resource development. Once functional dependence increases, both self-efficacy and perceived support decline simultaneously⁴⁵, triggering a negative cycle. Furthermore, self-funded treatment and low income restrict access to high-quality rehabilitation resources⁴⁶. Prior to discharge, clinical teams should conduct dual functional-economic assessments⁴⁷ and implement low-cost hybrid rehabilitation models combining home-based services and digital support. Finally, caregiver characteristics exert significant influence. Compared to spousal caregivers, children or others more effectively integrate digital and community resources to build broader support networks⁴⁸, while spouse caregivers tend to rely solely on traditional supports. Thus, intervention programs should shift focus from caregiver identity to caregiver empowerment⁴⁹ by strengthening digital skills for spouse caregivers and emotional communication skills for children’s caregivers. In summary, this study pioneers the use of GBTM in stroke rehabilitation psychology to model the co-evolution of resourcefulness and social support. The findings not only overcome limitations of traditional static-correlation research but also provide empirical evidence that enriches and extends theoretical frameworks in psychology and sociology. However, certain limitations exist. First, this single-center sample may introduce regional bias; future multi-center studies are recommended to verify the stability and generalizability of the model. Second, although the LOCF method was appropriate for handling missing data, it may underestimate volatility; more advanced methods are needed in future studies. Finally, only two psychosocial variables were examined; incorporating additional psychological and social determinants would support development of a more comprehensive intervention framework. 5. Conclusion This study, utilizing a GBTM dual-trajectory model, is the first to simultaneously demonstrate that middle-aged and young stroke patients exhibit four heterogeneous dynamic trajectories of resourcefulness and three trajectories of social support, with significant bidirectional synergistic effects between the two. Age, educational attainment, functional independence, and economic status jointly act as core determinants driving the direction of trajectory evolution. Therefore, clinical interventions should move beyond traditional static assessment frameworks and instead establish a stratified precision management system aligned with the “stage–resource–need” paradigm. For individuals with dual disadvantages, standardized resourcefulness training combined with collaborative construction of digital social support networks should be implemented prior to discharge. For individuals with “high resourcefulness but low support,” interventions should focus on strengthening the use of available support resources; whereas for individuals with “low resourcefulness but high support,” efforts should emphasize enhancing personal resourcefulness and developing problem-solving capabilities. Throughout rehabilitation, intervention strategies must be dynamically adjusted to accommodate evolving needs. Such an approach can effectively disrupt negative cycles, promote positive resource interactions, reduce the incidence of post-stroke depression, and ultimately improve rehabilitation outcomes. Declarations Acknowledgments We sincerely thank all those who contributed to this study, the Henan Provincial Department of Education, the Foundation of Xinxiang Medical College, and all the participant administrators and participants in this study for their invaluable support and participation. Authors’ contributions S.J. and L.YL. conceptualized this study and designed the research protocol; Z.RQ. and H.L. performed the study and screened data for analysis; C. ST. and W.S. checked the data for accuracy; L.YL. and Z.YQ. performed the statistical analyses; S.J., L.YL. and L.X. prepared the outlines and wrote the manuscript. All authors contributed to the critical revision of manuscript drafts and they read and approved the final manuscript. Ethics approval and consent to participate. Consent for publication The order of authors listed in the manuscript has been approved by all of us. Data Sharing and Data Availability The authors confirm that the data supporting the findings of this study are available within the article. Competing interests The authors declare no conflicts of interest. Ethics approval statement The survey questionnaire was primarily conducted via telephone or WeChat, with one face-to-face questionnaire and three telephone or WeChat follow-ups administered. Participants ranged in age from 18 to 59 years. This study adheres to the principles of the Declaration of Helsinki and has been approved by the Medical Ethics Committee of Xinxiang Medical University (XYLL—20240341). Participants voluntarily completed the questionnaire. Prior to the survey, researchers introduced themselves to subjects, explained the study objectives, scheduled questionnaire interviews with consenting participants, and obtained signed informed consent. Participants adhered to the principle of voluntariness throughout the study and could withdraw at any time. Patient consent statement In this study, patients were evaluated before the investigation, and those who agreed to participate signed an informed consent form before the study commenced. Funding This study was supported by the Humanities and Social Sciences Research Program of the Henan Provincial Department of Education (2024-ZDJH-485), the Research Project of the Xinxiang Municipal Federation of Social Sciences (SKL-2025-0271), and the 2024 Postgraduate Research and Innovation Supporting Program Grant of Xinxiang Medical University (YJSCX202459Y). References Yang R, Liu X, Zhao Z, Zhao Y, Jin X. Burden of neurological diseases in Asia, from 1990 to 2021 and its predicted level to 2045: a Global Burden of Disease study. BMC Public Health . 2025;25(1):706. doi:10.1186/s12889-025-21928-9 Ji C, Ge X, Zhang J, Tong H. The Stroke Burden in China and Its Long-Term Trends: Insights from the Global Burden of Disease (GBD) Study 1990–2021. Nutr Metab Cardiovasc Dis . Published online January 2025:103848. doi: 10.1016/j.numecd.2025.103848 Medeiros GC, Roy D, Kontos N, Beach SR. Post-stroke depression: A 2020 updated review. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8453595","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572374172,"identity":"289dab7c-632d-4fe3-9310-cdf79d45f9ee","order_by":0,"name":"yali li","email":"","orcid":"","institution":"Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"yali","middleName":"","lastName":"li","suffix":""},{"id":572374173,"identity":"af55100f-1d6b-4eea-8c23-76e1e8af027d","order_by":1,"name":"ruiqin zhang","email":"","orcid":"","institution":"Xinxiang Medical 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1","display":"","copyAsset":false,"role":"figure","size":5922,"visible":true,"origin":"","legend":"\u003cp\u003eResourcefulness Trajectory Diagram\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8453595/v1/da107ee8c01573df73203cff.png"},{"id":100126897,"identity":"7affc2b4-01a1-4a3b-b569-708645c2e626","added_by":"auto","created_at":"2026-01-13 09:26:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5825,"visible":true,"origin":"","legend":"\u003cp\u003eSocial Support Trajectory Diagram\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8453595/v1/2e9b857fff0f1b79a1c67780.png"},{"id":100126907,"identity":"ac18f281-9bcf-43c5-99cc-2626234498ac","added_by":"auto","created_at":"2026-01-13 09:26:08","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":5922,"visible":true,"origin":"","legend":"\u003cp\u003eResourcefulness Trajectory Diagram\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8453595/v1/f8d2e11e900b82c221b43f42.png"},{"id":100126900,"identity":"6812512e-a303-46f9-9b6d-e68c7c0d216b","added_by":"auto","created_at":"2026-01-13 09:26:08","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":5825,"visible":true,"origin":"","legend":"\u003cp\u003eSocial Support Trajectory Diagram\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8453595/v1/9e896d38a13eb98ae8a98894.png"},{"id":100382313,"identity":"36e5ea2e-a90d-415f-b670-d851eb97c61e","added_by":"auto","created_at":"2026-01-16 10:42:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1996093,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8453595/v1/f24333fb-fbcf-4fba-af92-4eb6b665930b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual-Trajectory Analysis of Resourcefulness and Social Support in Middle- Aged and Young Stroke Patients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStroke has become one of the leading causes of death and disability worldwide, with a noticeable trend toward affecting younger individuals in recent years\u0026sup1;. In China, 31% of stroke patients are middle-aged and young adults, and this proportion continues to rise\u0026sup2;. More critically, approximately one-third of survivors develop post-stroke depression (PSD) after discharge, which severely impedes their recovery and quality of life and significantly increases mortality and suicide risk\u0026sup3;. Therefore, identifying and intervening in the key psychosocial mechanisms underlying PSD has become a central focus in contemporary stroke rehabilitation nursing research.