Longitudinal Trajectories of Perceived Stress During College Transition Among First-Generation Students: The Protective Roles of Psychological Resilience and Social Support

Longitudinal Trajectories of Perceived Stress During College Transition Among First-Generation Students: The Protective Roles of Psychological Resilience and Social Support | 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 Longitudinal Trajectories of Perceived Stress During College Transition Among First-Generation Students: The Protective Roles of Psychological Resilience and Social Support Jing Han, Meijing Chen, Xiaoyi Wu, Mingjun Xie, Yu BU, Qinglin XU, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8407034/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Mar, 2026 Read the published version in BMC Psychology → Version 1 posted 17 You are reading this latest preprint version Abstract The transition to college is a critical period for first-generation college students (FGCS), during which their perceived stress significantly influences developmental outcomes, including psychological well-being and physical health. However, this longitudinal study was conducted across four waves (October 2023, March 2024, October 2024, March 2025) to investigate these patterns in a sample of Chinese university students ( M age = 18.16, SD = 0.87; 52.5% male; 66.55% FGCS). We identified three distinct stress trajectories using latent class growth analysis: a low-rapidly declining-rebounding trajectory (10.23%), a moderate-gradual declining-stabilizing trajectory (29.58%), and a high-stable trajectory (60.19%). Students in the low-rebounding trajectory reported the most favorable outcomes (lowest anxiety and depression, highest life satisfaction and physical health), whereas those in the high-stable trajectory reported the poorest outcomes. Beyond the significant main effects of psychological resilience and social support, a significant interaction was observed. Specifically, FGCS with concurrently high levels of both resilience and social support were more likely to belong to the low-rebounding trajectory than the high-stable class. These findings underscore the dynamic nature of perceived stress and the synergistic role of protective factors in fostering positive adaptation among FGCS, offering valuable insights for developing individualized interventions. First-generation college students transition to college Perceived stress Psychological resilience Social support Figures Figure 1 Figure 2 Figure 3 Introduction Perceived stress among first-generation college students (FGCS) not only serves as a critical predictor of their attrition risk and academic achievement gaps but also acts as a key mechanism through which intergenerational educational disparities are perpetuated or disrupted (Stebleton et al., 2022; Toutkoushian et al., 2021 ). For instance, higher levels of stress are associated with poorer self-rated health, increased symptoms of depression and anxiety, and reduced subjective well-being (Hoyt et al., 2021 ; Samaha & Hawi, 2016). Unlike their continuing-generation peers, FGCS navigate the college transition without the benefit of familial academic capital, facing compounded stressors from cultural mismatch, financial precarity, and belonging uncertainty (Bennett et al., 2021; Hicks et al., 2021 ; Stebleton et al., 2022; Meza et al., 2023 ). Consequently, their stress experiences directly shape equity outcomes in higher education and long-term social mobility. Nonetheless, the field remains limited by a reliance on one or two cross-sectional measurements (Smith & Jones, 2023 ; Yeager et al., 2022 ; Chen & Vazsonyi, 2013 ), which offers little insight into the dynamic trajectories of stress—particularly throughout the critical transition to college. This gap is especially pertinent for FGCS. The college transition represents a period of heightened vulnerability to stress (Pascarella et al., 2004 ), with potential long-term implications for adult adaptation and success (Toutkoushian et al., 2021 ). As students navigate intensified environmental demands and developmental challenges during this time (Gerard & Booth, 2015; Lerner, 1982 ), their perceived stress is likely to fluctuate dynamically. Therefore, it is essential to investigate not only how perceived stress evolves over time among FGCS but also whether distinct subgroups with unique stress trajectories exist. Stress is likely to diminish for some students after initial adjustment, while others may experience persistent belonging uncertainty and adjustment-related stress that extends into the second year (Brady et al., 2020 ). Identifying protective factors associated with these different pathways is crucial for developing targeted interventions that promote positive adaptation and thriving among FGCS. While developmental theories (Lerner, 1982 ; Gerard & Booth, 2015) and prior research (Stokoe et al., 2024 ) posit that both individual and contextual factors shape stress experiences, few studies have empirically tested how these factors interact over time to influence stress outcomes in FGCS. Furthermore, potential differences in these protective mechanisms between FGCS and CGCS remain underexplored. Therefore, the present study adopts a longitudinal design to address these gaps and aims to: (1) identify heterogeneous trajectories of perceived stress among Chinese college students, with a focus on FGCS; (2) grounded in resilience theory (Fergus & Zimmerman, 2005 ) and prior research on social support and capital (Garcia et al., 2019 ; Liu et al., 2021 ; Xie, 2016 ), examine the protective roles of psychological resilience and social support in shaping these trajectories; and (3) investigate potential differences in the effects of these protective factors between FGCS and CGCS. This research offers significant theoretical and practical contributions. Theoretically, by simultaneously examining the independent and synergistic effects of internal assets (psychological resilience) and external resources (social support) across a critical developmental transition, this study provides a rigorous test and potential extension of the Developmental Assets Framework and the Theory of Resource Synergy, particularly in the context of non-traditional student populations. It advances understanding of how person-environment transactions dynamically shape adaptation over time. Practically, identifying distinct stress trajectories and their key moderators can directly inform the design of stage-specific and tailored interventions within higher education. For instance, if psychological resilience is found to be a critical buffer in the initial semester, then targeted skill-building programs can be prioritized early. Conversely, if the protective role of social support grows over time, efforts can shift toward strengthening peer networks and family-institution partnerships later in the transition. Such evidence-based insights are crucial for promoting educational equity and enhancing the well-being and success of FGCS. The Present Study Guided by a developmental systems perspective (Lerner, 2018), this two-year longitudinal investigation addresses two overarching aims: (a) to delineate the dynamic trajectories of perceived stress across the college transition, with a specific focus on first-generation college students (FGCS) in China, and (b) to elucidate the person-context mechanisms that shape adaptive versus maladaptive stress pathways. We advance three specific research hypotheses, derived from an integration of developmental, ecological, and resource-based theories, to guide this inquiry. Hypothesis 1: Heterogeneity and Consequences of Perceived Stress Trajectories Transition and life-course theories (Schlossberg, 2021 ; Elder, 1998) posit that developmental adaptation is characterized by systematic intra-individual change and significant inter-individual heterogeneity. While prior research documents an aggregate decline in student stress over time (Zhao et al., 2023 ), mean-level analyses obscure potential subgroup differences and the long-term developmental significance of distinct stress pathways (Nagin, 2005). Drawing from sociological perspectives on cultural capital (Bourdieu, 1986) and ecological models of development (Bronfenbrenner, 1979), we contend that the structural disparities faced by FGCS will manifest in systematically divergent stress dynamics compared to their continuing-generation peers (Stephens et al., 2012 ). We hypothesize: H1a: The longitudinal course of perceived stress will be best characterized by multiple, distinct trajectory classes (e.g., high-stable, declining, escalating ) across the first two college years, with the prevalence and form of these classes differing significantly between FGCS and CGCS. H1b: Membership in a more adaptive stress trajectory class (e.g., low-rapid decline ) will prospectively predict more favorable adjustment outcomes—including higher life satisfaction, better self-rated health, and lower depressive and anxiety symptoms—relative to membership in a chronic-high or escalating trajectory (Meeus, 2016). Hypothesis 2: The Protective Role of Developmental Assets The Developmental Assets Framework (Benson, 2007 ) posits that the convergence of internal strengths and external supports fosters positive youth development. Empirical evidence confirms that psychological resilience (Fergus & Zimmerman, 2005 ) and social support (Cohen & Wills, 1985) individually buffer against stress. However, the extant literature lacks a dynamic, person-centered examination of how these assets predict long-term patterns of stress regulation rather than single-timepoint outcomes (Wang & Degol, 2016). To address this gap, we hypothesize: H2: Higher baseline levels of psychological resilience and perceived social support will each independently increase the probability of belonging to a more adaptive (e.g., low-declining) stress trajectory over time. Hypothesis 3: Conditionally Coupled Resources and Group-Specific Mechanisms An integrated theoretical perspective, synthesizing Resource Synergy Theory (Hobfoll, 2002) and Cultural Mismatch Theory (Stephens et al., 2012 ), informs our final hypothesis. We propose that the functional utility of internal resources is context-dependent. For FGCS, who navigate higher education with less familial cultural capital and face unique structural barriers, robust external support may be a necessary condition for translating psychological resilience into effective stress regulation (Garcia Coll et al., 1996). In contrast, for CGCS, whose ecological niches are typically richer in pre-existing supports and cultural alignment, resilience and social support may function in a more independent or simply additive manner. Thus, we hypothesize a moderated-moderation model: H3: The interaction between psychological resilience and social support in predicting adaptive stress trajectory membership will be moderated by student generational status. Specifically, for FGCS, high psychological resilience will confer a protective advantage only when coupled with high social support (a conditionally coupled effect). For CGCS, resilience and social support are expected to demonstrate independent or additive effects without such strong synergistic coupling. Methods Participants And Procedure Data for this study were drawn from a four-wave longitudinal investigation of psychosocial adaptation among Chinese college students. Participants were recruited from three public universities to capture regional diversity: two institutions in Beijing (representing a major metropolitan context) and one in Changzhou, Jiangsu Province (representing a developed eastern region). An online survey was administered at four critical junctures across the first two academic years: at university entry (T1: October 2023), the end of the first semester (T2: March 2024), the beginning of the second year (T3: October 2024), and the end of the third semester (T4: March 2025). To ensure data integrity, each survey incorporated three attention-check items. Questionnaires were excluded if they contained more than one failed attention check or were completed in less than five minutes, a duration deemed insufficient for thoughtful response based on pilot testing. At baseline (T1), 2,760 first-year students provided valid responses ( M age = 18.18, SD = 0.58; 54.3% female). Valid response counts for T2, T3, and T4 were 2,654, 2,496, and 2,326, respectively. For the primary trajectory analyses, which require complete longitudinal data, the final analytic sample consisted of the 2,326 participants with responses at all four waves ( M age = 18.16, SD = 0.57; 55.6% female). This sample included 1,888 students from Beijing and 438 from Jiangsu. Attrition analysis revealed no significant differences in baseline demographic characteristics (age, gender, generational status) or key study variables (perceived stress, resilience, social support) between participants retained in the analytic sample and those who dropped out at any wave ( ps > .10). Prior to participation, all students provided electronic informed consent via the survey platform. The study protocol received full approval from the Institutional Review Board. The consent form detailed the study's purpose, procedures, risks, benefits, and confidentiality safeguards, and explicitly stated that participation was voluntary and could be withdrawn at any time without consequence. The average completion time per survey wave ranged from 15 to 30 minutes. Measures Perceived stress (T1-T4) Perceived stress was assessed using the Perceived Stress Scale (PSS; Cohen et al., 1983), which consists of 10 items (e.g., "How often have you been upset because of something that happened unexpectedly?"). Participants rated each item on a 5-point Likert scale ranging from 0 (never) to 4 (very often). After reverse-scoring relevant items, a total score was calculated, with higher scores indicating greater levels of perceived stress. In the present study, Cronbach's α = .83–.88 across waves. Psychological resilience (T1) Participants reported their level of psychological resilience using the 10-item Connor-Davidson Resilience Scale (CD-RISC; Connor & Davidson, 2003 ). Items (e.g., "I am able to adapt to change") were rated on a 5-point Likert scale ranging from 1 ("never") to 5 ("always"). A mean score was calculated, with higher scores reflecting greater resilience. In the current study, the scale demonstrated high internal consistency, with Cronbach's α = .92. Social support (T1) Social support (T1) was assessed using the Multidimensional Scale of Perceived Social Support (MSPSS; Zimet, Dahlem, Zimet, & Farley, 1988). This 12-item scale measures support from three sources: family, friends, and a significant other. Participants responded to items (e.g., "There is a special person who is around when I am in need") using a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). A mean score was computed, with higher scores indicating greater perceived social support. The scale demonstrated excellent internal consistency in this study, with a Cronbach′s α = .95. Depressive symptoms (T1 & T4) Depressive symptoms at T1/T4 were measured using the Chinese PHQ-9 (Kroenke et al., 2001 ), which aligns with DSM-IV diagnostic criteria. Respondents rated symptom frequency (e.g., "Feeling down, depressed, or hopeless") over the past two weeks on a 0–3 scale. Scores were summed (0–27 range; higher = greater severity; Cronbach′s α = .90–.92). Anxiety symptoms were assessed with the Chinese GAD-7 (Spitzer et al., 2006 ; DSM-5 criteria). Participants reported how often they felt bothered by symptoms (e.g., "Feeling nervous, anxious, or on edge") over the past month on a 0–3 scale. Total scores (0–21 range; higher = greater severity; Cronbach′s α = .93–.94) were used, with a cut-off ≥ 7 indicating probable clinical anxiety (Ip et al., 2022). Anxiety symptoms (T1 & T4) Anxiety symptoms were measured using the 7-item Generalized Anxiety Disorder scale (GAD-7; Spitzer, Kroenke, Williams, & Löwe, 2006 ). Respondents rated the frequency of their symptoms over the past month on a 4-point scale ranging from 0 (not at all) to 3 (nearly every day). The total score, derived from the sum of all items, reflects