Psychosocial Profiles of Family Caregivers for Colorectal Cancer Patients with Permanent Ostomies: A Latent Class Analysis of Resilience, Depression, and Self-Efficacy

Psychosocial Profiles of Family Caregivers for Colorectal Cancer Patients with Permanent Ostomies: A Latent Class Analysis of Resilience, Depression, and Self-Efficacy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Psychosocial Profiles of Family Caregivers for Colorectal Cancer Patients with Permanent Ostomies: A Latent Class Analysis of Resilience, Depression, and Self-Efficacy Sheng-Hua Jia, Jia Qiao, Songmei Cao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7574295/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To identify latent psychosocial profiles among colorectal cancer caregivers based on resilience, depression, and caregiving efficacy, and to examine the sociodemographic predictors and outcome differences associated with each subgroup. Methods A cross-sectional survey was conducted among 579 family caregivers in China. Standardized instruments assessed resilience, depression, caregiving self-efficacy, and perceived family support. Latent class analysis was used to identify caregiver profiles. Multinomial logistic regression examined sociodemographic predictors. Differences in psychological outcomes were compared across classes using Kruskal–Wallis tests with Bonferroni-adjusted post hoc analysis. Results Four distinct caregiver profiles emerged, characterized respectively by (1) low resilience with high burden, (2) high perceived support but low internal resources, (3) high resilience and self-efficacy with low distress, and (4) enduring caregiving with moderate emotional strain. Education, income, employment status, and caregiving relationship were significant predictors of class membership. Psychological outcomes—including depression, efficacy, resilience, and family support—varied significantly across profiles (all p < 0.001). Conclusions Caregivers differ widely in coping patterns and risk levels. Psychosocial profiling offers a more person-centered understanding of caregiver needs than traditional unidimensional assessments. Targeted support based on profile-specific risks may improve caregiver outcomes and optimize oncology nursing care. Colorectal cancer Family caregivers Ostomy care Resilience Self-efficacy Latent class analysis Figures Figure 1 Figure 2 1. Introduction Family caregivers are an indispensable component of cancer care, particularly for patients requiring complex, long-term support. In colorectal cancer, the presence of a permanent ostomy introduces unique caregiving challenges—ranging from stoma management to emotional adjustment—which can place significant strain on informal caregivers [ 1 ]. Extensive literature has documented the psychological toll of caregiving, including elevated levels of depression, anxiety, and caregiver fatigue [ 2 , 3 ]. These adverse outcomes not only affect caregiver well-being but may also compromise care quality and patient recovery [ 4 ]. Despite this recognition, most research continues to treat caregivers as a homogeneous population, focusing primarily on risk factors for burden or distress [ 5 , 6 ]. This variable-centered approach often overlooks the nuanced ways in which individuals differ in their capacity to cope with caregiving demands [ 7 ]. In reality, caregivers vary widely in their psychological resilience, caregiving efficacy, and access to support. Some adapt effectively even under chronic stress, while others struggle despite favorable external circumstances [ 8 ]. Resilience, broadly defined as the ability to maintain or regain psychological well-being in the face of adversity, has emerged as a critical but underexplored construct in caregiving research [ 9 ]. While a growing body of evidence highlights its protective role, few studies have examined how resilience interacts with perceived burden and caregiving competence across different subgroups [ 10 ]. Furthermore, reliance on aggregate or mean scores may obscure important patterns of heterogeneity that are clinically meaningful [ 11 ]. Person-centered approaches, such as latent class analysis (LCA), offer a promising framework to address this gap [ 12 ]. Rather than assuming uniformity, LCA identifies distinct caregiver profiles based on shared response patterns, allowing for a more nuanced understanding of caregiving experiences. Such classifications can inform the design of targeted interventions, optimize resource allocation, and support the move toward precision psycho-oncology [ 13 ]. Accordingly, this study aimed to identify distinct psychosocial profiles among family caregivers of colorectal cancer patients with permanent ostomies, based on their levels of resilience, psychological burden, and caregiving self-efficacy. We further examined the sociodemographic characteristics associated with each profile and discussed the implications for individualized caregiver support in oncology nursing. By capturing the complexity and variability of caregiving, this research seeks to contribute to more equitable, person-centered approaches to cancer care delivery. 