\u003c/p\u003e \u003cp\u003eAmong various psychosocial resources, resourcefulness and social support are widely recognized as crucial factors influencing patients\u0026rsquo; psychological adjustment and rehabilitation outcomes. Resourcefulness, as an essential internal coping resource, encompasses both the cognitive and behavioral abilities required to accomplish tasks independently (personal resourcefulness) and the ability to actively seek external assistance when independent completion is not feasible (social resourcefulness). Prior studies have demonstrated its effectiveness in buffering the transformation of stress into depressive symptoms⁴,⁵. Social support, by providing emotional comfort, informational guidance, and material assistance, can significantly enhance patients\u0026rsquo; psychological resilience and confidence in recovery⁶. Existing research has consistently identified a strong positive correlation between resourcefulness and social support⁷. Enhancing resourcefulness may help individuals utilize social support more effectively, while high-quality social support can further strengthen resourcefulness through emotional reinforcement and information exchange, forming a positive feedback loop that mitigates stress responses⁸,⁹. For instance, a cross-sectional study by Chai et al.\u0026sup1;⁰ revealed that diverse social networks improved self-rated health by enhancing resilience, leading to higher resourcefulness levels and attenuating the negative effects of high stress. Similarly, Seok and Lee\u0026sup1;\u0026sup1; reported that greater caregiver preparedness and competence contributed to improvements in patients\u0026rsquo; psychological resilience and health-related quality of life, suggesting that a mutually reinforcing cycle of resourcefulness and support can effectively alleviate disease-related stress.\u003c/p\u003e \u003cp\u003eHowever, most existing studies, based on cross-sectional or conventional longitudinal designs, have struggled to capture the bidirectional and dynamic relationship between these two constructs over time. Research investigating their synergistic temporal evolution remains limited, offering insufficient understanding of the co-developmental pathways of resourcefulness and social support\u0026sup1;\u0026sup2;,\u0026sup1;\u0026sup3;. In contrast, Group-Based Trajectory Modeling (GBTM) employs a semiparametric mixture model to classify heterogeneous populations into a finite number of latent subgroups, each with distinct developmental trajectories\u0026sup1;⁴,\u0026sup1;⁵. By assigning individuals to the most probable group based on maximum posterior probability, GBTM enables the identification of nonlinear temporal patterns and the exploration of bidirectional interaction mechanisms. Additionally, conditional and joint probability analyses allow visualization of overlapping and transitional relationships between trajectories, providing quantitative evidence for determining optimal intervention timing and targets\u0026sup1;⁶.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to construct a dual-trajectory model to capture the heterogeneous trajectories of resourcefulness and social support among middle-aged and young stroke patients. This approach offers a new paradigm and evidence-based support for understanding their dynamic, bidirectional causal mechanisms, facilitating the identification of high-risk individuals with \u0026ldquo;dual deficits,\u0026rdquo; and informing the development of precise and tiered intervention strategies. Specifically, our aims were to: (1) identify and validate the patterns and quantities of both resourcefulness and social support trajectories; (2) explore the bidirectional associations and conversion probabilities between these trajectories; (3) examine shared risk factors influencing trajectory membership; and (4) provide theoretical foundations and practical targets for interrupting negative spirals and reducing the incidence of PSD.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants and Procedures\u003c/h2\u003e \u003cp\u003eUsing purposive sampling, a 6-month longitudinal follow-up study was conducted from November 2024 to October 2025 among 324 middle-aged and young stroke patients hospitalized in the Department of Neurology at a Grade III Class A hospital in Xinxiang City. The inclusion criteria were: ① aged 18\u0026ndash;60 years; ② first-ever stroke confirmed by CT or MRI; ③ in the stable inpatient phase following the acute stage, having entered rehabilitation and free of severe organ diseases such as cardiac, pulmonary, or renal dysfunction; and ④ possessing intact communication abilities and voluntarily agreeing to participate. The exclusion criteria included: ① inability to cooperate due to critical condition or impaired consciousness; ② recurrent stroke or development of major illness during follow-up; and ③ voluntary withdrawal or participation in other research projects during the study period. According to previous studies\u0026sup1;⁷, the correlation coefficient between the two variables was 0.38. Following Monte Carlo simulation principles, a structural equation model was constructed using pwrSEM; with a sample size of 200 and 5000 iterations, a statistical power of 0.80 could be achieved. Considering a 25% attrition rate, a minimum of \u0026ge;\u0026thinsp;267 participants was required. Ultimately, 324 valid participants were included, meeting the sample size requirements for trajectory identification and parameter estimation accuracy\u0026sup1;⁴. This study adheres to the principles of the Declaration of Helsinki and has been approved by the Medical Ethics Committee of Xinxiang Medical University (XYLL\u0026mdash;20240341).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Research Instruments\u003c/h2\u003e \u003cp\u003eThe \u003cb\u003eGeneral Information Questionnaire\u003c/b\u003e, designed by the research team, collected demographic and clinical details including age, gender, education level, occupation, marital status, number of chronic comorbidities, and caregiving situation.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eResourcefulness Scale (RS)\u003c/b\u003e was developed by Zauszniewski et al.\u0026sup1;⁸ and translated/adapted into Chinese by Wang Shumi et al.\u0026sup1;⁹. It has demonstrated good applicability in diverse populations, including those with chronic diseases. The scale contains 28 items comprising two dimensions: personal resourcefulness (16 items) and social resourcefulness (12 items), scored on a 6-point Likert scale. Total scores range from 0 to 140, with higher scores indicating stronger resourcefulness. The Cronbach\u0026rsquo;s α coefficient of the Chinese version is 0.85, and the correlation coefficient between dimensions is 0.41\u0026sup2;⁰.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eSocial Support Rating Scale (SSRS)\u003c/b\u003e was developed by Xiao Shuiyuan\u0026sup2;\u0026sup1; and includes 10 items across three dimensions: objective support, subjective support, and the utilization of social support. Scores range from 12 to 66, with higher scores indicating better support levels. The Cronbach\u0026rsquo;s α coefficient ranges from 0.825 to 0.896.