increasing anxiety severity. Consistent with validation studies in Chinese populations (Ip et al., 2022), a cutoff score of 7 was applied, whereby participants scoring above this threshold were categorized as experiencing probable clinical anxiety. The scale showed excellent internal consistency in the current study, with Cronbach′s α values ranging from 0.93 to 0.94 across assessments. Life satisfaction (T1 & T4) Life satisfaction was measured using the five-item Satisfaction With Life Scale (SWLS; Diener et al., 1985 ). Participants rated their agreement with each item (e.g., “In most ways my life is close to ideal”) on a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree). A total score was computed by summing all items, with higher scores indicating higher life satisfaction. The scale demonstrated excellent internal consistency across all time points, with Cronbach′s αranging from 0.94 to 0.96. Physical health (T2 & T4) Physical health was assessed using the 7-item Index of Somatic Symptoms (ISS; Kellner, 1987). Participants rated the frequency with which they experienced various somatic complaints (e.g., headaches, dizziness, fatigue) on a 5-point scale ranging from 0 (not at all) to 4 (extremely). A total score was computed by summing up all items, with higher scores indicating more severe somatic symptoms. The measure demonstrated good internal consistency in this study, with Cronbach′s α values ranging from 0.87 to 0.91 across time points. Demographic variables Consistent with theoretical and empirical precedent (Flanagan & Kornbluh, 2019; Zhao et al., 2024), we controlled for gender (0 = female, 1 = male), age, generational status (0 = continuing-generation, 1 = first-generation), subjective socioeconomic status (10-rung MacArthur scale), and family income (categorical). Baseline scores of outcome variables were also included to account for pre-existing differences and strengthen causal inference. Data Analytic Strategy All analyses were performed in a two-step sequence. First, descriptive statistics and bivariate correlations among core variables were computed using SPSS 27.0. Second, a comprehensive latent variable modeling framework was implemented in Mplus 8.7, consisting of three sequential stages designed to answer our specific research questions about heterogeneity and mechanisms. Stage 1: Identifying Heterogeneous Trajectories of Perceived Stress An unconditional latent growth curve model (LGCM) was first estimated to capture the overall developmental pattern of perceived stress across the four waves. To identify distinct subgroups, we conducted a latent class growth analysis (LCGA). Solutions ranging from two to five latent classes were compared. The time metric was specified by fixing the slope loadings to 0, 1, 2, and 3 to correspond to the baseline (T1), 6-month (T2), 12-month (T3), and 18-month (T4) assessments, respectively. The optimal class solution was selected based on a combination of statistical criteria and theoretical coherence. The following fit indices were examined: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size adjusted BIC (Adj-BIC), bootstrapped likelihood ratio test (BLRT), adjusted Lo-Mendell-Rubin test (Adj-LMR-LRT), and entropy. Model interpretability and the requirement that each class comprise at least 5% of the sample were also decisive factors (Nylund, Asparouhov, & Muthén, 2007). Stage 2: Linking Trajectory Membership to Developmental Outcomes After identifying the optimal trajectory classes, we employed the manual Block-Croon-Hagenaars (BCH) method to examine whether class membership predicted distal outcomes at T4, including life satisfaction, depressive symptoms, anxiety symptoms, and somatic health (see Fig. 1 for the conceptual diagram). The BCH method, designed explicitly for mixture model distal outcome analysis, maintains class stability by adjusting for classification error and is robust with continuous outcomes (Asparouhov & Muthén, 2014 ). All analyses in this stage controlled for the corresponding baseline (T1) level of the outcome variable. Stage 3: Examining Predictors of Trajectory Membership To investigate the protective factors shaping trajectory membership, we used the R3STEP procedure. This step involved a series of nested multinomial logistic regression models within the latent class framework. Model 1 included only demographic covariates (gender, age, urbanicity, subjective socioeconomic status). Model 2 added the main effects of psychological resilience, social support, and generational status (FGCS vs. CGCS). Model 3 introduced the two-way interaction between resilience and social support. Model 4 added the three-way interaction among resilience, social support, and generational status. Following established practice for probing higher-order interactions, if the three-way interaction was significant, we subsequently estimated separate models for the FGCS and CGCS subgroups to examine the two-way interaction within each group (Hayes & Matthes, 2009 ). All continuous predictors (resilience, social support) were grand-mean-centered prior to creating interaction terms to mitigate multicollinearity. Simple slope analyses were conducted to decompose significant interaction effects. Results Descriptive Analyses Descriptive statistics and bivariate correlations for all study variables are presented in Table 1 . As expected, psychological resilience and perceived social support showed consistent negative associations with perceived stress across all four time points ( r values ranged from − .45 to − .30, all p < .01). Perceived stress was positively correlated with concurrent depressive and anxiety symptoms at both T1 and T4 ( r = .24 to .54, p < .01). Furthermore, higher levels of perceived stress were associated with lower life satisfaction ( r = − .28 to − .61, p < .01) and greater somatic symptoms ( r = .19 to .42, p .05). Longitudinal Trajectories of Perceived Stress Among FGCS Unconditional latent growth curve modeling (LGCM) was conducted to examine the changes in perceived stress over time. Both linear and quadratic models demonstrated acceptable fit to the data (linear model: χ²/df = 14.19, p < 0.05, CFI = 0.98, TLI = 0.97, RMSEA = 0.08, SRMR = 0.05; quadratic model: χ² /df = 11.53, p < 0.05, CFI = 0.99, TLI = 0.98, RMSEA = 0.07, SRMR = 0.01). Model comparison indicated that the quadratic model provided a significantly better fit than the linear model (Δ χ² (4) = 59.42, p < 0.001), and was therefore selected as the final model. Results from the quadratic model revealed that the mean intercept ( b = 17.01, SE = 0.14, p < 0.001), mean linear slope ( b = -1.27, SE = 0.15, p < 0.001), and mean quadratic slope ( b = 0.19, SE = 0.05, p < 0.001) were all statistically significant. 1 This pattern suggests that, on average, perceived stress levels among college students initially decreased, then experienced a slight rebound, before decreasing again over time. Furthermore, significant variances were found for the intercept ( b = 24.01, SE = 2.52, p < 0.001), linear slope ( b = 13.59, SE = 3.09, p < 0.001), and quadratic slope ( b = 1.03, SE = 0.23, p < 0.001), indicating substantial individual differences in both initial levels of perceived stress and their developmental trajectories. These results suggest possible heterogeneity in perceived stress trajectories, supporting the presence of distinct subgroups following different patterns of change. Accordingly, LCGA was employed to identify unobserved subgroups. 1 As shown in Table 2, model fit indices for the two-to five-class LCGA solutions are presented. Although the five-class model demonstrated the best statistical fit, the smallest class comprised less than 5% of the total sample. Therefore, the three-class model was selected as the optimal representation of perceived stress trajectories among college students (see Fig. 2 ). Class 1 (n = 1400, 60.19% of the sample) exhibited a high-stable trajectory, characterized by a high initial level of perceived stress (intercept: b = 19.39, SE = 0.19, p < 0.001) that remained stable over time (linear slope: b = 0.02, SE = 0.21, p = 0.93; quadratic term: b =-0.11, SE = 0.06, p = 0.08).Class 2 ( n = 688, 29.58%) followed a moderate-gradual decline-stabilization pattern, with a moderate baseline level (intercept: b = 14.56, SE = 0.33, p < 0.001), a significant linear decrease (linear slope: b = − 2.07, SE = 0.46, p < 0.001), and a gradual stabilization reflected by a positive quadratic trend (quadratic term: b = 0.38, SE = 0.13, p < 0.01).Class 3 ( n = 238, 10.23%) was labeled low-rapid decline-rebound , showing low initial stress (intercept: b = 10.51, SE = 0.74, p < 0.001), a sharp initial decline (linear slope: b = − 5.62, SE = 0.71, p < 0.001), and a significant rebound toward the end of the observation period (quadratic term: b = 1.27, SE = 0.2, p < 0.001). 1 Note: Although both LCGA and growth mixture modeling (GMM) are widely used techniques form odeling longitudinal heterogeneity, alternative GMM analyses were also conducted. However, these models either failed to converge or did not yield proper solutions; see the Supplementary Materials for further details.. Table 1 Descriptive statistics and correlation among main variables( N = 2326) M SD 1 2 3 4 5 6 7 8 9 10 1. PS_T1 17.09 6.70 - - - - - - - - - - 2. PS_T2 15.72 6.37 0.46 ** - - - - - - - - - 3. PS_T3 15.41 6.21 0.40 ** 0.58 ** - - - - - - - - 4. PS_T4 14.94 6.16 0.39 ** 0.51 ** 0.63 ** - - - - - - - 5. PR_T1 47.37 10.80 -0.43 ** -0.36 ** -0.33 ** -0.30 ** - - - - - - 6. SS_T1 19.65 6.14 -0.45 ** -0.36 ** -0.34 ** -0.31 ** 0.50 ** - - - - - 7. DS_T4 3.78 4.20 0.25 ** 0.34 ** 0.42 ** 0.54 ** -0.21 ** -0.23 ** - - - - 8. AS_T4 2.54 3.52 0.24 ** 0.32 ** 0.39 ** 0.50 ** -0.16 ** -0.21 ** 0.74 ** - - - 9. LS_T4 21.15 5.10 -0.28 ** -0.42 ** -0.51 ** -0.61 ** 0.33 ** 0.29 ** -0.44 ** -0.36 ** - - 10. PH_T4 8.17 4.08 0.19 ** 0.28 ** 0.34 ** 0.420 * -0.16 ** -0.21 ** 0.58 ** 0.55 ** − .32 ** - Covariates 11. Gender 0.04 0.50 0.01 0.07 ** 0.34 ** 0.08 ** -0.11 ** -0.04 0.02 -0.01 -0.17 ** -0.01 12. Age_T1 18.14 0.97 0.01 0.02 0.08 ** 0.02 -0.02 -0.01 0.01 -0.01 -0.02 -0.01 13. SSS_T1 10.04 2.68 -0.17 ** -0.13 ** -0.01 -0.11 ** 0.21 ** 0.19 ** -0.06 ** -0.04 * 0.14 ** -0.06 ** 14. DS_T1 4.58 4.35 0.53 ** 0.39 ** -0.10 ** 0.31 ** -0.41 ** -0.43 ** 0.33 ** 0.29 ** -0.28 ** 0.27 ** 15. AS_T1 3.29 3.85 0.53 ** 0.38 ** 0.34 ** 0.29 ** -0.37 ** -0.41 ** 0.31 ** 0.34 ** -0.22 ** 0.25 ** 16. LS_T1 21.57 4.85 -0.56 ** -0.42 ** 0.32 ** -0.32 ** 0.53 ** 0.47 ** -0.21 ** -0.17 ** 0.39 ** -0.14 ** 17. PH_T1 4.09 4.52 0.27 ** 0.47 ** -0.37 ** 0.34 ** -0.22 ** -0.24 ** 0.36 ** 0.35 ** -0.29 ** 0.37 ** Note . PS: Perceived stress; PR: Psychological resilience; SS: Social Support; DS: Depressive symptom; AS: Anxiety symptoms; LS: Life Satisfaction; PH: Physical health; SSS: subjective socioeconomic status; * p < 0.05, ** p < 0.01. Repeated-measures ANOVA and paired-samples t-tests were conducted to examine differences in perceived stress across time points among the three trajectory classes. A significant main effect of trajectory class was observed ( F (2, 2323) = 3713.47, p < 0.01, η² = 0.762). The low-rapid decline-rebound group reported significantly lower perceived stress at T1 compared to both the moderate-gradual decline-stabilization group ( M diff = − 4.49, p < 0.001) and the high-stable group ( M diff = − 9.63, p < 0.001). Post-hoc McNemar tests indicated that perceived stress levels at T2 and T3 were significantly lower than at T1 (p < 0.001). Although a slight rebound occurred at T4 relative to T3 (p < 0.001), T4 levels remained significantly lower than those at T1 ( p 0.05). The moderate-gradual decline-stabilization group showed significantly lower levels of stress at T1 than the high-stable group ( M diff = − 5.13, p < 0.001) and exhibited a significant decreasing trend over time (p < 0.001). In contrast, the high-stable group maintained consistently high stress levels across all time points with no significant change ( F (2.77, 2323) = 3868.46, p = 0.336). The trajectories of perceived stress differed significantly between FGCS and CGCS across the four time points. Independent-samples t-tests were used to compare perceived stress between FGCS and CGCS at each time point. No significant differences were found at T1 ( t (1417.41) = 1.68, p = 0.093) or T4 ( t (1434.20) = − 0.04, p = 0.967). However, FGCS reported significantly higher perceived stress than their continuing-generation peers at both T2 ( t (1422.29) = 2.90, p = 0.004) and T3 ( t (1393.18) = 2.31, p = 0.021). T3. Table 2 Model fit statistics for latent class growth analyses results ( N = 2326) Classes AIC BIC Adj-BIC Entropy Adj-LMR-LRT ( p value) BLRT ( p value) SCN(%) 2 58195.28 58258.55 58223.60 0.86 < 0.001 < 0.001 673(28.93%) 3 57773.39 57859.67 57812.01 0.80 < 0.001 < 0.001 238(10.23%) 4 57642.26 57751.54 57691.18 0.81 < 0.01 < 0.001 69(2.97%) 5 57488.98 57621.27 57548.20 0.82 < 0.001 < 0.001 89(3.83%) Note . The final class solution is bolded. AIC: Akaike information criterion, BIC: Bayesian information criterion, Adj-BIC: sample-size adjusted Bayesian information criterion, Adj-LMR-LRT: adjusted Lo-Mendell-Rubin likelihood test, BLRT: bootstrapped likelihood ratio test, SC: smallest class size. Trajectories Perceived Stress and Associations with Outcomes As presented in Table 4 , the BCH method revealed significant differences across trajectory classes in all outcome measures. The low-decreasing-increasing group demonstrated the most favorable outcomes, including the highest life satisfaction ( p < 0.001), the lowest levels of depressive ( p < 0.001) and anxiety symptoms ( p < 0.001), and the best physical health ( p < 0.001). In contrast, the high-stable group exhibited the lowest life satisfaction ( p < 0.001), the highest depressive ( p < 0.001) and anxiety symptoms ( p < 0.001), and the poorest physical health ( p < 0.001). Effects of Psychological Resilience, Social Support, and FGCS Status on Trajectories of Perceived Stress Table 3 presents the descriptive results of the three latent trajectory patterns of perceived stress. Based on these patterns, we employed the R3STEP approach to examine the effects of psychological resilience, social support, and FGCS status on trajectory membership, controlling for demographic covariates such as gender, age, urban location, and SSS (see Table 5 ). Both psychological resilience( OR = 1.66, p < 0.001) and social support ( OR = 1.80, p < 0.001) showed significant main effects on trajectory classification. Specifically, individuals with higher resilience or social support were more likely to belong to the low-rapid decline-rebound group compared to either the high-stable group ( OR = 2.51, p < 0.001) or the moderate-gradual decline-stabilization group ( OR = 3.63, p < 0.001). However, no significant interaction was found between resilience and social support on trajectory( OR = 1.05, p = 0.844). Model 4 indicated a significant three-way interaction among psychological resilience, social support, and FGCS status when comparing the high-stable group with the low-rapid decline-rebound group , and the moderate-gradual decline stabilization group with the low-rapid decline-rebound group . Further analysis (Table 6 ) revealed a significant two-way interaction between resilience and social support only among FGCS when comparing the high-stable and low-rapid decline-rebound trajectories ( OR = 2.48, p = 0.004), which was not observed among continuing-generation students. Simple slope analyses (Fig. 3 ) showed that, among FGCS with high resilience (i.e., 1 SD above the mean), higher social support significantly predicted membership in the low-rapid decline-rebound group compared to the high-stable group ( OR = 1.47, p = 0.032). Similarly, a marginally significant effect was found for the comparison between the low-rapid decline-rebound and moderate-gradual decline-stabilization groups among highly resilient FGCS ( OR = 1.40, p = 0.053). In contrast, no significant interaction was detected among continuing-generation students ( OR = 1.01, p = 0.988). Table 3 Descriptive results of the three latent patterns of perceived stress trajectories Perceived stress trajectory classes F Low-decreasing-increasing Moderate-decreasing-increasing