2. Methods 2.1 Study Design and Participants This cross-sectional study employed LCA to identify distinct psychosocial subgroups among family caregivers of colorectal cancer patients with permanent ostomies. Participants were recruited between January and July 2025 from the colorectal cancer follow-up clinics and stoma care outpatient services at four tertiary hospitals in Qingdao, China. Ethical approval for this study was obtained from the Ethics Committee of Qingdao Municipal Hospital (Approval No. 2025-KY-049), and all procedures were conducted in accordance with the Declaration of Helsinki [14]. Written informed consent was obtained from all participants prior to data collection. A total of 620 questionnaires were distributed, of which 593 were returned (response rate: 95.6%). After excluding incomplete or invalid responses, 579 questionnaires were deemed valid and included in the final analysis (validity rate: 97.6%). 2.1.1 Eligible participants were: · Primary family caregivers (i.e., the person providing the majority of daily care) of patients who had undergone permanent colostomy or ileostomy for colorectal cancer; Aged 18 years or older; Able to communicate in Mandarin and provide informed consent; Not receiving financial compensation for caregiving (non-professional caregivers). 2.1.2 Caregivers were excluded if they: Had clinically diagnosed cognitive impairment or mental illness; Were concurrently caring for multiple patients. Following recommendations for LCA with continuous indicators [15], we assumed approximately 8–10 parameters per latent class (e.g., class-specific means and class membership probabilities). For a 4-class model, this leads to approximately 40 estimated parameters. Based on the general rule of thumb requiring 10–20 participants per parameter, a minimum sample size of 400–800 was required. 2.2 Measures (1) Connor–Davidson Resilience Scale (Tenacity Subscale) Psychological resilience was assessed using the Tenacity subscale of the Chinese version of the Connor–Davidson Resilience Scale (CD-RISC) [16]. This subscale captures core aspects of resilience related to perseverance, goal orientation, and self-confidence under stress. It includes 13 items, each rated on a 5-point Likert scale ranging from 0 (not true at all) to 4 (true nearly all the time), with total scores ranging from 0 to 52. Higher scores reflect greater tenacity in the face of adversity. The Tenacity subscale has demonstrated strong internal consistency and construct validity in Chinese populations. Prior research has shown that this subscale is particularly relevant for assessing trait-like resilience in caregivers and individuals managing chronic illness [17]. (2) Hospital Anxiety and Depression Scale (Depression subscale) Depressive symptoms were measured using the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D), developed by Zigmond, Snaith [18]. The HADS-D is a self-report instrument designed to assess depressive symptoms in non-psychiatric populations, particularly in medical and cancer care settings. It avoids somatic items (e.g., fatigue, appetite) to reduce confounding with physical illness symptoms. The depression subscale consists of 7 items, each rated on a 4-point Likert scale (0–3), with total scores ranging from 0 to 21. Higher scores indicate more severe depressive symptoms, with commonly used cutoffs of 0–7 (normal), 8–10 (borderline), and 11–21 (clinical level depression). The HADS-D has been widely used in oncology and caregiver research and shows good internal consistency, test-retest reliability, and convergent validity. A validated Chinese version has demonstrated strong psychometric properties in Chinese caregiver populations [19]. (3) Caregiving Self-Efficacy (Behavior Subscale) Caregiving self-efficacy was assessed using the Behavior Subscale of the Caregiver Self-Efficacy Scale, originally developed by Steffen et al. [20]. This subscale specifically measures caregivers’ perceived confidence in managing behavioral and practical challenges associated with caregiving tasks, which is particularly relevant in cancer care contexts. The subscale consists of five items, each rated on a 10-point Likert scale ranging from 0 (not at all confident) to 9 (completely confident). Items assess caregivers’ confidence in handling difficult behaviors, maintaining emotional control, and implementing caregiving