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Data Collection\u003c/h2\u003e \u003cp\u003ePrevious studies\u0026sup2;\u0026sup2;\u003csup\u003e-\u003c/sup\u003e\u0026sup2;⁴ have shown that the three-month period following hospital discharge is critical for stroke progression. Considering expert recommendations and available human and material resources, follow-up assessments were conducted at four time points: during hospitalization (baseline, T0), one-month post-discharge (T1), three months post-discharge (T2), and six months post-discharge (T3) (\u0026plusmn;\u0026thinsp;1 week). T0 assessments were conducted in quiet hospital areas by the principal investigator and two trained researchers through face-to-face surveys. Before data collection, the purpose, significance, and instructions for the questionnaire were fully explained, and written informed consent was obtained. Contact information and home addresses were recorded for follow-up tracking. Participants completed the questionnaires independently when possible; for those with physical limitations, items were read aloud and answers recorded objectively by the researchers, without guiding responses. Completed questionnaires were checked immediately for completeness and accuracy, and any unclear items were clarified directly with the participant. Telephone or WeChat follow-ups were conducted at T1, T2, and T3 using designated stroke-center communication channels. Participants unreachable at \u0026ge;\u0026thinsp;3 follow-ups were categorized as lost to follow-up, and those missing\u0026thinsp;\u0026ge;\u0026thinsp;2 follow-up points were considered dropouts. The Last Observation Carried Forward (LOCF) method\u0026sup2;⁵ was used for missing data, which has demonstrated favorable performance in longitudinal stroke rehabilitation data with short follow-up intervals\u0026sup2;⁶,\u0026sup2;⁷ and aligns with the sensitivity analysis recommendations of Sterne et al.\u0026sup2;⁸.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Data Analysis\u003c/h2\u003e \u003cp\u003eData were double-entered using EpiData 3.1 and analyzed with Stata MP 18. First, separate single-trajectory models for resourcefulness and social support were fitted, beginning with 2\u0026ndash;6 potential trajectory groups. Polynomial orders from cubic to zero were tested. The optimal model was selected based on the lowest Bayesian Information Criterion (BIC), average posterior probability (AvePP)\u0026thinsp;\u0026ge;\u0026thinsp;0.70, subgroup membership\u0026thinsp;\u0026ge;\u0026thinsp;5%, and clinical interpretability. The best-fitting parameters for each variable were then applied to the dual-trajectory model. Conditional and joint probabilities were calculated to assess the developmental relationship between the two variables. Categorical data were expressed as n (%), and normally distributed continuous data were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Multinomial logistic regression was used to identify factors associated with trajectory group membership. A two-tailed α of 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eFrom November 2024 to April 2025, 338 middle-aged and young stroke patients meeting the inclusion/exclusion criteria were initially enrolled. By October 2025, all follow-up assessments were completed. At T1, 1 participant refused follow-up and 3 experienced recurrences; at T2, 5 refused follow-up and 5 experienced recurrences; and at T3, 4 patients were lost to follow-up (unreachable or disconnected) and 4 withdrew from the study. The Last Observation Carried Forward method\u0026sup2;⁶ was used to impute 8 missing values at T3, resulting in a total of 14 dropout cases. Ultimately, 324 patients successfully completed all follow-ups. The loss-to-follow-up rate at each point was 1.2% (T1) and 3.0% (T2), with an overall rate of 4.2%. The final participation rate reached 98.2%, and the questionnaire validity rate was 100% at all time points. General demographic and clinical characteristics are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Resourcefulness Trajectory\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with the increasing number of resourcefulness trajectory subgroups, the absolute values of BIC continuously decreased, with AvePP consistently\u0026thinsp;\u0026ge;\u0026thinsp;0.70. However, when the number exceeded four, one subgroup demonstrated a small sample size and a highly similar developmental pattern to adjacent types. Considering both interpretability and practical significance, a four-group model with trajectory orders of 3-3-3-3 was selected as the optimal solution. The trajectory patterns are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAll groups showed slight improvement in resourcefulness during the first month after discharge, followed by divergence. Group 1 (31.4%) displayed minimal change and persistently low levels, defined as the Low Resourcefulness\u0026ndash;Persistent Vulnerability Group. Group 2 (30.3%) showed moderate levels but a sustained decline with the steepest drop between T1 and T3, designated as the Moderate Resourcefulness\u0026ndash;Rapid Decline Group. Group 3 (22.1%) increased from T0 to T1, declined at T2, but remained above T0 levels through T3, identified as the High Resourcefulness\u0026ndash;Fluctuating Recovery Group. Group 4 (16.2%) maintained consistently high levels after an initial increase, labeled as the High Resourcefulness\u0026ndash;Stable Excellence Group.\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\u003eParameters for resourcefulness Trajectory Fitting\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=\"char\" char=\".\" 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=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Trajectories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvePP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup Member Ratio (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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colname=\"c5\"\u003e \u003cp\u003e0.998, 0.984, 0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.55, 48.21, 18.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 3 3 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3980.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3942.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999, 0.976, 0.975, 0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.40, 30.30, 22.10, 16.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 3 3 3 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3895.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3847.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.998, 0.917, 0.985, 0.950, 0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.92, 8.09, 25.98, 20.88, 16.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 3 3 3 3 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3762.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3705.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.987, 0.991, 0.958, 0.977, 0.964, 0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.63, 22.56, 7.53, 25.20, 20.90, 16.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Logistic Regression for Resourcefulness Trajectory Groups\u003c/h2\u003e \u003cp\u003eUsing the Low Resourcefulness\u0026ndash;Persistent Vulnerability Group as the reference and controlling for age, gender, and educational level, multinomial logistic regression was performed. Given that this reference group remained at the lowest level across all time points, RRR\u0026thinsp;\u0026gt;\u0026thinsp;1 in the remaining groups signifies protective effects facilitating upward resourcefulness development.\u003c/p\u003e \u003cp\u003eThe results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Middle-aged patients (46\u0026ndash;60 years) were less likely than younger patients to be classified into the High Resourcefulness\u0026ndash;Fluctuating Recovery Group or the High Resourcefulness\u0026ndash;Stable Excellence Group (RRR\u0026thinsp;=\u0026thinsp;0.148 and 0.051, both P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Educational attainment was a significant protective factor. Individuals with high school/vocational education or college/university degrees had significantly greater odds of entering higher resourcefulness groups than those with primary education or below. For example, the RRR for the college/university education category entering the High Resourcefulness\u0026ndash;Stable Excellence Group was 8.422 (P\u0026thinsp;=\u0026thinsp;0.001). Moreover, no family history of stroke, unimpaired BADL function, and higher income also increased the likelihood of belonging to higher resourcefulness trajectories, indicating the contributions of functional status and socioeconomic support to resourcefulness development.\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\u003eLogistic Regression Results for Baseline Factors Influencing Resourcefulness Trajectories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExamples\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003emoderate resourcefulness-rapidly declining group VS low resourcefulness-persistently weak group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ehigh resourcefulness-fluctuating recovery group VS low resourcefulness-persistently weak group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ehigh resourcefulness-stable excellence group VS low resourcefulness-persistently weak group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRRR (95%CI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eRRR (95%CI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eRRR (95%CI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (young adults18ཞ45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(24.