High-stable M SD M SD M SD PS_T1 9.97 a 7.50 14.46 b 6.34 19.6 c 5.20 377.29 ** PS_T2 5.95 a 5.00 12.54 b 4.98 18.95 c 4.40 1027.51 *** PS_T3 3.83 a 3.26 11.69 b 3.98 19.22 c 3.22 2570.25 *** PS_T4 4.83 a 4.43 11.34 b 4.56 18.42 c 3.69 1533.15 *** Note . PS: Perceived stress; Means with various subscripts in a row were significantly different from one another. abc Indication of significant differences between means, with a being the lowest value and c being the highest value. *** p < 0.001. Table 4 Developmental Outcomes at T4 by perceived stress trajectory groups Variables Perceived stress trajectory classes high-stable VS moderate-decreasing-increasing moderate-decreasing-increasing VS low-decreasing-increasing high-stable VS low-decreasing-increasing M dif SE p M dif SE p M dif SE p DS_T4 -0.81 0.07 < 0.001 0.34 0.08 < 0.001 -1.15 0.06 < 0.001 AS_T4 -0.87 0.07 < 0.001 0.15 0.07 < 0.05 -1.02 0.05 < 0.001 LS_T4 0.86 0.06 < 0.001 -0.47 0.11 < 0.001 1.33 0.05 < 0.001 PH_T4 -0.54 0.08 < 0.001 0.282 0.13 0.09 -0.76 0.11 < 0.001 Note . DS: Depressive symptom; AS: Anxiety symptoms; LS: Life Satisfaction; PH: Physical health. Table 5 Multinominal logistic regression odds ratios for baseline predictors Ref. High-stable Ref. Moderate-decreasing-increasing Moderate-decreasing-increasing Low-decreasing-increasing Low-decreasing-increasing Coeff (SE) OR Coeff (SE) OR Coeff (SE) OR Model 1 Gender(ref.girls) 0.28(0.19) 1.32 0.31(0.16)† 1.37 0.59(0.13) *** 1.81 Age -0.05(0.08) 0.95 0.05(0.08) 1.02 -0.03(0.05) 0.97 SSS 0.25(0.09) ** 1.28 0.47(0.08) *** 0.63 -0.22(0.06) *** 0.80 BeiJing(ref.Jiang Su) 0.87(0.37) * 2.38 -1.52(0.33) *** 0.22 -0.65(0.17) *** 0.52 Model2 Gender(ref.girls) -0.62(0.13) *** 0.54 -0.38(0.08)† 0.69 0.24(0.21) 1.27 FG(ref.CG) 0.23(0.15) 1.26 0.01(0.07) 1.01 -0.22(0.21) 0.8 Age 0.04(0.05) 1.05 0.04(0.08) 0.96 0.00(0.00) 0.92 SSS 0.09(0.17) 1.1 0.17(0.1)† 1.18 0.07(0.1) 1.07 BeiJing(ref.Jiang Su) 0.68(0.18) *** 1.98 1.32(0.32) *** 3.73 0.63(0.34)† 1.88 PR 0.51(0.08) *** 1.66 0.92(0.15) *** 2.51 0.41(0.15) ** 1.51 SS 0.59(0.09) *** 1.8 1.29(0.16) *** 3.63 0.7(0.16) *** 2.02 Model3 Gender(ref.girls) -0.62(0.13) *** 0.54 -0.38(0.2)† 0.69 0.24(0.21) 1.27 FG(ref.CG) 0.23(0.12) 1.26 0.001(0.02) 1.01 -0.22(0.21) 0.8 Age 0.05(0.07) 1.05 -0.04(0.08) 0.96 -0.08(0.08) 0.92 SSS 0.1(0.07) 1.11 0.1(0.07)† 1.19 0.07(0.11) 1.08 BeiJing(ref.Jiang Su) 0.69(0.18) *** 1.99 1.31 (0.33) *** 3.69 0.62(0.37)† 1.85 PR 0.5(0.1) *** 1.64 1.01(0.56)† 2.73 0.07(0.11) 0.61 SS 0.58(0.09) *** 1.78 1.37(0.51) ** 3.93 0.51(0.61) 0.56 PR * SS 0.05(0.09) 1.05 -0.07(0.57) 0.93 -0.12(0.6) 0.95 Model4 Gender(ref.girls) -0.6(0.14) *** 0.55 -0.4 (0.2) * 0.67 0.2(0.21) 1.22 FG(ref.CG) 0.19(0.51) 1.21 0.29(0.39) 1.33 0.1(0.42) 1.1 Age 0.04(0.05) 1.04 -0.030(0.09) 0.97 -0.07(0.09) 0.93 SSS 0.1(0.07) 1.11 0.16(0.1) 1.17 0.05(0.1) 1.05 BeiJing(ref.Jiang Su) 0.66 (0.18) *** 1.94 1.42(0.33) *** 4.13 0.76(0.36) * 2.13 PR 0.48 (0.14) *** 1.61 1.24(0.29) *** 3.47 0.77(0.31) * 2.15 SS 0.5 (0.14) *** 1.64 1.53(0.36) *** 4.61 1.03(0.37) ** 2.8 PR * SS 0.1(0.17) 1.1 -0.76(0.34) * 0.47 -0.85(0.38) * 0.43 FG * PR(ref. CG * PR) 0.18(0.18) 1.2 -0.42(0.42) 0.66 -0.6(0.43) 0.55 FG * SS(ref. CG * SS) -0.01(0.12) 0.99 -0.36(0.27) 0.7 -0.35(0.26) 0.71 FG * PR * SS(ref. CG * PR * SS) -0.01(0.16) 1.0 0.91(0.31) ** 2.48 0.91(0.32) ** 2.49 Note . OR: odds ratio, PR: Psychological resilience, SS: Social Support, SSS: subjective socioeconomic status. † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001. Table 6 Multinominal Logistic Regression of Baseline Psychological resilience and Social Support on Trajectory Patterns of Perceived stress among FGCS and CGCS FGCS (n = 1548) CGCS (n = 778) Ref. High-stable Ref. Moderate-decreasing-increasing Ref. High-stable Ref. Moderate-decreasing-increasing Moderate-decreasing-increasing Low-decreasing-increasing Low-decreasing-increasing Moderate-decreasing-increasing Low-decreasing-increasing Low-decreasing-increasing Coeff (SE) OR Coeff (SE) OR Coeff (SE) OR Coeff (SE) OR Coeff (SE) OR Coeff (SE) OR Gender(ref. girls) -0.49(0.13) *** 0.61 -0.48(0.21) * 0.62 0.01(0.22) 1.0 -0.38(0.17) * 0.69 -0.01(0.26) 0.99 0.37(0.26) 1.44 Age 0.09(0.07) 1.1 0.03(0.11) 1.03 -0.06(1.22) 0.94 -0.04(0.05) 0.96 -0.1(0.14) 0.91 -0.06(0.14) 0.95 SSS 0.8(0.06) 1.08 0.18(0.11) 1.19 0.1(0.11) 1.11 0.06(0.11) 1.06 0.75(0.14) 1.08 0.1(0.13) 1.01 PR 0.46(0.08) *** 1.59 0.56(0.18) ** 1.75 0.1(0.19) 1.11 0.42(0.09) *** 1.51 0.94(0.23) *** 2.55 0.52(0.23) 1.68 SS 0.54(0.08) *** 1.72 0.93(0.18) *** 2.52 0.38(0.17) 1.14 0.38(0.1) *** 1.46 1.2(0.24) *** 3.32 0.82(0.24) 2.27 PR * SS 0.06(0.07) 1.06 0.39(0.18) * 1.47 0.33(0.17)† 1.4 0.01(0.08) 1.01 -0.21(0.2) 0.81 -0.21(0.2) 0.81 Note . OR: odds ratio, PR: Psychological resilience, SS: Social Support, SSS: subjective socioeconomic status; FGCS: first-generation college student, CGCS: Continuing-generation college student; † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001. Discussion The transition to higher education represents a period of heightened developmental vulnerability, particularly for FGCS who must navigate novel academic and social ecologies without the intergenerational scaffolding available to their continuing-generation peers. While perceived stress during this transition powerfully shapes subsequent health and adjustment outcomes—and is understood to be malleable to psychosocial resources—existing research has yet to systematically model its longitudinal course, link distinct stress trajectories to long-term well-being, or elucidate the conditional mechanisms through which protective factors operate. Informed by an integrated theoretical perspective drawing on Conservation of Resources Theory and Ecological Systems Theory, this four-wave longitudinal study addresses these gaps by (1) identifying heterogeneous developmental trajectories of perceived stress across the college transition, and (2) examining how psychological resilience and social support—independently and interactively—influence membership in more adaptive pathways. Uncovering Heterogeneous Dynamics and Divergent Windows in Transitional Stress This study moves beyond static, cross-sectional approaches to reconceptualize perceived stress as a dynamic, person-centered process across the college transition. By modeling intra-individual change, three distinct longitudinal trajectories emerged— low-rapid decline-rebound , moderate-gradual decline-stabilization , and high-stable —that empirically challenge linear assumptions of stress adaptation. This evidence refines and extends Transition Cycle Theory, advancing it from a descriptive model toward a predictive framework that directly links dynamic stress patterns to consequential long-term adjustment outcomes. A comparative longitudinal analysis between FGCS and CGCS further revealed a crucial finding. While no initial (T1) or final (T4) mean-level differences were observed, a significant divergence window emerged during the mid-to-late first year (T2 and T3). This pattern indicates that structural disadvantages faced by FGCS—such as gaps in academic cultural capital or social integration challenges—become most salient and detrimental when the initial transition period ends and sustained academic and social demands intensify. The later convergence at T4 is multifaceted, potentially reflecting genuine adaptation, the development of effective compensatory strategies, or a form of survivor bias. Critically, these results delineate the phase-specific nature of equity gaps, identifying the mid-first-year period not merely as a point of heightened stress, but as a developmentally sensitive window for implementing targeted, mechanism-informed interventions to disrupt emerging disparities before they crystallize into chronic trajectories. Developmental Outcomes Associated with Stress Trajectories Membership in distinct stress trajectory groups proved to be a robust predictor of long-term developmental adjustment. Students following the low-rapid decline-rebound pathway consistently demonstrated the most favorable outcomes, reporting the lowest anxiety and depressive symptomatology alongside the highest life satisfaction and self-rated health. Conversely, the high-stable trajectory was persistently linked to the poorest adjustment profile across all measured well-being indicators. These results substantiate the prognostic utility of modeling stress dynamically, while providing empirical grounding for two key developmental tenets: first, that fluctuations in perceived stress constitute a normative, process-oriented component of adaptive adjustment, consistent with transition-cycle models; and second, that a successful developmental transition is characterized not merely by low absolute stress, but by a capacity for effective stress regulation over time, echoing core principles of Positive Youth Development. Critically, baseline stress levels emerged as a significant antecedent of subsequent trajectory membership. This situation underscores the initial transition phase as a developmentally sensitive period during which long-term stress pathways may be established. Consequently, these findings shift the practical emphasis from generalized, reactive stress management toward early identification and proactive, resource-oriented prevention. Implementing systematic screening during this critical window and delivering tailored, strength-building interventions—rather than offering generic support after maladaptive patterns have crystallized—represents a strategically targeted approach to divert at-risk students away from chronic, high-stable stress trajectories . Differential Mechanisms of Protective Factors: FGCS versus CGCS Integrating the Developmental Assets Framework with an ecological-transactional perspective, this study reveals distinct protective mechanisms between FGCS and CGCS. For FGCS, psychological resilience and social support operated in a conditionally coupled manner: resilience significantly attenuated stress only under conditions of high social support. In this subgroup, social support appears to function not merely as an auxiliary resource, but as a critical enabling scaffold that translates resilience into adaptive coping—suggesting that for students navigating structural disadvantages, reliable external support may be essential to activate internal strengths. In contrast, for CGCS, resilience and social support exerted largely independent and additive influences on stress adaptation, consistent with their generally resource-replete ecological contexts. Pre-existing socio-cultural capital and supportive networks likely allow CGCS to draw upon either resource flexibly, without necessitating strong synergy between them. These differential pathways carry clear implications for intervention design. Practical support for FGCS should be ecologically integrated, pairing resilience-skill development with guaranteed access to structured, sustained relational resources—such as formal mentoring programs or identity-affirming peer communities. For CGCS, support may be more effectively delivered through modular and elective formats that accommodate individual preferences and needs. Taken together, the findings argue for moving beyond one-size-fits-all models toward precision support frameworks that are empirically tailored to students′ ecological backgrounds and resource profiles. Contributions, Limitations, and Future Directions This study contributes to the literature in three integrated domains. Theoretically, it advances a dynamic, person-centered perspective on stress adaptation, moving beyond static or group-average approaches to delineate how distinct trajectories of perceived stress unfold during the transition to college. By identifying the conditional effects of psychological resilience and social support—particularly their synergistic role for first-generation students—the findings refine theoretical models of person-context transaction, highlighting subgroup specificity in developmental pathways. Methodologically, the research demonstrates the value of a multi-wave longitudinal design for capturing the temporal dynamics of stress across a pivotal developmental window. This approach permits the modeling of heterogeneous change patterns that would remain obscured in cross-sectional or short-term studies. Practically, the results offer an actionable, evidence-based framework for intervention design in higher education. By mapping differential risk profiles (e.g., the high-stable trajectory) and identifying context-dependent protective mechanisms, the study informs the development of timely, tailored support programs that target both internal assets and external resources—especially for students from under-resourced backgrounds. Several limitations of the present study should be acknowledged, each suggesting productive avenues for further inquiry. First, although the two-year timeframe captures the critical transition period, it does not span the entire undergraduate experience. Longer-term follow-ups help determine whether the identified trajectories stabilize, diverge, or undergo further transitions in later college years and beyond. Second, measuring psychological resilience and social support only at baseline restricts our understanding of how changes in these resources co-evolve with stress over time. Future research would benefit from repeated assessments of protective factors to model bidirectional or reciprocal dynamics. Third, the use of self-reported measures, while well-validated, introduces the possibility of common-method variance. Complementing self-report with behavioral, institutional (e.g., academic records), or physiological indicators would strengthen the validity and scope of the findings. Finally, the sample was drawn from one national context. Cross-cultural replications are needed to examine whether the observed trajectory patterns and protective mechanisms generalize across diverse educational systems, cultural norms, and socioeconomic conditions. Such research would help distinguish universal developmental processes from context-specific pathways. Conclusion This longitudinal study systematically examined heterogeneous trajectories of perceived stress during the college transition and their underlying protective mechanisms, yielding three key advances. First, the research identified three distinct stress adaptation trajectories among incoming college students: high-stable, moderate-gradual-declining-stabilizing, and low-rapidly declining-rebounding. The high-stable trajectory was consistently associated with the most adverse developmental outcomes, including the highest levels of anxiety and depressive symptoms and the lowest life satisfaction and self-rated health. This result not only confirms individual differences in stress adaptation but also highlights the prolonged detrimental effects of chronic high stress on students′ well-being from a developmental perspective. Second, psychological resilience (as an internal asset) and social support (as an external resource) were established as key protective factors influencing these trajectories. Both factors independently predicted more adaptive stress patterns, and their synergistic effect emerged as particularly consequential. Third, and most critically, the protective mechanisms operated differently across student groups. A salient finding was that only among first-generation college students did the combination of high resilience and high social support significantly increase the likelihood of shifting from a high-stable stress pathway to a more adaptive, low-rapidly declining-rebounding trajectory. This outcome delineates the boundary conditions of resource compensation effects and offers a micro-level psychological mechanism through which educational inequities may be perpetuated or mitigated. In summary, by mapping heterogeneous stress trajectories and their conditional protective pathways, this study deepens theoretical understanding of dynamic person–environment interactions. The findings carry direct implications for promoting equity in higher education: integrated interventions that simultaneously foster psychological resilience and strengthen social support systems—particularly tailored for first-generation and other vulnerable student populations—represent a promising strategy for disrupting maladaptive stress cycles and facilitating successful college transition. Declarations Conflict of interest The authors have no conflicts of interest to disclose. Consent for publication Not applicable. Ethics approval and consent to participate This study was approved by the Institutional Review Board of Beijing Normal University (Approval No.: BNU202310200155). All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. Informed consent was obtained from all participants prior to their involvement in the study. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution J.H. participated in conceptualizing the study, conducting statistical analyses, and drafting the manuscript; M.C. helped to interpret the data and edited the manuscript; X.W. participated in data collection, and helped to conduct statistical analyses; S.X. helped to edit and revise the manuscript; M.X. helped to revise and edit the manuscript; Y.B. helped with the visualization of the results and helped to revise and edit the manuscript; Q.C. participated in the design and coordination of the study, provided critical reviews of the manuscript, and contributed to funding acquisition. All authors read and approved the final manuscript. Acknowledgement We extend our sincere gratitude to the university students who participated in this study and to the research assistants for their invaluable support in data collection. 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Supplementary Files 1.StressTrajFGSupplement.docx.docx Cite Share Download PDF Status: Published Journal Publication published 09 Mar, 2026 Read the published version in BMC Psychology → Version 1 posted Editorial decision: Revision requested 28 Jan, 2026 Reviews received at journal 27 Jan, 2026 Reviews received at journal 27 Jan, 2026 Reviews received at journal 24 Jan, 2026 Reviews received at journal 23 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers invited by journal 15 Jan, 2026 Editor assigned by journal 13 Jan, 2026 Editor invited by journal 05 Jan, 2026 Submission checks completed at journal 31 Dec, 2025 First submitted to journal 31 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-8407034","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576421061,"identity":"19a61256-b5eb-4503-b163-78a607f68a7c","order_by":0,"name":"Jing Han","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Han","suffix":""},{"id":576421062,"identity":"9712b039-64bd-41fc-a4f4-bcc9d73d9209","order_by":1,"name":"Meijing Chen","email":"","orcid":"","institution":"Beijing Normal 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University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"BU","suffix":""},{"id":576421066,"identity":"d5874dac-abd2-4554-9a2a-e1496f3f8478","order_by":5,"name":"Qinglin XU","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Qinglin","middleName":"","lastName":"XU","suffix":""},{"id":576421067,"identity":"8ff679b6-af25-4fd8-9948-b7f81050e4aa","order_by":6,"name":"Danhua Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDCCAwxsQNKCh4G9B8xnbCBSiwQPA88ZErUAUQ6RWviOnz32mKdGQsZc8i2QwWAju+EA87MH+LRInslLN+Y5JsFjORvEYEgz3nCAzdwAnxaDAzlm0jxsEjwGt0EMhsOJGw4AuXi1nH8DVPkPqOXmGZCW/0RouQE0nLcNqOUGD0jLAcJaJG+8MZOc2wfUciYvTXKOQbLxzMNsZni18J3PMZN4883G3gAYdBJvKuxk+443P8OrBd2dQMxMgvpRMApGwSgYBdgBADbaQZiANCHvAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Normal University","correspondingAuthor":true,"prefix":"","firstName":"Danhua","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-12-19 17:22:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8407034/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8407034/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40359-026-04265-3","type":"published","date":"2026-03-09T15:59:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100681289,"identity":"8be97f1c-4cf0-43d0-b138-81bc81aede51","added_by":"auto","created_at":"2026-01-20 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12:07:56","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180462,"visible":true,"origin":"","legend":"","description":"","filename":"e36c80dbbaf346bba720dd778bb125751structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8407034/v1/1ab67de3775265c619865234.xml"},{"id":100681418,"identity":"756312a0-f405-4b75-86d6-af0e9e282227","added_by":"auto","created_at":"2026-01-20 12:10:41","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195623,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8407034/v1/b0b81b44e34ad1989f4a08d1.html"},{"id":100681192,"identity":"32ffc365-c876-4327-a1cb-ed5fd4adc912","added_by":"auto","created_at":"2026-01-20 12:07:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80680,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model of the current study\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. PR: Psychological resilience; SS: Social Support; FG: First-generation college student status; DS: Depressive symptoms; AS: Anxiety symptoms; LS: Life Satisfaction; PH: Physical health.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8407034/v1/f92d9515419bd6a7f901bbc9.jpg"},{"id":100681559,"identity":"304917b4-a163-4901-96c5-9fec10928556","added_by":"auto","created_at":"2026-01-20 12:11:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41183,"visible":true,"origin":"","legend":"\u003cp\u003ePerceived stress trajectories of college students\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8407034/v1/7ecc95bd7b4d12678598f4ce.png"},{"id":100681451,"identity":"daff3dce-5620-4448-911f-237cffaa5ec0","added_by":"auto","created_at":"2026-01-20 12:10:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100271,"visible":true,"origin":"","legend":"\u003cp\u003eThe Interaction Effects of Psychological Resilience and Social Support on Perceived Stress Trajectories Across Different Groups.\u003c/p\u003e\n\u003cp\u003eNote. Simple slope plots for (A) FGCS in the comparison between the \u003cem\u003ehigh-stable\u003c/em\u003e and \u003cem\u003elow-rapid decline-rebound\u003c/em\u003e groups; (B) continuing-generation students in the comparison between the \u003cem\u003ehigh-stable\u003c/em\u003eand \u003cem\u003elow-rapid decline-rebound\u003c/em\u003e groups; (C) FGCS in the comparison between the \u003cem\u003emoderate-gradual decline-stabilization\u003c/em\u003e and \u003cem\u003elow-rapid decline-rebound\u003c/em\u003egroups; (D) continuing-generation students in the comparison between the \u003cem\u003emoderate-gradual decline-stabilization\u003c/em\u003e and \u003cem\u003elow-rapid decline-rebound\u003c/em\u003e groups.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8407034/v1/5e4aa8dbe8dc3623c117cf35.jpg"},{"id":104739703,"identity":"9283a447-5bba-4e69-9c00-d00e66403ea1","added_by":"auto","created_at":"2026-03-16 16:12:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2378484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8407034/v1/30e87fb2-94bc-4ea0-974c-f42be25fef4f.pdf"},{"id":100681415,"identity":"7314346f-4baa-4a7a-adc8-6c6bc312ae65","added_by":"auto","created_at":"2026-01-20 12:10:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22747,"visible":true,"origin":"","legend":"","description":"","filename":"1.StressTrajFGSupplement.docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-8407034/v1/4a5b0afe4d5db8fd68f810ff.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal Trajectories of Perceived Stress During College Transition Among First-Generation Students: The Protective Roles of Psychological Resilience and Social Support","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePerceived stress among first-generation college students (FGCS) not only serves as a critical predictor of their attrition risk and academic achievement gaps but also acts as a key mechanism through which intergenerational educational disparities are perpetuated or disrupted (Stebleton et al., 2022; Toutkoushian et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, higher levels of stress are associated with poorer self-rated health, increased symptoms of depression and anxiety, and reduced subjective well-being (Hoyt et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Samaha \u0026amp; Hawi, 2016). Unlike their continuing-generation peers, FGCS navigate the college transition without the benefit of familial academic capital, facing compounded stressors from cultural mismatch, financial precarity, and belonging uncertainty (Bennett et al., 2021; Hicks et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Stebleton et al., 2022; Meza et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, their stress experiences directly shape equity outcomes in higher education and long-term social mobility. Nonetheless, the field remains limited by a reliance on one or two cross-sectional measurements (Smith \u0026amp; Jones, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yeager et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen \u0026amp; Vazsonyi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which offers little insight into the dynamic trajectories of stress\u0026mdash;particularly throughout the critical transition to college. This gap is especially pertinent for FGCS. The college transition represents a period of heightened vulnerability to stress (Pascarella et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), with potential long-term implications for adult adaptation and success (Toutkoushian et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As students navigate intensified environmental demands and developmental challenges during this time (Gerard \u0026amp; Booth, 2015; Lerner, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), their perceived stress is likely to fluctuate dynamically. Therefore, it is essential to investigate not only how perceived stress evolves over time among FGCS but also whether distinct subgroups with unique stress trajectories exist. Stress is likely to diminish for some students after initial adjustment, while others may experience persistent belonging uncertainty and adjustment-related stress that extends into the second year (Brady et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Identifying protective factors associated with these different pathways is crucial for developing targeted interventions that promote positive adaptation and thriving among FGCS. While developmental theories (Lerner, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Gerard \u0026amp; Booth, 2015) and prior research (Stokoe et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) posit that both individual and contextual factors shape stress experiences, few studies have empirically tested how these factors interact over time to influence stress outcomes in FGCS. Furthermore, potential differences in these protective mechanisms between FGCS and CGCS remain underexplored.\u003c/p\u003e \u003cp\u003eTherefore, the present study adopts a longitudinal design to address these gaps and aims to: (1) identify heterogeneous trajectories of perceived stress among Chinese college students, with a focus on FGCS; (2) grounded in resilience theory (Fergus \u0026amp; Zimmerman, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and prior research on social support and capital (Garcia et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xie, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), examine the protective roles of psychological resilience and social support in shaping these trajectories; and (3) investigate potential differences in the effects of these protective factors between FGCS and CGCS.\u003c/p\u003e \u003cp\u003eThis research offers significant theoretical and practical contributions. Theoretically, by simultaneously examining the independent and synergistic effects of internal assets (psychological resilience) and external resources (social support) across a critical developmental transition, this study provides a rigorous test and potential extension of the Developmental Assets Framework and the Theory of Resource Synergy, particularly in the context of non-traditional student populations. It advances understanding of how person-environment transactions dynamically shape adaptation over time. Practically, identifying distinct stress trajectories and their key moderators can directly inform the design of stage-specific and tailored interventions within higher education. For instance, if psychological resilience is found to be a critical buffer in the initial semester, then targeted skill-building programs can be prioritized early. Conversely, if the protective role of social support grows over time, efforts can shift toward strengthening peer networks and family-institution partnerships later in the transition. Such evidence-based insights are crucial for promoting educational equity and enhancing the well-being and success of FGCS.\u003c/p\u003e\n\u003ch3\u003eThe Present Study\u003c/h3\u003e\n\u003cp\u003eGuided by a developmental systems perspective (Lerner, 2018), this two-year longitudinal investigation addresses two overarching aims: (a) to delineate the dynamic trajectories of perceived stress across the college transition, with a specific focus on first-generation college students (FGCS) in China, and (b) to elucidate the person-context mechanisms that shape adaptive versus maladaptive stress pathways. We advance three specific research hypotheses, derived from an integration of developmental, ecological, and resource-based theories, to guide this inquiry.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHypothesis 1: Heterogeneity and Consequences of Perceived Stress Trajectories\u003c/h2\u003e \u003cp\u003eTransition and life-course theories (Schlossberg, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Elder, 1998) posit that developmental adaptation is characterized by systematic intra-individual change and significant inter-individual heterogeneity. While prior research documents an aggregate decline in student stress over time (Zhao et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), mean-level analyses obscure potential subgroup differences and the long-term developmental significance of distinct stress pathways (Nagin, 2005). Drawing from sociological perspectives on cultural capital (Bourdieu, 1986) and ecological models of development (Bronfenbrenner, 1979), we contend that the structural disparities faced by FGCS will manifest in systematically divergent stress dynamics compared to their continuing-generation peers (Stephens et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We hypothesize:\u003c/p\u003e \u003cp\u003eH1a: The longitudinal course of perceived stress will be best characterized by multiple, distinct trajectory classes (e.g., \u003cem\u003ehigh-stable, declining, escalating\u003c/em\u003e) across the first two college years, with the prevalence and form of these classes differing significantly between FGCS and CGCS.\u003c/p\u003e \u003cp\u003eH1b: Membership in a more adaptive stress trajectory class (e.g., \u003cem\u003elow-rapid decline\u003c/em\u003e) will prospectively predict more favorable adjustment outcomes\u0026mdash;including higher life satisfaction, better self-rated health, and lower depressive and anxiety symptoms\u0026mdash;relative to membership in a chronic-high or escalating trajectory (Meeus, 2016).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHypothesis 2: The Protective Role of Developmental Assets\u003c/h3\u003e\n\u003cp\u003eThe Developmental Assets Framework (Benson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) posits that the convergence of internal strengths and external supports fosters positive youth development. Empirical evidence confirms that psychological resilience (Fergus \u0026amp; Zimmerman, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and social support (Cohen \u0026amp; Wills, 1985) individually buffer against stress. However, the extant literature lacks a dynamic, person-centered examination of how these assets predict long-term patterns of stress regulation rather than single-timepoint outcomes (Wang \u0026amp; Degol, 2016). To address this gap, we hypothesize:\u003c/p\u003e \u003cp\u003eH2: Higher baseline levels of psychological resilience and perceived social support will each independently increase the probability of belonging to a more adaptive (e.g., low-declining) stress trajectory over time.