strategies effectively. The total score ranges from 0 to 45, with higher scores indicating greater self-efficacy in caregiving behavior management. This scale has demonstrated good internal consistency and construct validity, and the Chinese version used in this study has been validated in prior studies involving cancer caregivers [21]. (4) Multidimensional Scale of Perceived Social Support (Family Support Subscale) Perceived family support was assessed using the Family subscale of the Multidimensional Scale of Perceived Social Support (MSPSS) [22]. The MSPSS is a widely used 12-item instrument that measures perceived social support from three sources: family, friends, and significant others, with each subscale comprising 4 items. The family subscale specifically evaluates the extent to which individuals perceive their family members as available, caring, and supportive. Sample items include “My family really tries to help me” and “I get the emotional help and support I need from my family.” Participants rate each item on a 7-point Likert scale ranging from 1 = very strongly disagree to 7 = very strongly agree, with higher scores indicating stronger perceived support. The total score for the family subscale ranges from 4 to 28. This instrument has shown good internal consistency in Chinese populations (Cronbach’s α > 0.85) and is sensitive to variation in caregiver psychological outcomes such as burden and distress [23]. (5) Sociodemographic and Caregiving Characteristics The survey collected data on caregiver age, gender, education level, relationship to the patient, duration of caregiving, and average daily caregiving hours. 2.3 Data Analysis Descriptive statistics were used to summarize caregiver characteristics. The LCA was conducted using Mplus version 8.4 to identify distinct latent subgroups based on the four psychosocial indicators: resilience, family support, depressive symptoms, and caregiving self-efficacy. Models with 1 to 6 classes were estimated sequentially. Model fit was evaluated using the following statistical criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Sample-size Adjusted BIC (aBIC), Entropy, Lo–Mendell–Rubin Likelihood Ratio Test (LMR–LRT) [24,25]. Model selection was based on a combination of statistical indices, parsimony, and clinical interpretability [26]. After latent classes were assigned using posterior probability-based classification, post hoc comparisons were conducted: Continuous variables were analyzed using ANOVA or Kruskal–Wallis tests, as appropriate; Categorical variables were compared using Chi-square (χ²) tests. Missing data (<5%) were handled using full information maximum likelihood (FIML) estimation [27]. All analyses were conducted using R version 4.3.2 and Mplus version 8.4, with two-tailed p-values < 0.05 considered statistically significant. 3. Results 3.1 Latent Class Identification and Characteristics Models for one to six latent classes were estimated to explore heterogeneity in the psychological profiles and caregiving patterns of family caregivers. As shown in Table 1 , the four-class model yielded the most optimal fit based on multiple indices. Specifically, the LMR adjusted likelihood ratio test and the bootstrap likelihood ratio test (BLRT) indicated that the four-class model provided a significantly better fit than the three-class model (LMR p < .05; BLRT p < .001). Additionally, it achieved lower AIC and BIC values compared to alternative solutions, along with an entropy value of 0.631, suggesting acceptable classification precision. See Table 2 for the comparison of sociodemographic characteristics across latent caregiver subgroups. Table 1 Latent Class Model Fit Comparison. Classes (K) Free Parameters Log Likelihood AIC BIC aBIC Entropy LMR p-value BLRT p-value 1 13 -5194.12 10414.24 10447.86 10426.12 — — — 2 20 -5141.50 10323.00 10380.24 10344.32 0.601 0.011 < .001 3 27 -5104.44 10262.88 10344.73 10294.64 0.621 0.057 < .001 4 34 -5085.97 10259.94 10297.37 10268.28 0.631 0.037 < .001 5 41 -5074.08 10230.16 10302.20 10266.41 0.597 0.268 < .001 6 48 -5067.65 10231.29 10337.96 10299.59 0.590 0.465 < .001 Table 2 Comparison of Sociodemographic Characteristics Across Latent Caregiver Subgroups Variables Class1 Class2 Class3 Class4 F/χ² P Age (standardized) 60.08 ± 12.63 61.99 ± 4.74 42.11 ± 3.87 57.69 ± 14.10 359.97 < 0.001 Gender = Female 95 (65.1%) 67 (49.3%) 68 (42.2%) 68 (50.0%) 16.69 < 0.001 Gender = Male 51 (34.9%) 69 (50.7%) 93 (57.8%) 68 (50.0%) Education = Primary or below 59 (40.4%) 45 (33.1%) 13 (8.1%) 22 (16.2%) 103.64 < 0.001 Education = High school 67 (45.9%) 67 (49.3%) 57 (35.4%) 52 (38.2%) Education = College and above 59 (40.4%) 45 (33.1%) 13 (8.1%) 22 (16.2%) Employment = Unemployed 89 (61.0%) 27 (19.9%) 16 (9.9%) 43 (31.6%) 268.65 < 0.001 Employment = Part-time 26 (17.8%) 18 (13.2%) 33 (20.5%) 16 (11.8%) Employment = Retired 9 (6.2%) 74 (54.4%) 7 (4.3%) 35 (25.7%) Employment = Full time 26 (17.8%) 18 (13.2%) 33 (20.5%) 16 (11.8%) Income = Low 64 (43.8%) 50 (36.8%) 16 (9.9%) 35 (25.7%) 65.67 < 0.001 Income = Middle 49 (33.6%) 55 (40.4%) 59 (36.6%) 57 (41.9%) Income = High 33 (22.6%) 31 (22.8%) 86 (53.4%) 44 (32.4%) Care relationship = Spouse 18 (12.3%) 10 (7.4%) 29 (18.0%) 19 (14.0%) 126.08 < 0.001 Care relationship = Others 34 (23.3%) 93 (68.4%) 18 (11.2%) 34 (25.0%) Care relationship = Adult child 94 (64.4%) 33 (24.3%) 114 (70.8%) 83 (61.0%) Note: Model selection indices for 1- to 6-class latent class models are presented. AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; aBIC = sample-size adjusted BIC. Entropy indicates classification accuracy, with values closer to 1 suggesting clearer separation between classes. LMR p-value = Lo–Mendell–Rubin adjusted likelihood ratio test; BLRT p-value = Bootstrapped likelihood ratio test. Bolded values typically indicate the optimal model based on a balance between fit statistics and parsimony. In this study, the 4-class model was selected as optimal, considering fit statistics (lower AIC, BIC, aBIC), entropy (0.631), and significant LMR and BLRT values. Based on the patterns of indicator distributions, the classes were labeled as follows: Class 1: Low Resilience with High Burden (21.9%) This group exhibited the highest average score on depression (M = 9.96) and the lowest levels of family support (M = 13.57) and behavioral self-efficacy (M = 27.49). Additionally, caregivers in this class reported moderate daily caregiving time (M = 8.58 hours) and caregiving duration (M = 19.11 months). This class reflects a group of caregivers with emotional vulnerability and limited coping resources, who may be struggling to adapt to sustained caregiving challenges. Their psychological profile suggests the need for integrated psychosocial and instrumental support interventions. Class 2: High Family Support but Low Internal Resources (25.7%) Caregivers in this group reported the strongest perceived family support (M = 19.90) but demonstrated the lowest resilience (M = 10.55). Depression levels were moderate (M = 7.94), and caregiving time and duration were comparable to the overall sample. The profile indicates a dependence on external resources for stress buffering, yet potential internal resource deficits. Without targeted empowerment strategies, this group may be vulnerable to burnout if support structures diminish. Class 3: Adaptive Self-Management (27.2%) This subgroup showed the most favorable psychological profile, with the lowest depression (M = 5.63), highest behavioral self-efficacy (M = 38.28), and above-average resilience (M = 14.35). Family support was moderate (M = 16.11), and caregiving demands were balanced. Caregivers in this class appeared to possess adaptive capacities to regulate emotions, solve problems, and sustain engagement. They may benefit from low-intensity, maintenance-oriented interventions to preserve resilience. Class 4: Enduring Care with Tenacity (25.2%) Members of this class had the longest caregiving duration (M = 24.47 months) and highest resilience scores (M = 14.37), but moderately elevated depression (M = 8.04). Their behavioral self-efficacy was average (M = 31.42), and daily caregiving hours remained substantial (M = 9.81). This pattern suggests a group of experienced caregivers with strong psychological tenacity but signs of accumulated burden. Collectively, these four latent classes underscore meaningful heterogeneity in the caregiving experience and adaptation patterns. The coexistence of high external support but low internal strength (Class 2), or of long-term tenacity with emotional distress (Class 4), reflects the complex dynamics of caregiver resilience. These classifications provide a data-driven foundation for tailoring support strategies according to distinct psychosocial profiles. 3.2 Multivariable Logistic Regression Analysis 3.2.1 Class 1 (low resilience with high burden) vs. Class 4 (enduring care with tenacity) Several sociodemographic factors were associated with increased likelihood of Class 1 membership (Table 3 ). Compared to caregivers with college education or above, those with primary (OR = 6.09, 95% CI: 2.78–13.36, p < .001) or high school education (OR = 3.24, 95% CI: 1.64–6.40, p = .001) had significantly higher odds. Employment status was also influential: unemployed (OR = 4.12, 95% CI: 2.00–8.50, p < .001) and part-time caregivers (OR = 3.54, 95% CI: 1.35–8.82, p = .006) were more likely to be in Class 1 than those employed full-time. Lower income levels were linked to higher odds of Class 1 membership: low