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiddle-aged 46ཞ59岁\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246(75.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.271(0.772\u0026thinsp;~\u0026thinsp;0.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.148(0.039\u0026thinsp;~\u0026thinsp;0.562)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.051(0.012\u0026thinsp;~\u0026thinsp;0.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (man)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196(60.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewoman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128(39.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.222(0.638\u0026thinsp;~\u0026thinsp;2.341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.899(0.426\u0026thinsp;~\u0026thinsp;1.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.250(0.531\u0026thinsp;~\u0026thinsp;2.944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (primary and junior high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84(25.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh or vocational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165(50.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.527(2.125\u0026thinsp;~\u0026thinsp;9.646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.623(1.851\u0026thinsp;~\u0026thinsp;11.544)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.979(0.981\u0026thinsp;~\u0026thinsp;9.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(23.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.749(1.705\u0026thinsp;~\u0026thinsp;13.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.874(2.536\u0026thinsp;~\u0026thinsp;24.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.422(2.308\u0026thinsp;~\u0026thinsp;30.732)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpouse (none)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74(22.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250(77.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.416(0.268\u0026thinsp;~\u0026thinsp;7.490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.796(0.106\u0026thinsp;~\u0026thinsp;5.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.453(0.044\u0026thinsp;~\u0026thinsp;4.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation (worker)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142(43.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(30.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.809(0.248\u0026thinsp;~\u0026thinsp;2.638)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.123(0.274\u0026thinsp;~\u0026thinsp;4.609)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.418(0.276\u0026thinsp;~\u0026thinsp;7.295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed/freelancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(25.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.403(0.395\u0026thinsp;~\u0026thinsp;4.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.560(0.858\u0026thinsp;~\u0026thinsp;14.771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.405(0.455\u0026thinsp;~\u0026thinsp;12.714)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status (currently employed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223(68.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eretired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101(31.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.011(0.365\u0026thinsp;~\u0026thinsp;2.804)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.103(0.345\u0026thinsp;~\u0026thinsp;3.526)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.882(0.472\u0026thinsp;~\u0026thinsp;7.501)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic diseases (1\u0026ndash;2 types)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220(67.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4 types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(32.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.099(0.527\u0026thinsp;~\u0026thinsp;2.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.673(0.756\u0026thinsp;~\u0026thinsp;3.699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.823(1.563\u0026thinsp;~\u0026thinsp;9.351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance type (Employee medical insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107(33.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResident medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173(53.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.056(0.411\u0026thinsp;~\u0026thinsp;2.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.547(0.186\u0026thinsp;~\u0026thinsp;1.611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.660(0.191\u0026thinsp;~\u0026thinsp;2.286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOut-of-pocket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(13.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.378(0.107\u0026thinsp;~\u0026thinsp;1.339)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.042(0.079\u0026thinsp;~\u0026thinsp;0.222)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.088(0.015\u0026thinsp;~\u0026thinsp;0.523)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (low level: 3000~)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79(24.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium level: 5000~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.983(0.471\u0026thinsp;~\u0026thinsp;2.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.354(0.566\u0026thinsp;~\u0026thinsp;3.237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.256(0.720\u0026thinsp;~\u0026thinsp;7.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh level: 7000~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(25.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.099(0.848\u0026thinsp;~\u0026thinsp;5.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.960(1.054\u0026thinsp;~\u0026thinsp;8.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.614(1.009\u0026thinsp;~\u0026thinsp;12.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121(37.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203(62.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.766(0.382\u0026thinsp;~\u0026thinsp;1.536)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.440(0.205\u0026thinsp;~\u0026thinsp;0.942)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.165(0.067\u0026thinsp;~\u0026thinsp;0.407)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving arrangements (spouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145(44.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpouse and children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(27.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.676(0.716\u0026thinsp;~\u0026thinsp;3.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.350(0.915\u0026thinsp;~\u0026thinsp;6.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.423(0.816\u0026thinsp;~\u0026thinsp;7.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.111\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(27.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.736(0.594\u0026thinsp;~\u0026thinsp;12.611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.512(0.230\u0026thinsp;~\u0026thinsp;9.933)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.874(0.962\u0026thinsp;~\u0026thinsp;7.936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver (spouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135(41.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106(32.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.307(0.617\u0026thinsp;~\u0026thinsp;2.769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.399(0.586\u0026thinsp;~\u0026thinsp;3.342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.354(0.471\u0026thinsp;~\u0026thinsp;3.892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.574\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(25.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.409(0.144\u0026thinsp;~\u0026thinsp;1.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.901(0.230\u0026thinsp;~\u0026thinsp;2.705)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.613(0.170\u0026thinsp;~\u0026thinsp;2.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBADL (barrier)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(26.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree barrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239(73.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.565(0.252\u0026thinsp;~\u0026thinsp;1.265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.725(0.296\u0026thinsp;~\u0026thinsp;1.