\u003c/p\u003e\n\u003ch3\u003eHypothesis 3: Conditionally Coupled Resources and Group-Specific Mechanisms\u003c/h3\u003e\n\u003cp\u003eAn integrated theoretical perspective, synthesizing Resource Synergy Theory (Hobfoll, 2002) and Cultural Mismatch Theory (Stephens et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), informs our final hypothesis. We propose that the functional utility of internal resources is context-dependent. For FGCS, who navigate higher education with less familial cultural capital and face unique structural barriers, robust external support may be a necessary condition for translating psychological resilience into effective stress regulation (Garcia Coll et al., 1996). In contrast, for CGCS, whose ecological niches are typically richer in pre-existing supports and cultural alignment, resilience and social support may function in a more independent or simply additive manner. Thus, we hypothesize a moderated-moderation model:\u003c/p\u003e \u003cp\u003eH3: The interaction between psychological resilience and social support in predicting adaptive stress trajectory membership will be moderated by student generational status. Specifically, for FGCS, high psychological resilience will confer a protective advantage only when coupled with high social support (a conditionally coupled effect). For CGCS, resilience and social support are expected to demonstrate independent or additive effects without such strong synergistic coupling.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipants And Procedure\u003c/h2\u003e \u003cp\u003eData for this study were drawn from a four-wave longitudinal investigation of psychosocial adaptation among Chinese college students. Participants were recruited from three public universities to capture regional diversity: two institutions in Beijing (representing a major metropolitan context) and one in Changzhou, Jiangsu Province (representing a developed eastern region). An online survey was administered at four critical junctures across the first two academic years: at university entry (T1: October 2023), the end of the first semester (T2: March 2024), the beginning of the second year (T3: October 2024), and the end of the third semester (T4: March 2025).\u003c/p\u003e \u003cp\u003eTo ensure data integrity, each survey incorporated three attention-check items. Questionnaires were excluded if they contained more than one failed attention check or were completed in less than five minutes, a duration deemed insufficient for thoughtful response based on pilot testing. At baseline (T1), 2,760 first-year students provided valid responses (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e = 18.18, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.58; 54.3% female). Valid response counts for T2, T3, and T4 were 2,654, 2,496, and 2,326, respectively. For the primary trajectory analyses, which require complete longitudinal data, the final analytic sample consisted of the 2,326 participants with responses at all four waves (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e = 18.16, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.57; 55.6% female). This sample included 1,888 students from Beijing and 438 from Jiangsu. Attrition analysis revealed no significant differences in baseline demographic characteristics (age, gender, generational status) or key study variables (perceived stress, resilience, social support) between participants retained in the analytic sample and those who dropped out at any wave (\u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.10).\u003c/p\u003e \u003cp\u003e Prior to participation, all students provided electronic informed consent via the survey platform. The study protocol received full approval from the Institutional Review Board. The consent form detailed the study's purpose, procedures, risks, benefits, and confidentiality safeguards, and explicitly stated that participation was voluntary and could be withdrawn at any time without consequence. The average completion time per survey wave ranged from 15 to 30 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003ePerceived stress (T1-T4)\u003c/h2\u003e \u003cp\u003ePerceived stress was assessed using the Perceived Stress Scale (PSS; Cohen et al., 1983), which consists of 10 items (e.g., \"How often have you been upset because of something that happened unexpectedly?\"). Participants rated each item on a 5-point Likert scale ranging from 0 (never) to 4 (very often). After reverse-scoring relevant items, a total score was calculated, with higher scores indicating greater levels of perceived stress. In the present study, Cronbach's α\u0026thinsp;=\u0026thinsp;.83\u0026ndash;.88 across waves.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003ePsychological resilience (T1)\u003c/h3\u003e\n\u003cp\u003eParticipants reported their level of psychological resilience using the 10-item Connor-Davidson Resilience Scale (CD-RISC; Connor \u0026amp; Davidson, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Items (e.g., \"I am able to adapt to change\") were rated on a 5-point Likert scale ranging from 1 (\"never\") to 5 (\"always\"). A mean score was calculated, with higher scores reflecting greater resilience. In the current study, the scale demonstrated high internal consistency, with Cronbach's α\u0026thinsp;=\u0026thinsp;.92.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSocial support (T1)\u003c/h2\u003e \u003cp\u003eSocial support (T1) was assessed using the Multidimensional Scale of Perceived Social Support (MSPSS; Zimet, Dahlem, Zimet, \u0026amp; Farley, 1988). This 12-item scale measures support from three sources: family, friends, and a significant other. Participants responded to items (e.g., \"There is a special person who is around when I am in need\") using a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). A mean score was computed, with higher scores indicating greater perceived social support. The scale demonstrated excellent internal consistency in this study, with a Cronbach\u0026prime;s α\u0026thinsp;=\u0026thinsp;.95.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDepressive symptoms (T1 \u0026amp; T4)\u003c/h2\u003e \u003cp\u003eDepressive symptoms at T1/T4 were measured using the Chinese PHQ-9 (Kroenke et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), which aligns with DSM-IV diagnostic criteria. Respondents rated symptom frequency (e.g., \"Feeling down, depressed, or hopeless\") over the past two weeks on a 0\u0026ndash;3 scale. Scores were summed (0\u0026ndash;27 range; higher\u0026thinsp;=\u0026thinsp;greater severity; Cronbach\u0026prime;s α\u0026thinsp;=\u0026thinsp;.90\u0026ndash;.92). Anxiety symptoms were assessed with the Chinese GAD-7 (Spitzer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; DSM-5 criteria). Participants reported how often they felt bothered by symptoms (e.g., \"Feeling nervous, anxious, or on edge\") over the past month on a 0\u0026ndash;3 scale. Total scores (0\u0026ndash;21 range; higher\u0026thinsp;=\u0026thinsp;greater severity; Cronbach\u0026prime;s α\u0026thinsp;=\u0026thinsp;.93\u0026ndash;.94) were used, with a cut-off \u0026ge;\u0026thinsp;7 indicating probable clinical anxiety (Ip et al., 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnxiety symptoms (T1 \u0026amp; T4)\u003c/h2\u003e \u003cp\u003eAnxiety symptoms were measured using the 7-item Generalized Anxiety Disorder scale (GAD-7; Spitzer, Kroenke, Williams, \u0026amp; L\u0026ouml;we, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Respondents rated the frequency of their symptoms over the past month on a 4-point scale ranging from 0 (not at all) to 3 (nearly every day). The total score, derived from the sum of all items, reflects increasing anxiety severity. Consistent with validation studies in Chinese populations (Ip et al., 2022), a cutoff score of 7 was applied, whereby participants scoring above this threshold were categorized as experiencing probable clinical anxiety. The scale showed excellent internal consistency in the current study, with Cronbach\u0026prime;s α values ranging from 0.93 to 0.94 across assessments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLife satisfaction (T1 \u0026amp; T4)\u003c/h2\u003e \u003cp\u003eLife satisfaction was measured using the five-item Satisfaction With Life Scale (SWLS; Diener et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Participants rated their agreement with each item (e.g., \u0026ldquo;In most ways my life is close to ideal\u0026rdquo;) on a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree). A total score was computed by summing all items, with higher scores indicating higher life satisfaction. The scale demonstrated excellent internal consistency across all time points, with Cronbach\u0026prime;s αranging from 0.94 to 0.96.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePhysical health (T2 \u0026amp; T4)\u003c/h2\u003e \u003cp\u003ePhysical health was assessed using the 7-item Index of Somatic Symptoms (ISS; Kellner, 1987). Participants rated the frequency with which they experienced various somatic complaints (e.g., headaches, dizziness, fatigue) on a 5-point scale ranging from 0 (not at all) to 4 (extremely). A total score was computed by summing up all items, with higher scores indicating more severe somatic symptoms. The measure demonstrated good internal consistency in this study, with Cronbach\u0026prime;s α values ranging from 0.87 to 0.91 across time points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDemographic variables\u003c/h2\u003e \u003cp\u003eConsistent with theoretical and empirical precedent (Flanagan \u0026amp; Kornbluh, 2019; Zhao et al., 2024), we controlled for gender (0\u0026thinsp;=\u0026thinsp;female, 1\u0026thinsp;=\u0026thinsp;male), age, generational status (0\u0026thinsp;=\u0026thinsp;continuing-generation, 1\u0026thinsp;=\u0026thinsp;first-generation), subjective socioeconomic status (10-rung MacArthur scale), and family income (categorical). Baseline scores of outcome variables were also included to account for pre-existing differences and strengthen causal inference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData Analytic Strategy\u003c/h2\u003e \u003cp\u003eAll analyses were performed in a two-step sequence. First, descriptive statistics and bivariate correlations among core variables were computed using SPSS 27.0. Second, a comprehensive latent variable modeling framework was implemented in Mplus 8.7, consisting of three sequential stages designed to answer our specific research questions about heterogeneity and mechanisms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStage 1: Identifying Heterogeneous Trajectories of Perceived Stress\u003c/h2\u003e \u003cp\u003eAn unconditional latent growth curve model (LGCM) was first estimated to capture the overall developmental pattern of perceived stress across the four waves. To identify distinct subgroups, we conducted a latent class growth analysis (LCGA). Solutions ranging from two to five latent classes were compared. The time metric was specified by fixing the slope loadings to 0, 1, 2, and 3 to correspond to the baseline (T1), 6-month (T2), 12-month (T3), and 18-month (T4) assessments, respectively. The optimal class solution was selected based on a combination of statistical criteria and theoretical coherence. The following fit indices were examined: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size adjusted BIC (Adj-BIC), bootstrapped likelihood ratio test (BLRT), adjusted Lo-Mendell-Rubin test (Adj-LMR-LRT), and entropy. Model interpretability and the requirement that each class comprise at least 5% of the sample were also decisive factors (Nylund, Asparouhov, \u0026amp; Muth\u0026eacute;n, 2007).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStage 2: Linking Trajectory Membership to Developmental Outcomes\u003c/h2\u003e \u003cp\u003eAfter identifying the optimal trajectory classes, we employed the manual Block-Croon-Hagenaars (BCH) method to examine whether class membership predicted distal outcomes at T4, including life satisfaction, depressive symptoms, anxiety symptoms, and somatic health (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the conceptual diagram). The BCH method, designed explicitly for mixture model distal outcome analysis, maintains class stability by adjusting for classification error and is robust with continuous outcomes (Asparouhov \u0026amp; Muth\u0026eacute;n, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). All analyses in this stage controlled for the corresponding baseline (T1) level of the outcome variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStage 3: Examining Predictors of Trajectory Membership\u003c/h2\u003e \u003cp\u003eTo investigate the protective factors shaping trajectory membership, we used the R3STEP procedure. This step involved a series of nested multinomial logistic regression models within the latent class framework. Model 1 included only demographic covariates (gender, age, urbanicity, subjective socioeconomic status). Model 2 added the main effects of psychological resilience, social support, and generational status (FGCS vs. CGCS). Model 3 introduced the two-way interaction between resilience and social support. Model 4 added the three-way interaction among resilience, social support, and generational status. Following established practice for probing higher-order interactions, if the three-way interaction was significant, we subsequently estimated separate models for the FGCS and CGCS subgroups to examine the two-way interaction within each group (Hayes \u0026amp; Matthes, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). All continuous predictors (resilience, social support) were grand-mean-centered prior to creating interaction terms to mitigate multicollinearity. Simple slope analyses were conducted to decompose significant interaction effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Analyses\u003c/h2\u003e \u003cp\u003eDescriptive statistics and bivariate correlations for all study variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As expected, psychological resilience and perceived social support showed consistent negative associations with perceived stress across all four time points (\u003cem\u003er\u003c/em\u003e values ranged from \u0026minus;\u0026thinsp;.45 to \u0026minus;\u0026thinsp;.30, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). Perceived stress was positively correlated with concurrent depressive and anxiety symptoms at both T1 and T4 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.24 to .54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). Furthermore, higher levels of perceived stress were associated with lower life satisfaction (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.28 to \u0026minus;\u0026thinsp;.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01) and greater somatic symptoms (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.19 to .42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01) at the final assessment (T4). Independent samples t-tests revealed no significant differences in participant age or levels of perceived stress across the three participating universities (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLongitudinal Trajectories of Perceived Stress Among FGCS\u003c/h2\u003e \u003cp\u003eUnconditional latent growth curve modeling (LGCM) was conducted to examine the changes in perceived stress over time. Both linear and quadratic models demonstrated acceptable fit to the data (linear model: \u003cem\u003eχ\u0026sup2;/df\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eCFI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98, \u003cem\u003eTLI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003eRMSEA\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003eSRMR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05; quadratic model: \u003cb\u003eχ\u0026sup2;\u003c/b\u003e\u003cem\u003e/df\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eCFI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.99, \u003cem\u003eTLI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98, \u003cem\u003eRMSEA\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07, \u003cem\u003eSRMR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). Model comparison indicated that the quadratic model provided a significantly better fit than the linear model (Δ\u003cem\u003eχ\u0026sup2;\u003c/em\u003e(4)\u0026thinsp;=\u0026thinsp;59.