income (OR = 3.61, p < .001) and middle income (OR = 2.25, p = .025) compared to high income. Gender and age were not significant predictors, though a non-significant trend was observed for female caregivers (OR = 1.67, p = .080). Care relationship type did not remain significant after adjustment. Table 3 Multinomial Logistic Regression Predicting Class Membership (Reference = Class 4) Group Variable B SE OR 95% CI (OR) p-value Explanation Class 1 Age (standardized) 0.15 0.14 1.16 [0.87, 1.53] 0.311 Per 1 SD increase in age Gender = Male -0.51 0.29 0.60 [0.34, 1.06] 0.080 vs. Female Education = Primary or below 1.81 0.40 6.09 [2.78, 13.36] < 0.001 vs. College and above Education = High school 1.17 0.35 3.24 [1.64, 6.40] 0.001 vs. College and above Employment = Unemployed 1.42 0.37 4.12 [2.00, 8.50] < 0.001 vs. Full-time Employment = Part-time 1.27 0.47 3.54 [1.35, 8.82] 0.006 vs. Full-time Employment = Retired -0.23 0.50 0.80 [0.30, 2.13] 0.650 vs. Full-time Income = Low 1.28 0.37 3.61 [1.56, 7.42] < 0.001 vs. High Income = Middle 0.81 0.36 2.25 [1.10, 4.57] 0.025 vs. High Care relationship = Spouse -0.19 0.36 0.83 [0.41, 1.68] 0.605 vs. Adult child Care relationship = Others 0.27 0.44 1.31 [0.55, 3.11] 0.536 vs. Adult child Class 2 Age (standardized) 0.51 0.17 1.67 [1.20, 2.31] 0.002 Per 1 SD increase in age Gender = Male -0.06 0.33 0.94 [0.50, 1.79] 0.854 vs. Female Education = Primary or below 2.61 0.48 13.58 [5.26, 35.09] < 0.001 vs. College and above Education = High school 1.98 0.42 7.27 [3.18, 16.60] < 0.001 vs. College and above Employment = Unemployed 0.40 0.49 1.49 [0.57, 3.91] 0.415 vs. Full-time Employment = Part-time 1.24 0.58 3.46 [1.12, 10.70] 0.031 vs. Full-time Employment = Retired 1.81 0.45 6.09 [2.54, 14.60] < 0.001 vs. Full-time Income = Low 1.32 0.45 3.75 [1.56, 9.00] 0.003 vs. High Income = Middle 1.00 0.41 2.73 [1.22, 6.13] 0.015 vs. High Care relationship = Spouse 2.37 0.38 10.69 [5.12, 22.31] < 0.001 vs. Adult child Care relationship = Others 0.19 0.53 1.21 [0.43, 3.41] 0.723 vs. Adult child Class 3 Age (standardized) -2.95 0.44 0.05 [0.02, 0.12] < 0.001 Per 1 SD increase in age Gender = Male 0.74 0.43 2.09 [0.89, 4.90] 0.089 vs. Female Education = Primary or below -2.57 0.72 0.08 [0.02, 0.31] < 0.001 vs. College and above Education = High school -0.84 0.44 0.43 [0.18, 1.01] 0.053 vs. College and above Employment = Unemployed -2.72 0.55 0.07 [0.02, 0.19] 0.000 vs. Full-time Employment = Part-time -0.44 0.59 0.64 [0.20, 2.03] 0.453 vs. Full-time Employment = Retired -3.59 0.64 0.03 [0.01, 0.10] 0.000 vs. Full-time Income = Low -1.41 0.60 0.24 [0.08, 0.79] 0.018 vs. High Income = Middle -2.04 0.51 0.13 [0.05, 0.36] < 0.000 vs. High Care relationship = Spouse -1.59 0.54 0.20 [0.07, 0.59] 0.003 vs. Adult child Care relationship = Others -0.12 0.58 0.89 [0.29, 2.74] 0.835 vs. Adult child 3.2.2 Class 2 (high family support but low internal resources) vs. Class 4 (enduring care with tenacity) This model compared Class 2 (caregivers with strong family support but limited internal coping resources, typically elderly spouses) with Class 4 (chronically overburdened but psychologically resilient caregivers). Spousal caregivers were significantly more likely than adult children to be classified into Class 2 (OR = 10.69, 95% CI: 5.12–22.31, p < .001). Education was a strong differentiator: caregivers with primary education or below had over 13 times the odds of belonging to Class 2 (OR = 13.58, 95% CI: 5.26–35.09, p < .001), and those with high school education had over seven times the odds (OR = 7.27, 95% CI: 3.18–16.60, p < .001), compared to those with college education or above. Other demographic variables—employment status, income level, gender, and age—did not reach statistical significance but demonstrated directional trends. Specifically, caregivers in Class 2 were more likely to be retired, have lower income, and be older than those in Class 4. These results reinforce the characterization of Class 2 as a profile of older, less-educated, and dependent spousal caregivers, in contrast to the more functionally and psychologically resilient Class 4. 3.2.3 Class 3 (adaptive self-management) vs. Class 4 (chronic overload with tenacity) Compared to Class 4, caregivers in Class 3—defined by resilient and adaptive coping—were significantly more likely to be adult children. Spousal caregivers had markedly lower odds of being in Class 3 (OR = 0.20, 95% CI: 0.07–0.59, p = .003), while those categorized as “others” did not differ significantly. Education again emerged as a robust predictor. Primary education or below was associated with a significantly decreased likelihood of Class 3 membership (OR = 0.08, 95% CI: 0.02–0.31, p < .001). High school education was marginally associated with lower odds (OR = 0.43, 95% CI: 0.18–1.01, p = .053). No statistically significant associations were observed for employment status, income level, gender, or age in this model. See Fig. 1 for the combined forest plot comparing predictors of class 1, 2, and 3 versus class 4 (log scale). 