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.355(0.135\u0026thinsp;~\u0026thinsp;0.937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: *Variable brackets indicate reference groups; BADL includes bathing, dressing, eating, toileting, grooming, and walking. No difficulty in any item indicates no disability; difficulty in any item indicates disability.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Social Support Trajectory\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, increasing the number of subgroups yielded progressively smaller BIC absolute values, with AvePP\u0026thinsp;\u0026ge;\u0026thinsp;0.70. However, beyond three groups, the p-values became insignificant, making further subgrouping unjustified. Therefore, a three-group model with polynomial order 2-2-2 was identified as the optimal fit. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the developmental trends.\u003c/p\u003e \u003cp\u003e Overall, stroke patients exhibited relatively high social support after discharge (36.40\u0026thinsp;\u0026plusmn;\u0026thinsp;7.43). All patients reported the highest support level at T0, followed by varying degrees of decline. Group 1 (18.1%) maintained low-to-moderate but stable levels, referred to as the Low-to-Moderate Social Support\u0026ndash;Stable Group. Group 2 (49.8%) remained at moderate-to-high support but experienced continuous decline over six months, labeled the Moderate-to-High Social Support\u0026ndash;Declining Group. Group 3 (32.1%) had high baseline levels, a slight decrease by T2, and a rapid rise by T3, maintaining overall high support, identified as the High Social Support\u0026ndash;Gradual Recovery Group.\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\u003eFitting Parameters for Social Support Trajectories\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Trajectories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvePP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup Member Ratio (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4113.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4098.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.983, 0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.59, 34.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 2 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3897.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3875.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.931, 0.967, 0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.10, 49.80, 32.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 2 2 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3843.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3816.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.964, 0.895, 0.957, 0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.13, 19.80, 42.83, 31.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 2 2 2 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3721.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3683.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.972, 0.912, 0.959, 0.956, 0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.45, 19.28, 38.45, 22.99, 12.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Logistic Regression for Social Support Trajectory Groups\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. With the Low-to-Moderate Social Support\u0026ndash;Stable Group as the reference and controlling for major covariates, multinomial logistic regression showed that middle-aged patients were more likely than younger patients to be categorized into the Moderate-to-High Social Support\u0026ndash;Declining Group (RRR\u0026thinsp;=\u0026thinsp;4.274, P\u0026thinsp;=\u0026thinsp;0.036). Educational attainment remained a significant positive factor: individuals with high school/vocational diplomas, associate degrees, or higher education were far more likely to be assigned to the High Social Support\u0026ndash;Gradual Recovery Group (RRR\u0026thinsp;=\u0026thinsp;11.064, P\u0026thinsp;=\u0026thinsp;0.001). Additionally, absence of family history, non-self-paying medical insurance, intact ADL function, and having caregivers other than spouses significantly increased the likelihood of entering higher-support trajectories, suggesting that social structural and functional resources jointly influence social support development.\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\u003eLogistic Regression Results for Baseline Factors Influencing Social Support\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExamples\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eModerate-High Social Support - Rapid Decline Group VS Moderate-Low Social Support - Persistent Stability Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eHigh Social Support - Fluctuating Recovery Group VS Moderate-Low Social Support - Persistent Stability Group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRRR (95%CI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eRRR (95%CI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (young adults18ཞ45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(24.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiddle-aged 46ཞ59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246(75.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.274(1.102\u0026thinsp;~\u0026thinsp;16.577)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.483(0.356\u0026thinsp;~\u0026thinsp;6.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (man)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196(60.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewoman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128(39.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.978(0.399\u0026thinsp;~\u0026thinsp;2.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.281(0.493\u0026thinsp;~\u0026thinsp;3.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (primary and junior high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84(25.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh or vocational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165(50.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.483(0.548\u0026thinsp;~\u0026thinsp;4.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4.525(1.397\u0026thinsp;~\u0026thinsp;14.661)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(23.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.760(0.477\u0026thinsp;~\u0026thinsp;6.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e11.064(2.616\u0026thinsp;~\u0026thinsp;46.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpouse (none)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74(22.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250(77.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.568(0.059\u0026thinsp;~\u0026thinsp;112.302)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.006(0.044\u0026thinsp;~\u0026thinsp;92.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation (worker)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142(43.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(30.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.554(0.322\u0026thinsp;~\u0026thinsp;1.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.530(0.085\u0026thinsp;~\u0026thinsp;3.320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed/freelancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(25.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.181(0.102\u0026thinsp;~\u0026thinsp;3.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.189(0.030\u0026thinsp;~\u0026thinsp;1.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status (currently employed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223(68.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eretired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101(31.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.017(0.218\u0026thinsp;~\u0026thinsp;4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.109(0.400\u0026thinsp;~\u0026thinsp;11.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic diseases (1\u0026ndash;2 types)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220(67.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4 types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(32.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.987(0.382\u0026thinsp;~\u0026thinsp;2.553)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.893(0.323\u0026thinsp;~\u0026thinsp;2.472)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance type (Employee medical insurance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107(33.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResident medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173(53.