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and was therefore selected as the final model. Results from the quadratic model revealed that the mean intercept (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17.01, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), mean linear slope (\u003cem\u003eb\u003c/em\u003e = -1.27, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and mean quadratic slope (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all statistically significant.\u003csup\u003e1\u003c/sup\u003e This pattern suggests that, on average, perceived stress levels among college students initially decreased, then experienced a slight rebound, before decreasing again over time. Furthermore, significant variances were found for the intercept (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24.01, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), linear slope (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.59, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and quadratic slope (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.03, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating substantial individual differences in both initial levels of perceived stress and their developmental trajectories. These results suggest possible heterogeneity in perceived stress trajectories, supporting the presence of distinct subgroups following different patterns of change. Accordingly, LCGA was employed to identify unobserved subgroups.\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;2, model fit indices for the two-to five-class LCGA solutions are presented. Although the five-class model demonstrated the best statistical fit, the smallest class comprised less than 5% of the total sample. Therefore, the three-class model was selected as the optimal representation of perceived stress trajectories among college students (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Class 1 (n\u0026thinsp;=\u0026thinsp;1400, 60.19% of the sample) exhibited a high-stable trajectory, characterized by a high initial level of perceived stress (intercept: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.39, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) that remained stable over time (linear slope: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.93; quadratic term: \u003cem\u003eb\u003c/em\u003e=-0.11, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08).Class 2 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;688, 29.58%) followed a \u003cem\u003emoderate-gradual decline-stabilization\u003c/em\u003e pattern, with a \u003cem\u003emoderate baseline\u003c/em\u003e level (intercept: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14.56, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a significant linear decrease (linear slope: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.07, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a gradual stabilization reflected by a positive quadratic trend (quadratic term: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.38, SE\u0026thinsp;=\u0026thinsp;0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).Class 3 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;238, 10.23%) was labeled \u003cem\u003elow-rapid decline-rebound\u003c/em\u003e, showing low initial stress (intercept: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.51, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a sharp initial decline (linear slope: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.62, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a significant rebound toward the end of the observation period (quadratic term: \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.27, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eNote: Although both LCGA and growth mixture modeling (GMM) are widely used techniques form odeling longitudinal heterogeneity, alternative GMM analyses were also conducted. However, these models either failed to converge or did not yield proper solutions; see the Supplementary Materials for further details..\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\u003eDescriptive statistics and correlation among main variables(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2326)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. PS_T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. PS_T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. PS_T3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. PS_T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. PR_T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.43\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.36\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.33\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.30\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. SS_T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.45\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.36\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.34\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.31\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. DS_T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.23\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. AS_T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.16\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.74\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. LS_T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.51\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.61\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.29\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.44\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.36\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. PH_T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.420\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.16\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.58\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.55\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.32\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \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 \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.11\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12. Age_T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13. SSS_T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.11\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.06\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.04\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.06\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14. DS_T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.10\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.43\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.33\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.29\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.27\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15. AS_T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.37\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.34\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.25\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16. LS_T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.56\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.32\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.47\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.39\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17. PH_T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.37\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.24\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.36\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.35\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.29\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.37\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNote\u003c/em\u003e. PS: Perceived stress; PR: Psychological resilience; SS: Social Support; DS: Depressive symptom; AS: Anxiety symptoms; LS: Life Satisfaction; PH: Physical health; SSS: subjective socioeconomic status;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003cp\u003eRepeated-measures ANOVA and paired-samples t-tests were conducted to examine differences in perceived stress across time points among the three trajectory classes. A significant main effect of trajectory class was observed (\u003cem\u003eF\u003c/em\u003e (2, 2323)\u0026thinsp;=\u0026thinsp;3713.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eη\u0026sup2;\u003c/em\u003e = 0.762). \u003cem\u003eThe low-rapid decline-rebound\u003c/em\u003e group reported significantly lower perceived stress at T1 compared to both \u003cem\u003ethe moderate-gradual decline-stabilization\u003c/em\u003e group (\u003cem\u003eM\u003c/em\u003e\u003csub\u003ediff\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003ethe high-stable group\u003c/em\u003e (\u003cem\u003eM\u003c/em\u003e\u003csub\u003ediff\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;9.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Post-hoc McNemar tests indicated that perceived stress levels at T2 and T3 were significantly lower than at T1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although a slight rebound occurred at T4 relative to T3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), T4 levels remained significantly lower than those at T1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and did not differ significantly from T2 or T3 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The \u003cem\u003emoderate-gradual decline-stabilization\u003c/em\u003e group showed significantly lower levels of stress at T1 than the \u003cem\u003ehigh-stable group\u003c/em\u003e (\u003cem\u003eM\u003c/em\u003e\u003csub\u003ediff\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and exhibited a significant decreasing trend over time (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the \u003cem\u003ehigh-stable group\u003c/em\u003e maintained consistently high stress levels across all time points with no significant change (\u003cem\u003eF\u003c/em\u003e (2.77, 2323)\u0026thinsp;=\u0026thinsp;3868.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.336).\u003c/p\u003e \u003cp\u003eThe trajectories of perceived stress differed significantly between FGCS and CGCS across the four time points. Independent-samples t-tests were used to compare perceived stress between FGCS and CGCS at each time point. No significant differences were found at T1 (\u003cem\u003et\u003c/em\u003e (1417.41)\u0026thinsp;=\u0026thinsp;1.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.093) or T4 (\u003cem\u003et\u003c/em\u003e (1434.20)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.967). However, FGCS reported significantly higher perceived stress than their continuing-generation peers at both T2 (\u003cem\u003et\u003c/em\u003e (1422.29)\u0026thinsp;=\u0026thinsp;2.90, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and T3 (\u003cem\u003et\u003c/em\u003e (1393.18)\u0026thinsp;=\u0026thinsp;2.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021).\u003c/p\u003e \u003cp\u003eT3. Table\u0026nbsp;2 Model fit statistics for latent class growth analyses results (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2326)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClasses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdj-BIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdj-LMR-LRT\u003c/p\u003e \u003cp\u003e(\u003cem\u003ep\u003c/em\u003e value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLRT\u003c/p\u003e \u003cp\u003e(\u003cem\u003ep\u003c/em\u003e value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSCN(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58195.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58258.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58223.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e673(28.93%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e57773.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e57859.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e57812.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e238(10.23%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57642.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57751.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57691.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e69(2.97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57488.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57621.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57548.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e89(3.83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. The final class solution is bolded. AIC: Akaike information criterion, BIC: Bayesian information criterion, Adj-BIC: sample-size adjusted Bayesian information criterion, Adj-LMR-LRT: adjusted Lo-Mendell-Rubin likelihood test, BLRT: bootstrapped likelihood ratio test, SC: smallest class size.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eTrajectories Perceived Stress and Associations with Outcomes\u003c/h2\u003e \u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the BCH method revealed significant differences across trajectory classes in all outcome measures. The \u003cem\u003elow-decreasing-increasing group\u003c/em\u003e demonstrated the most favorable outcomes, including the highest life satisfaction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the lowest levels of depressive (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and anxiety symptoms (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the best physical health (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the \u003cem\u003ehigh-stable group\u003c/em\u003e exhibited the lowest life satisfaction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the highest depressive (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and anxiety symptoms (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the poorest physical health (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eEffects of Psychological Resilience, Social Support, and FGCS Status on Trajectories of Perceived Stress\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the descriptive results of the three latent trajectory patterns of perceived stress. Based on these patterns, we employed the R3STEP approach to examine the effects of psychological resilience, social support, and FGCS status on trajectory membership, controlling for demographic covariates such as gender, age, urban location, and SSS (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Both psychological resilience(\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and social support (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed significant main effects on trajectory classification. Specifically, individuals with higher resilience or social support were more likely to belong to the \u003cem\u003elow-rapid decline-rebound group\u003c/em\u003e compared to either the \u003cem\u003ehigh-stable group\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or the \u003cem\u003emoderate-gradual decline-stabilization group\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, no significant interaction was found between resilience and social support on trajectory(\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.844).