3.3 Class Comparisons on Psychosocial and Caregiving Variables Significant differences were observed across classes on multiple psychosocial and caregiving-related indicators (Table 4 , Fig. 2 ). Resilience (CDRISC_Tenacity) differed markedly between groups ( H = 42.67, p < .001), with Class 3 demonstrating the highest standardized scores. Depressive symptoms (HADS_Depression) also varied significantly ( H = 30.09, p < .001), with the highest levels reported by Class 1. In addition, significant class differences were found in perceived family support (MSPSS_Family; H = 28.10, p < .001) and caregiving self-efficacy (CSSE_Behavioral; H = 20.10, p < .001), both favoring Class 3. Care burden indicators—including daily care hours ( H = 27.44, p < .001) and overall caregiving duration ( H = 17.01, p = .002)—also varied significantly, with Class 4 assuming more intensive and prolonged care responsibilities. Post hoc Bonferroni-adjusted comparisons indicated that these differences were primarily driven by contrasts involving Class 1 and Class 3, as well as Class 1 and Class 4. Together, these findings validate the class distinctions and underscore the heterogeneity in psychosocial functioning and care demands across caregiver subgroups. Table 4 Comparison of Key Psychosocial and Caregiving Variables Across Latent Classes HADS_Depression CDRISC_Tenacity MSPSS_Family CSSE_Behavioral Care Duration_Months Daily Care_Hours Class1 9.93 ± 3.44 12.06 ± 2.67 13.57 ± 4.05 27.73 ± 4.96 9.81 ± 5.39 5.02 ± 2.16 Class2 8.79 ± 3.73 10.54 ± 2.89 19.92 ± 3.01 30.81 ± 5.04 10.94 ± 5.09 5.07 ± 1.70 Class3 5.65 ± 2.52 14.37 ± 3.21 18.30 ± 3.25 38.34 ± 5.26 12.38 ± 4.37 5.34 ± 1.47 Class4 7.38 ± 3.70 14.42 ± 3.34 17.28 ± 3.65 33.15 ± 4.51 25.45 ± 5.20 6.04 ± 1.92 H 119.148 131.482 160.866 229.791 300.46 22.369 p < .001 < .001 < .001 < .001 < .001 < .001 Note: Values are presented as Mean ± SD. P-values from Kruskal–Wallis tests. 4. Discussion This study identified four distinct latent classes among caregivers of colorectal cancer patients with permanent ostomies, based on resilience, psychological burden, and caregiving self-efficacy. By integrating both vulnerability and strength-based indicators, this study provides a comprehensive framework to understand caregiver heterogeneity and inform targeted supportive strategies in oncology nursing. The use of LCA offers not only statistical precision, but also clinical insight into how demographic and psychosocial factors combine to shape caregiving experiences. 4.1 Characterizing Heterogeneous Caregiver Subtypes The findings revealed that caregivers do not constitute a uniform population. Instead, they can be empirically grouped into four classes, each with distinct risk profiles, resources, and demographic compositions. 4.1.1 Class 1: Low Resilience with High Burden (n = 146) This group demonstrated the most psychologically vulnerable profile, with the highest levels of depressive symptoms, low perceived family support, and the lowest caregiving self-efficacy. They were predominantly composed of caregivers with lower education levels, lower income, and higher rates of unemployment or part-time employment. Most were women (≈ 70%) aged around 50, and a significant proportion were spouses or other non-adult-child caregivers. These characteristics align with established predictors of caregiver distress, including limited education, economic strain, and gender-based caregiving expectations [ 28 , 29 ]. Importantly, the combination of structural disadvantages and weak internal resources may lead to compounding vulnerability [ 30 ]. This group mirrors what Liu et al. [ 31 ] have described as "high-risk caregivers," warranting proactive identification and early psychosocial intervention. 4.1.2 Class 2: High Support but Low Internal Resources (n = 136) Of particular interest is the discrepancy observed in Class 2 (high support but low internal resources): despite reporting high levels of perceived family support, these caregivers showed low internal efficacy and elevated depressive symptoms. Demographically, these were mostly older adults (mean age ≈ 62), with over half being retired spousal caregivers—typically women with limited formal education. Despite the presence of external support, their internal psychological resources appeared depleted. This dissociation suggests that perceived support is not always protective, particularly in the absence of intrapersonal strengths. This phenomenon aligns with the concept of “silent burden” in spousal caregivers, who may suppress emotional needs due to normative expectations [ 32 ]. Their high vulnerability, often under-recognized, underscores the need for psychological empowerment interventions beyond family support systems. These patterns highlight the need to assess both environmental and intrapsychic resources shaping adaptation [ 33 , 34 ]. 