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.669(0.484\u0026thinsp;~\u0026thinsp;5.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.392(0.621\u0026thinsp;~\u0026thinsp;9.217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOut-of-pocket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(13.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.093(0.021\u0026thinsp;~\u0026thinsp;0.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.154(0.031\u0026thinsp;~\u0026thinsp;0.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (low level: 3000~)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79(24.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium level: 5000~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.984(0.748\u0026thinsp;~\u0026thinsp;5.260)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.453(0.489\u0026thinsp;~\u0026thinsp;4.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh level: 7000~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(25.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.122(0.367\u0026thinsp;~\u0026thinsp;3.428)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.055(0.627\u0026thinsp;~\u0026thinsp;6.733)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121(37.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203(62.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.311(0.116\u0026thinsp;~\u0026thinsp;0.828)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.265(0.093\u0026thinsp;~\u0026thinsp;0.758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving arrangements (spouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145(44.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpouse and children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(27.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.925(0.555\u0026thinsp;~\u0026thinsp;6.676)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.755(0.744\u0026thinsp;~\u0026thinsp;10.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.129\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(27.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.707(0.108\u0026thinsp;~\u0026thinsp;205.434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.797(0.149\u0026thinsp;~\u0026thinsp;310.422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaregiver (spouse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135(41.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106(32.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.270(0.081\u0026thinsp;~\u0026thinsp;0.893)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.186(0.052\u0026thinsp;~\u0026thinsp;0.669)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.010\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(25.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.098(0.027\u0026thinsp;~\u0026thinsp;0.361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.029(0.007\u0026thinsp;~\u0026thinsp;0.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBADL (barrier)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(26.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree barrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239(73.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.074(0.015\u0026thinsp;~\u0026thinsp;0.365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.034(0.007\u0026thinsp;~\u0026thinsp;0.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: *Variable brackets indicate reference groups; BADL includes bathing, dressing, eating, toileting, grooming, and walking. No difficulty in any item indicates no disability; difficulty in any item indicates disability.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Dual Trajectories of Resourcefulness and Social Support\u003c/h2\u003e \u003cp\u003eGBTM revealed notable associations between the resourcefulness and social support trajectory groups (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with each column totaling 100%. Among individuals in the Low Resourcefulness\u0026ndash;Persistent Vulnerability Group, 33.2% belonged to the Low-to-Moderate Social Support\u0026ndash;Stable Group and 58.7% to the Moderate-to-High Social Support\u0026ndash;Declining Group (totaling 91.9%), while only 8.1% were classified into the High Social Support\u0026ndash;Gradual Recovery Group. The proportion assigned to the Moderate-to-High Social Support\u0026ndash;Declining Group decreased progressively across the four resourcefulness types (58.7%, 55.8%, 49.3%, 18.1%).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrates that the Low Resourcefulness\u0026ndash;Persistent Vulnerability Group accounted for the highest proportion in the Low-to-Moderate Social Support\u0026ndash;Stable Group (57.5% \u0026gt; 38.0% \u0026gt; 8.0%). Meanwhile, the High Resourcefulness\u0026ndash;Stable Excellence Group comprised 2.1%, 5.9%, and 39.6% of social support trajectories 1, 2, and 3, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProbability of Social Support Trajectory Groups Under Resourcefulness Trajectory Group Conditions (%)\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocial Support Trajectory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eResourcefulness Trajectory\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow resourcefulness-persistently weak group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emoderate resourcefulness-rapidly declining group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehigh resourcefulness-fluctuating recovery group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehigh resourcefulness-stable excellence group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate-Low Social Support - Persistent Stability Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate-High Social Support - Rapid Decline Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Social Support - Fluctuating Recovery Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eProbability of the Resourcefulness Group under Social Support Trajectory Conditions (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResourcefulness Trajectory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSocial Support Trajectory\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate-Low Social Support - Persistent Stability Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate-High Social Support - Rapid Decline Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Social Support - Fluctuating Recovery Group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow resourcefulness-persistently weak group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate resourcefulness-rapidly declining group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh resourcefulness-fluctuating recovery group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh resourcefulness-stable excellence group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows joint probabilities, with total probability summing to 100%. Notably, 10.6% of patients were simultaneously in the Low Resourcefulness\u0026ndash;Persistent Vulnerability and Low-to-Moderate Social Support\u0026ndash;Stable Groups; 16.8% in the Moderate Resourcefulness\u0026ndash;Rapid Decline and Moderate-to-High Social Support\u0026ndash;Declining Groups; 7.4% in the High Resourcefulness\u0026ndash;Fluctuating Recovery and High Social Support\u0026ndash;Gradual Recovery Groups; and 12.8% in the High Resourcefulness\u0026ndash;Stable Excellence and High Social Support\u0026ndash;Gradual Recovery Groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJoint Probability of the Resourcefulness Trajectory Group and the Social Support Trajectory Group (%)\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocial Support Trajectory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eResourcefulness Trajectory\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow resourcefulness-persistently weak group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emoderate resourcefulness-rapidly declining group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehigh resourcefulness-fluctuating recovery group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehigh resourcefulness-stable excellence group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate-Low Social Support - Persistent Stability Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate-High Social Support - Rapid Decline Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Social Support - Fluctuating Recovery Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.8%\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"},{"header":"4. Discussion","content":"\u003cp\u003eThis study, based on a GBTM framework, is the first to simultaneously characterize the heterogeneous developmental trajectories of resourcefulness and social support among middle-aged and young stroke patients. The findings reveal multidimensional evolutionary patterns in both trajectories, demonstrating the complexity and individual variability in resource dynamics during rehabilitation. Consistent with Zauszniewski\u0026rsquo;s observations\u0026sup2;⁹, resourcefulness among individuals with chronic illnesses undergoing the same intervention does not follow a single linear distribution but instead presents stratified trajectories. Ignoring such heterogeneity can dilute the effectiveness of interventions. Similarly, Tian\u0026sup3;⁰ showed through longitudinal analysis that the co-evolution of \u0026ldquo;social support\u0026ndash;psychological resources\u0026rdquo; is heterogeneous and differentiates over time. This suggests that resourcefulness and social support during post-stroke rehabilitation are not static but dynamic psychosocial resources with continuous transformations and mutual influence. Thus, trajectory characteristics can serve as crucial indicators for personalized intervention strategies.