\u003c/p\u003e \u003cp\u003eModel 4 indicated a significant three-way interaction among psychological resilience, social support, and FGCS status when comparing the \u003cem\u003ehigh-stable group\u003c/em\u003e with the \u003cem\u003elow-rapid decline-rebound group\u003c/em\u003e, and the \u003cem\u003emoderate-gradual decline stabilization group\u003c/em\u003e with the \u003cem\u003elow-rapid decline-rebound group\u003c/em\u003e. Further analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) revealed a significant two-way interaction between resilience and social support only among FGCS when comparing the \u003cem\u003ehigh-stable\u003c/em\u003e and \u003cem\u003elow-rapid decline-rebound\u003c/em\u003e trajectories (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), which was not observed among continuing-generation students. Simple slope analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that, among FGCS with high resilience (i.e., 1 \u003cem\u003eSD\u003c/em\u003e above the mean), higher social support significantly predicted membership in the \u003cem\u003elow-rapid decline-rebound group\u003c/em\u003e compared to the \u003cem\u003ehigh-stable\u003c/em\u003e group (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032). Similarly, a marginally significant effect was found for the comparison between the \u003cem\u003elow-rapid decline-rebound\u003c/em\u003e and \u003cem\u003emoderate-gradual decline-stabilization\u003c/em\u003e groups among highly resilient FGCS (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.40, p\u0026thinsp;=\u0026thinsp;0.053). In contrast, no significant interaction was detected among continuing-generation students (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.988).\u003c/p\u003e \u003cp\u003e \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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive results of the three latent patterns of perceived stress trajectories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003ePerceived stress trajectory classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLow-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModerate-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHigh-stable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS_T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.97\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.46\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.6\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e377.29\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS_T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.95\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.54\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.95\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1027.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS_T3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.83\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.69\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.22\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2570.25\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS_T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.83\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.34\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.42\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1533.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. PS: Perceived stress; Means with various subscripts in a row were significantly different from one another.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csub\u003eabc\u003c/sub\u003eIndication of significant differences between means, with a being the lowest value and c being the highest value. ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDevelopmental Outcomes at T4 by perceived stress trajectory groups\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=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c11\" namest=\"c2\"\u003e \u003cp\u003ePerceived stress trajectory classes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ehigh-stable VS moderate-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003emoderate-decreasing-increasing VS low-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003ehigh-stable VS low-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003edif\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003edif\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003edif\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSE\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\u003eDS_T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAS_T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLS_T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH_T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eNote\u003c/em\u003e. DS: Depressive symptom; AS: Anxiety symptoms; LS: Life Satisfaction; PH: Physical health.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinominal logistic regression odds ratios for baseline predictors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eRef. High-stable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eRef. Moderate-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModerate-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eLow-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eLow-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCoeff (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCoeff (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCoeff (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(ref.girls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31(0.16)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.59(0.13)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.05(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.25(0.09)\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.47(0.08)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.22(0.06)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeiJing(ref.Jiang Su)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.87(0.37)\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-1.52(0.33)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.65(0.17)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(ref.girls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.62(0.13)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.38(0.08)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFG(ref.CG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.22(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17(0.1)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07(0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeiJing(ref.Jiang Su)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.68(0.18)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.32(0.32)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63(0.34)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.51(0.08)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.92(0.15)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.41(0.15)\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.59(0.09)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.29(0.16)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.7(0.16)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(ref.girls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.62(0.13)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.38(0.2)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFG(ref.CG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.22(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.08(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1(0.07)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeiJing(ref.Jiang Su)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.69(0.18)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003cb\u003e(0.33)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.62(0.37)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.5(0.1)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01(0.56)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.58(0.09)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.37(0.51)\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.51(0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003csup\u003e*\u003c/sup\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07(0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.12(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(ref.girls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.6(0.14)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4\u003cb\u003e(0.2)\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFG(ref.CG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19(0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.030(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.07(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16(0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05(0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeiJing(ref.Jiang Su)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003cb\u003e(0.18)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42(0.33)\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.76(0.36)\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003cb\u003e(0.14)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.24(0.29)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.77(0.31)\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003cb\u003e(0.14)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.53(0.36)\u003c/b\u003e\u003csup\u003e\u003cb\u003e***\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.03(0.37)\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003csup\u003e*\u003c/sup\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.76(0.34)\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.85(0.38)\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFG\u003csup\u003e*\u003c/sup\u003ePR(ref. CG\u003csup\u003e*\u003c/sup\u003ePR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18(0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.42(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.6(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFG\u003csup\u003e*\u003c/sup\u003eSS(ref. CG\u003csup\u003e*\u003c/sup\u003eSS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.36(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.35(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFG\u003csup\u003e*\u003c/sup\u003ePR\u003csup\u003e*\u003c/sup\u003eSS(ref. CG\u003csup\u003e*\u003c/sup\u003ePR\u003csup\u003e*\u003c/sup\u003eSS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01(0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.91(0.31)\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.91(0.32)\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote\u003c/em\u003e. OR: odds ratio, PR: Psychological resilience, SS: Social Support, SSS: subjective socioeconomic status. \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinominal Logistic Regression of Baseline Psychological resilience and Social Support on Trajectory Patterns of Perceived stress among FGCS and CGCS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eFGCS (n\u0026thinsp;=\u0026thinsp;1548)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eCGCS (n\u0026thinsp;=\u0026thinsp;778)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eRef. High-stable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRef. Moderate-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eRef. High-stable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eRef. Moderate-decreasing-increasing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModerate-decreasing-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLow-decreasing-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLow-decreasing-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModerate-decreasing-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eLow-decreasing-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eLow-decreasing-increasing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCoeff (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCoeff (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCoeff (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCoeff (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eCoeff (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eCoeff (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(ref. girls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.49(0.13)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.48(0.21)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.38(0.17)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.01(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.37(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.06(1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.04(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.1(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.06(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.75(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.1(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46(0.08)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56(0.18)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42(0.09)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.94(0.23)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.52(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54(0.08)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93(0.18)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38(0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.38(0.1)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.2(0.24)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.82(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003csup\u003e*\u003c/sup\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39(0.18)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33(0.17)\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.21(0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.21(0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNote\u003c/em\u003e. OR: odds ratio, PR: Psychological resilience, SS: Social Support, SSS: subjective socioeconomic status;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eFGCS: first-generation college student, CGCS: Continuing-generation college student;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e\u0026dagger;\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe transition to higher education represents a period of heightened developmental vulnerability, particularly for FGCS who must navigate novel academic and social ecologies without the intergenerational scaffolding available to their continuing-generation peers. While perceived stress during this transition powerfully shapes subsequent health and adjustment outcomes\u0026mdash;and is understood to be malleable to psychosocial resources\u0026mdash;existing research has yet to systematically model its longitudinal course, link distinct stress trajectories to long-term well-being, or elucidate the conditional mechanisms through which protective factors operate. Informed by an integrated theoretical perspective drawing on Conservation of Resources Theory and Ecological Systems Theory, this four-wave longitudinal study addresses these gaps by (1) identifying heterogeneous developmental trajectories of perceived stress across the college transition, and (2) examining how psychological resilience and social support\u0026mdash;independently and interactively\u0026mdash;influence membership in more adaptive pathways.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eUncovering Heterogeneous Dynamics and Divergent Windows in Transitional Stress\u003c/h2\u003e \u003cp\u003eThis study moves beyond static, cross-sectional approaches to reconceptualize perceived stress as a dynamic, person-centered process across the college transition. By modeling intra-individual change, three distinct longitudinal trajectories emerged\u0026mdash;\u003cem\u003elow-rapid decline-rebound\u003c/em\u003e, \u003cem\u003emoderate-gradual decline-stabilization\u003c/em\u003e, and \u003cem\u003ehigh-stable\u003c/em\u003e\u0026mdash;that empirically challenge linear assumptions of stress adaptation. This evidence refines and extends Transition Cycle Theory, advancing it from a descriptive model toward a predictive framework that directly links dynamic stress patterns to consequential long-term adjustment outcomes.