4.1.3 Class 3: Resilient Caregivers (n = 161) Caregivers in this class reported the highest scores in resilience and caregiving efficacy, along with the lowest depression levels. They were younger (mean age ≈ 47), more educated (over 40% with college or higher), and more likely to be adult children of the patient. Employment rates were high, and many reported middle to high income. This group reflects what Gaugler et al. [ 35 ] describe as “adaptive caregivers” who leverage social, cognitive, and educational resources to buffer the psychological demands of caregiving. Their resilience may stem from access to instrumental support, flexible coping strategies, and greater autonomy. Importantly, they demonstrate that caregiving does not inevitably lead to psychological distress, supporting a strengths-based approach to caregiver assessment [ 36 ]. 4.1.4 Class 4: Enduring Care with Tenacity (n = 136) This group exhibited moderate resilience and psychological functioning, despite reporting the longest caregiving duration and highest daily care hours. Demographically, they were balanced in age (mean ≈ 54), often adult children, and held diverse educational and occupational statuses. While currently stable, their prolonged intensity of care may predispose them to delayed exhaustion. This aligns with the literature on “chronic caregivers” who maintain functional stability but are at risk of attrition without timely support [ 37 , 38 ]. Oncology nurses must be attuned to subtle signs of wear-and-tear, even in caregivers who do not self-report distress. 4.2 A Dual-Axis Framework: Burden and Resilience Interact, Not Compete The inverse profiles of Class 1 and Class 3 illustrate a central insight: resilience and burden are not endpoints of a single continuum, but distinct dimensions that interact to shape psychological outcomes. This supports a dual-axis model, wherein caregivers can be high in both burden and resilience (Class 3), or low in both (Class 1), depending on personal and contextual factors [ 39 – 41 ]. This conceptualization resonates with the stress-appraisal-coping theory [ 42 ], as well as recent models proposing that resilience reflects not just recovery from stress, but ongoing adaptive capacity in the face of adversity [ 43 ]. It challenges oncology teams to assess not only distress symptoms, but also protective mechanisms like tenacity, problem-solving efficacy, and perceived mastery. 4.3 Implications for Oncology Nursing Assessment and Intervention These findings hold significant relevance for clinical oncology nursing. First, they validate the need for proactive and multidimensional caregiver assessment, going beyond burden screening to include indicators of internal capacity and role identity. Nurses are in a unique position to initiate early stratification based on class-like profiles using brief instruments embedded into intake or survivorship planning. Second, the findings suggest stratified intervention pathways: Class 1 (low resilience with high burden) caregivers may benefit from multicomponent support including psychoeducation, counseling, and socioeconomic linkage. Class 2 (high support but low internal resources) caregivers need psychological empowerment strategies—such as motivational interviewing or structured skills training—to rebuild self-efficacy. Class 3 (resilient) caregivers require ongoing monitoring and access to peer or respite programs to sustain long-term care provision. Class 4 (enduring care with tenacity) caregivers represent an asset group—individuals who could serve as mentors or resource models within community-based caregiver networks. This class-based logic supports the move toward precision psycho-oncology, where the right support is delivered to the right caregiver at the right time [ 43 ]. It also aligns with caregiver-inclusive care models advocated by the World Health Organization and the European Oncology Nursing Society [ 44 , 45 ]. 4.4 Toward Integrated, System-Level Application Beyond individual triage, this classification has implications for systemic policy. The profiles could be integrated into interdisciplinary rounds, shared across oncology care teams, and used to coordinate referrals to psycho-oncology, social work, and community organizations. Class-stratified data could also inform institutional caregiver education curricula and care quality indicators. Furthermore, this approach offers an empirical foundation for longitudinal caregiver monitoring, allowing health systems to detect shifting needs over time, particularly as care trajectories evolve into survivorship or terminal phases. 