\u003c/p\u003e \u003cp\u003eHowever, traditional approaches often rely solely on baseline assessments and overlook dynamic changes\u0026sup3;\u0026sup1;. This study demonstrates that even patients with similar baseline resourcefulness levels may diverge significantly in recovery depending on their trajectory type. For example, the \u0026ldquo;Moderate Resourcefulness\u0026ndash;Rapid Decline\u0026rdquo; and \u0026ldquo;High Resourcefulness\u0026ndash;Fluctuating Recovery\u0026rdquo; types exhibited no significant differences at T0, yet by T3 the former dropped to low levels while the latter rebounded above baseline. This indicates that resilience and recovery patterns\u0026mdash;not initial levels\u0026mdash;are key determinants of long-term outcomes. Guo et al.\u0026sup3;\u0026sup2; also reported that fluctuating resourcefulness trajectories increase depression risk by 2.4 times compared with stable high levels, independent of baseline NIHSS and mRS scores, suggesting that trajectory grouping has higher predictive validity and clinical intervention value. Thus, trajectory-based screening enables early identification of patients at high risk for PSD and supports precise intervention matching and stratified PSD management.\u003c/p\u003e \u003cp\u003eWe identified substantial overlap and strong bidirectional associations between trajectory types of resourcefulness and social support. Among patients with persistently low resourcefulness, 58.7% belonged to the \u0026ldquo;Moderate-to-High Social Support\u0026ndash;Marked Decline\u0026rdquo; trajectory. Meanwhile, 79.6% of those with consistently high resourcefulness belonged to the \u0026ldquo;High Social Support\u0026ndash;Gradual Recovery\u0026rdquo; group. This alignment indicates that individuals with higher resourcefulness tend to maintain stronger support networks, whereas those with poorer resourcefulness experience rapid social support depletion. Consistent with Gui et al.\u0026sup3;\u0026sup3;, greater diversity of support sources strengthens coping abilities and delays cognitive decline. Latent class growth modeling research\u0026sup3;⁴ further confirms that diverse social networks enhance psychological resilience, and high-resilience individuals maintain broader and more stable social connections, forming a mutually reinforcing loop of strong resourcefulness and high support. This dynamic association suggests that resourcefulness and social support may form either positive or negative feedback cycles throughout rehabilitation\u0026sup3;⁵.\u003c/p\u003e \u003cp\u003eBy applying GBTM and conditional probability analysis, this study reveals temporal synergistic and compensatory mechanisms between internal psychological resources and external social resources. Based on the results, we categorized and proposed intervention strategies\u0026sup3;⁶. For patients classified as \u0026ldquo;low resourcefulness\u0026thinsp;+\u0026thinsp;low support\u0026rdquo; (10.6%), combined intervention should be initiated before discharge, integrating standardized resourcefulness training\u0026sup3;⁷ with digital follow-up platforms\u0026sup3;⁸ to strengthen both self-management ability and support network utilization. For those with \u0026ldquo;high resourcefulness\u0026thinsp;+\u0026thinsp;low support\u0026rdquo; (e.g., in the High Resourcefulness\u0026ndash;Fluctuating Recovery group), the focus should be on reinforcing support utilization through peer support groups and community rehabilitation resources\u0026sup3;⁹ to prevent resource depletion. For \u0026ldquo;low resourcefulness\u0026thinsp;+\u0026thinsp;high support\u0026rdquo; individuals, cognitive restructuring and problem-solving training⁴⁰ are recommended to prevent excessive reliance on external support and maintain self-efficacy. Importantly, Sun et al.⁴\u0026sup1; reported that social support needs are stage-specific and shift from emotional companionship to informational empowerment and eventually social reintegration. Therefore, interventions must adopt a \u0026ldquo;stage\u0026ndash;need\u0026ndash;training\u0026rdquo; closed-loop approach, dynamically adjusting content to improve efficiency of resource mobilization and reduce PSD risk.\u003c/p\u003e \u003cp\u003eLogistic regression results show that the four-dimensional resource structure of \u0026ldquo;age\u0026ndash;education\u0026ndash;function\u0026ndash;economy\u0026rdquo; is the central driving force influencing whether resourcefulness and social support evolve upward or downward. This parallel effect reflects compounded risk from overlapping individual and environmental vulnerabilities. Younger patients (18\u0026ndash;44 years) were significantly more likely to enter high resourcefulness trajectories than middle-aged patients. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicates that middle-aged patients had a 4.3-fold higher risk of developing the \u0026ldquo;moderate-to-high support\u0026ndash;declining\u0026rdquo; trajectory, possibly due to heavier life responsibilities such as employment and caregiving⁴\u0026sup2;, which increase vulnerability to resource depletion despite initial adequate support. Therefore, future interventions should incorporate role demands and family life-cycle factors⁸.\u003c/p\u003e \u003cp\u003eEducation plays a decisive role in cognitive capital. Individuals with college-level education or above were 8\u0026ndash;11 times more likely to enter the highest trajectories than those with primary-school education or below, independent of socioeconomic and functional status. Higher education enhances not only personal resourcefulness but also social resourcefulness and support utilization⁴\u0026sup3;. Therefore, for patients with low education, visually intuitive, stepwise, and repeatedly accessible digital educational materials should be prioritized⁴⁴.\u003c/p\u003e \u003cp\u003eFunctional independence and household economic status form a dual threshold for resource maintenance. BADL impairment significantly reduces the likelihood of entering high trajectories (RRR\u0026thinsp;\u0026asymp;\u0026thinsp;0.35; 0.03\u0026ndash;0.07), suggesting functional ability is a prerequisite for upward psychosocial resource development. Once functional dependence increases, both self-efficacy and perceived support decline simultaneously⁴⁵, triggering a negative cycle. Furthermore, self-funded treatment and low income restrict access to high-quality rehabilitation resources⁴⁶. Prior to discharge, clinical teams should conduct dual functional-economic assessments⁴⁷ and implement low-cost hybrid rehabilitation models combining home-based services and digital support. Finally, caregiver characteristics exert significant influence. Compared to spousal caregivers, children or others more effectively integrate digital and community resources to build broader support networks⁴⁸, while spouse caregivers tend to rely solely on traditional supports. Thus, intervention programs should shift focus from caregiver identity to caregiver empowerment⁴⁹ by strengthening digital skills for spouse caregivers and emotional communication skills for children\u0026rsquo;s caregivers.\u003c/p\u003e \u003cp\u003eIn summary, this study pioneers the use of GBTM in stroke rehabilitation psychology to model the co-evolution of resourcefulness and social support. The findings not only overcome limitations of traditional static-correlation research but also provide empirical evidence that enriches and extends theoretical frameworks in psychology and sociology. However, certain limitations exist. First, this single-center sample may introduce regional bias; future multi-center studies are recommended to verify the stability and generalizability of the model. Second, although the LOCF method was appropriate for handling missing data, it may underestimate volatility; more advanced methods are needed in future studies. Finally, only two psychosocial variables were examined; incorporating additional psychological and social determinants would support development of a more comprehensive intervention framework.