\u003c/p\u003e \u003cp\u003eA comparative longitudinal analysis between FGCS and CGCS further revealed a crucial finding. While no initial (T1) or final (T4) mean-level differences were observed, a significant divergence window emerged during the mid-to-late first year (T2 and T3). This pattern indicates that structural disadvantages faced by FGCS\u0026mdash;such as gaps in academic cultural capital or social integration challenges\u0026mdash;become most salient and detrimental when the initial transition period ends and sustained academic and social demands intensify. The later convergence at T4 is multifaceted, potentially reflecting genuine adaptation, the development of effective compensatory strategies, or a form of survivor bias. Critically, these results delineate the phase-specific nature of equity gaps, identifying the mid-first-year period not merely as a point of heightened stress, but as a developmentally sensitive window for implementing targeted, mechanism-informed interventions to disrupt emerging disparities before they crystallize into chronic trajectories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eDevelopmental Outcomes Associated with Stress Trajectories\u003c/h2\u003e \u003cp\u003eMembership in distinct stress trajectory groups proved to be a robust predictor of long-term developmental adjustment. Students following the \u003cem\u003elow-rapid decline-rebound\u003c/em\u003e pathway consistently demonstrated the most favorable outcomes, reporting the lowest anxiety and depressive symptomatology alongside the highest life satisfaction and self-rated health. Conversely, the \u003cem\u003ehigh-stable trajectory\u003c/em\u003e was persistently linked to the poorest adjustment profile across all measured well-being indicators. These results substantiate the prognostic utility of modeling stress dynamically, while providing empirical grounding for two key developmental tenets: first, that fluctuations in perceived stress constitute a normative, process-oriented component of adaptive adjustment, consistent with transition-cycle models; and second, that a successful developmental transition is characterized not merely by low absolute stress, but by a capacity for effective stress regulation over time, echoing core principles of Positive Youth Development.\u003c/p\u003e \u003cp\u003eCritically, baseline stress levels emerged as a significant antecedent of subsequent trajectory membership. This situation underscores the initial transition phase as a developmentally sensitive period during which long-term stress pathways may be established. Consequently, these findings shift the practical emphasis from generalized, reactive stress management toward early identification and proactive, resource-oriented prevention. Implementing systematic screening during this critical window and delivering tailored, strength-building interventions\u0026mdash;rather than offering generic support after maladaptive patterns have crystallized\u0026mdash;represents a strategically targeted approach to divert at-risk students away from chronic, \u003cem\u003ehigh-stable stress trajectories\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Mechanisms of Protective Factors: FGCS versus CGCS\u003c/h2\u003e \u003cp\u003eIntegrating the Developmental Assets Framework with an ecological-transactional perspective, this study reveals distinct protective mechanisms between FGCS and CGCS. For FGCS, psychological resilience and social support operated in a conditionally coupled manner: resilience significantly attenuated stress only under conditions of high social support. In this subgroup, social support appears to function not merely as an auxiliary resource, but as a critical enabling scaffold that translates resilience into adaptive coping\u0026mdash;suggesting that for students navigating structural disadvantages, reliable external support may be essential to activate internal strengths.\u003c/p\u003e \u003cp\u003eIn contrast, for CGCS, resilience and social support exerted largely independent and additive influences on stress adaptation, consistent with their generally resource-replete ecological contexts. Pre-existing socio-cultural capital and supportive networks likely allow CGCS to draw upon either resource flexibly, without necessitating strong synergy between them.\u003c/p\u003e \u003cp\u003eThese differential pathways carry clear implications for intervention design. Practical support for FGCS should be ecologically integrated, pairing resilience-skill development with guaranteed access to structured, sustained relational resources\u0026mdash;such as formal mentoring programs or identity-affirming peer communities. For CGCS, support may be more effectively delivered through modular and elective formats that accommodate individual preferences and needs. Taken together, the findings argue for moving beyond one-size-fits-all models toward precision support frameworks that are empirically tailored to students\u0026prime; ecological backgrounds and resource profiles.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eContributions, Limitations, and Future Directions\u003c/h3\u003e\n\u003cp\u003eThis study contributes to the literature in three integrated domains. Theoretically, it advances a dynamic, person-centered perspective on stress adaptation, moving beyond static or group-average approaches to delineate how distinct trajectories of perceived stress unfold during the transition to college. By identifying the conditional effects of psychological resilience and social support\u0026mdash;particularly their synergistic role for first-generation students\u0026mdash;the findings refine theoretical models of person-context transaction, highlighting subgroup specificity in developmental pathways. Methodologically, the research demonstrates the value of a multi-wave longitudinal design for capturing the temporal dynamics of stress across a pivotal developmental window. This approach permits the modeling of heterogeneous change patterns that would remain obscured in cross-sectional or short-term studies. Practically, the results offer an actionable, evidence-based framework for intervention design in higher education. By mapping differential risk profiles (e.g., the high-stable trajectory) and identifying context-dependent protective mechanisms, the study informs the development of timely, tailored support programs that target both internal assets and external resources\u0026mdash;especially for students from under-resourced backgrounds.\u003c/p\u003e \u003cp\u003eSeveral limitations of the present study should be acknowledged, each suggesting productive avenues for further inquiry. First, although the two-year timeframe captures the critical transition period, it does not span the entire undergraduate experience. Longer-term follow-ups help determine whether the identified trajectories stabilize, diverge, or undergo further transitions in later college years and beyond. Second, measuring psychological resilience and social support only at baseline restricts our understanding of how changes in these resources co-evolve with stress over time. Future research would benefit from repeated assessments of protective factors to model bidirectional or reciprocal dynamics. Third, the use of self-reported measures, while well-validated, introduces the possibility of common-method variance. Complementing self-report with behavioral, institutional (e.g., academic records), or physiological indicators would strengthen the validity and scope of the findings. Finally, the sample was drawn from one national context. Cross-cultural replications are needed to examine whether the observed trajectory patterns and protective mechanisms generalize across diverse educational systems, cultural norms, and socioeconomic conditions. Such research would help distinguish universal developmental processes from context-specific pathways.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis longitudinal study systematically examined heterogeneous trajectories of perceived stress during the college transition and their underlying protective mechanisms, yielding three key advances. First, the research identified three distinct stress adaptation trajectories among incoming college students: high-stable, moderate-gradual-declining-stabilizing, and low-rapidly declining-rebounding. The high-stable trajectory was consistently associated with the most adverse developmental outcomes, including the highest levels of anxiety and depressive symptoms and the lowest life satisfaction and self-rated health. This result not only confirms individual differences in stress adaptation but also highlights the prolonged detrimental effects of chronic high stress on students\u0026prime; well-being from a developmental perspective. Second, psychological resilience (as an internal asset) and social support (as an external resource) were established as key protective factors influencing these trajectories. Both factors independently predicted more adaptive stress patterns, and their synergistic effect emerged as particularly consequential. Third, and most critically, the protective mechanisms operated differently across student groups. A salient finding was that only among first-generation college students did the combination of high resilience and high social support significantly increase the likelihood of shifting from a high-stable stress pathway to a more adaptive, low-rapidly declining-rebounding trajectory. This outcome delineates the boundary conditions of resource compensation effects and offers a micro-level psychological mechanism through which educational inequities may be perpetuated or mitigated.\u003c/p\u003e \u003cp\u003eIn summary, by mapping heterogeneous stress trajectories and their conditional protective pathways, this study deepens theoretical understanding of dynamic person\u0026ndash;environment interactions. The findings carry direct implications for promoting equity in higher education: integrated interventions that simultaneously foster psychological resilience and strengthen social support systems\u0026mdash;particularly tailored for first-generation and other vulnerable student populations\u0026mdash;represent a promising strategy for disrupting maladaptive stress cycles and facilitating successful college transition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study was approved by the Institutional Review Board of Beijing Normal University (Approval No.: BNU202310200155). All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. Informed consent was obtained from all participants prior to their involvement in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.H. participated in conceptualizing the study, conducting statistical analyses, and drafting the manuscript; M.C. helped to interpret the data and edited the manuscript; X.W. participated in data collection, and helped to conduct statistical analyses; S.X. helped to edit and revise the manuscript; M.X. helped to revise and edit the manuscript; Y.B. helped with the visualization of the results and helped to revise and edit the manuscript; Q.C. participated in the design and coordination of the study, provided critical reviews of the manuscript, and contributed to funding acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe extend our sincere gratitude to the university students who participated in this study and to the research assistants for their invaluable support in data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting this research are not publicly accessible but can be provided by the corresponding author upon justified request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAsparouhov T, Muth\u0026eacute;n B. Auxiliary variables in mixture modeling: Three-step approaches using Mplus. 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J Youth Adolesc. 2023;52(8):1873\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10964-023-01788-5\u003c/span\u003e\u003cspan address=\"10.1007/s10964-023-01788-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"First-generation college students, transition to college, Perceived stress, Psychological resilience, Social support","lastPublishedDoi":"10.21203/rs.3.rs-8407034/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8407034/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe transition to college is a critical period for first-generation college students (FGCS), during which their perceived stress significantly influences developmental outcomes, including psychological well-being and physical health. However, this longitudinal study was conducted across four waves (October 2023, March 2024, October 2024, March 2025) to investigate these patterns in a sample of Chinese university students (\u003cem\u003eM\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e= 18.16, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.87; 52.5% male; 66.55% FGCS). We identified three distinct stress trajectories using latent class growth analysis: \u003cem\u003ea low-rapidly declining-rebounding trajectory\u003c/em\u003e (10.23%), \u003cem\u003ea moderate-gradual declining-stabilizing trajectory\u003c/em\u003e (29.58%), and \u003cem\u003ea high-stable trajectory\u003c/em\u003e (60.19%). Students in the \u003cem\u003elow-rebounding trajectory\u003c/em\u003e reported the most favorable outcomes (lowest anxiety and depression, highest life satisfaction and physical health), whereas those in the \u003cem\u003ehigh-stable trajectory\u003c/em\u003e reported the poorest outcomes. Beyond the significant main effects of psychological resilience and social support, a significant interaction was observed. Specifically, FGCS with concurrently high levels of both resilience and social support were more likely to belong to the low-rebounding trajectory than the high-stable class. These findings underscore the dynamic nature of perceived stress and the synergistic role of protective factors in fostering positive adaptation among FGCS, offering valuable insights for developing individualized interventions.\u003c/p\u003e","manuscriptTitle":"Longitudinal Trajectories of Perceived Stress During College Transition Among First-Generation Students: The Protective Roles of Psychological Resilience and Social Support","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 10:24:31","doi":"10.21203/rs.3.rs-8407034/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-28T07:51:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T18:51:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T05:53:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T12:40:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T00:42:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T00:25:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103377999745428510576306607162559679129","date":"2026-01-18T16:42:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303387930862470315436922035684995139395","date":"2026-01-18T02:56:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262699644088477653324994968866554748479","date":"2026-01-16T17:32:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334141047603722688638314605811436119691","date":"2026-01-15T22:07:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16419210539312087640513908585412838396","date":"2026-01-15T11:17:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190923395934060931104183496746354760491","date":"2026-01-15T06:33:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-15T06:13:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T03:29:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-05T17:15:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-31T11:24:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-12-31T11:10:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2873274-03fd-4e5d-9ca8-41de8b0d176d","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:07:38+00:00","versionOfRecord":{"articleIdentity":"rs-8407034","link":"https://doi.org/10.1186/s40359-026-04265-3","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2026-03-09 15:59:08","publishedOnDateReadable":"March 9th, 2026"},"versionCreatedAt":"2026-01-20 10:24:31","video":"","vorDoi":"10.1186/s40359-026-04265-3","vorDoiUrl":"https://doi.org/10.1186/s40359-026-04265-3","workflowStages":[]},"version":"v1","identity":"rs-8407034","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8407034","identity":"rs-8407034","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}