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study, utilizing a GBTM dual-trajectory model, is the first to simultaneously demonstrate that middle-aged and young stroke patients exhibit four heterogeneous dynamic trajectories of resourcefulness and three trajectories of social support, with significant bidirectional synergistic effects between the two. Age, educational attainment, functional independence, and economic status jointly act as core determinants driving the direction of trajectory evolution. Therefore, clinical interventions should move beyond traditional static assessment frameworks and instead establish a stratified precision management system aligned with the \u0026ldquo;stage\u0026ndash;resource\u0026ndash;need\u0026rdquo; paradigm. For individuals with dual disadvantages, standardized resourcefulness training combined with collaborative construction of digital social support networks should be implemented prior to discharge. For individuals with \u0026ldquo;high resourcefulness but low support,\u0026rdquo; interventions should focus on strengthening the use of available support resources; whereas for individuals with \u0026ldquo;low resourcefulness but high support,\u0026rdquo; efforts should emphasize enhancing personal resourcefulness and developing problem-solving capabilities. Throughout rehabilitation, intervention strategies must be dynamically adjusted to accommodate evolving needs. Such an approach can effectively disrupt negative cycles, promote positive resource interactions, reduce the incidence of post-stroke depression, and ultimately improve rehabilitation outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all those who contributed to this study, the Henan Provincial Department of Education, the Foundation of Xinxiang Medical College, and all the participant administrators and participants in this study for their invaluable support and participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.J. and L.YL. conceptualized this study and designed the research protocol; Z.RQ. and H.L. performed the study and screened data for analysis; C. ST. and W.S. checked the data for accuracy; L.YL. and Z.YQ. performed the statistical analyses; S.J., L.YL. and L.X. prepared the outlines and wrote the manuscript. All authors contributed to the critical revision of manuscript drafts and they read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe order of authors listed in the manuscript has been approved by all of us.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing and Data Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey questionnaire was primarily conducted via telephone or WeChat, with one face-to-face questionnaire and three telephone or WeChat follow-ups administered. Participants ranged in age from 18 to 59 years.\u0026nbsp;This study adheres to the principles of the Declaration of Helsinki and has been approved by the Medical Ethics Committee of Xinxiang Medical University (XYLL\u0026mdash;20240341).\u0026nbsp;Participants voluntarily completed the questionnaire. Prior to the survey, researchers introduced themselves to subjects, explained the study objectives, scheduled questionnaire interviews with consenting participants, and obtained signed informed consent. Participants adhered to the principle of voluntariness throughout the study and could withdraw at any time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, patients were evaluated before the investigation, and those who agreed to participate signed an informed consent form before the study commenced.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Humanities and Social Sciences Research Program of the Henan Provincial Department of Education (2024-ZDJH-485), the Research Project of the Xinxiang Municipal Federation of Social Sciences (SKL-2025-0271), and the 2024 Postgraduate Research and Innovation Supporting Program Grant of Xinxiang Medical University (YJSCX202459Y).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang R, Liu X, Zhao Z, Zhao Y, Jin X. 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Designing and evaluating IT applications for informal caregivers: Scoping review. \u003cem\u003eJ Med Internet Res\u003c/em\u003e. 2024;26: e57393. doi:10.2196/57393\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stroke Rehabilitation, Resourcefulness༛Social support༛Dual-trajectory modeling༛Precision intervention","lastPublishedDoi":"10.21203/rs.3.rs-8453595/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8453595/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eWith the rising incidence of stroke among younger populations and the high prevalence of post-stroke depression (PSD), this study examined dual trajectories of resourcefulness and social support in first-ever stroke patients aged 18–59 years. The aim was to clarify their dynamic interaction and inform precision strategies for PSD prevention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eUsing purposive sampling, 324 first-ever stroke patients hospitalized in three tertiary hospitals in Henan Province (November 2024–October 2025) were followed at four time points: during the stable hospitalization phase and at 1-, 3-, and 6-months post-discharge. Group-based trajectory modeling (GBTM, Stata 18) was used to identify distinct trajectories of resourcefulness and social support. Conditional and joint probability analyses, together with multinomial logistic regression, were performed to examine the associations between trajectory groups and influencing factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFour resourcefulness trajectories were identified: persistently low (31.4%), moderate but declining (30.3%), high but fluctuating recovery (22.1%), and consistently high (16.2%). Social support also demonstrated three trajectories: moderate–low stability (18.1%), moderate–high rapid decline (49.8%), and high fluctuating recovery (32.1%). Strong coupling existed between the two: 58.7% of individuals with persistently low resourcefulness were also in the rapidly declining support group, while 79.6% of those with consistently high resourcefulness belonged to the high-support recovery group. Older age (≥46 years), self-payment, impaired BADL, low income, and low education increased the risk of low-trajectory membership. Conversely, having a college degree or above increased the likelihood of being in the highest trajectories of both resourcefulness and social support by 8.4 and 11.1 times, respectively, compared with primary education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eResourcefulness and social support in middle-aged and young stroke patients demonstrate heterogeneous and dynamic development with clear reciprocal reinforcement. Single-point assessments are insufficient. Stage-specific interventions tailored to trajectory characteristics should be prioritized, especially for “dual-low” individuals, to disrupt negative spirals, lower PSD risk, and improve recovery outcomes.\u003c/p\u003e","manuscriptTitle":"Dual-Trajectory Analysis of Resourcefulness and Social Support in Middle- Aged and Young Stroke Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 09:26:02","doi":"10.21203/rs.3.rs-8453595/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-18T08:00:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T21:47:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303408966206511694421637384941241202893","date":"2026-02-10T19:03:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T18:26:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19685913652368212727693338632591500078","date":"2026-01-22T17:13:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T13:55:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-31T11:55:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-30T01:45:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-30T01:45:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-26T08:27:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f50ba86-1cec-4d0f-8599-4faaa96ce5c9","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T04:39:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 09:26:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8453595","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8453595","identity":"rs-8453595","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}