4.5 Methodological Contributions, Limitations, and Future Directions This study advances caregiver research in several ways. First, it applies LCA to a clinically relevant population—family caregivers of colorectal cancer patients with ostomies—rarely analyzed with person-centered methods. Second, it integrates burden and resilience indicators, countering the historical overemphasis on negative outcomes. Third, the large and demographically diverse sample enhances generalizability across care relationships and social strata. However, several limitations must be acknowledged. The cross-sectional design precludes causal inference or longitudinal trajectory mapping. Although class profiles were statistically robust, their temporal stability and responsiveness to intervention remain unknown. Additionally, caregiver outcomes were self-reported, potentially subject to social desirability or underreporting biases, particularly in older spousal caregivers. Future studies should pursue longitudinal validation of class transitions and test intervention effectiveness by class. Integrating biological or behavioral indicators (e.g., cortisol, sleep patterns) could enrich understanding of resilience under sustained caregiving stress. Moreover, cross-cultural replication would be valuable, as caregiving norms and support structures vary widely across health systems. Finally, the development of class-informed clinical screening tools and their integration into nursing workflow remains a practical next step. Brief tools aligned with latent profiles—using validated indicators such as the HADS, CDRISC, or self-efficacy scales—could support triage and early referral without overwhelming frontline staff. 5. Conclusion This study highlights the substantial psychosocial heterogeneity among family caregivers of colorectal cancer patients with permanent ostomies. Using a person-centered analytic approach, we identified distinct patterns of resilience, psychological burden, and caregiving efficacy that cannot be captured by aggregate measures alone. These findings underscore the need to move beyond binary assessments of distress and toward a more nuanced, multidimensional understanding of caregiver functioning. For oncology nursing, this evidence supports the integration of psychosocial profiling into routine caregiver assessments. Tailoring interventions to reflect caregivers' specific strengths and needs—rather than relying on one-size-fits-all approaches—may improve early identification of risk, optimize resource allocation, and enhance caregiver well-being. This paradigm also aligns with broader movements in precision psycho-oncology and person-centered care. Future research should examine how caregiver profiles evolve over time, respond to targeted interventions, and influence patient outcomes. Embedding class-informed frameworks into multidisciplinary cancer care has the potential to strengthen the sustainability, humanity, and impact of caregiver support across the oncology trajectory. Declarations Authorship contribution statement S. H. J. and J. Q. contributed equally to this work and are co-first authors. S. M. C. is the corresponding author. S. H. J. and J. Q.: study design, data collection, statistical analysis, and manuscript drafting. S.M. C.:supervision, critical review, and manuscript editing. All authors read and approved the final manuscript. Data Availability Statement The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Funding This study received non-financial support from the China Social Welfare Foundation (Grant No. HLCXT-20230705). The Foundation provided administrative and logistical assistance but did not provide direct financial funding. It had no role in study design, data collection, data analysis, data interpretation, manuscript preparation, or the decision to submit the manuscript for publication. The views expressed are solely those of the authors and do not necessarily reflect the policies or endorsements of the China Social Welfare Foundation. Declaration Of Competing Interest The authors declare that they have no conflict of interest. Acknowledgements We are grateful to all the participants who contributed their time and insights to this study. 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1","display":"","copyAsset":false,"role":"figure","size":61329,"visible":true,"origin":"","legend":"\u003cp\u003eCombined Forest Plot Comparing Predictors of Class 1, 2, and 3 Versus Class 4 (Log Scale)\u003c/p\u003e\n\u003cp\u003eCaptions: Forest plot presenting adjusted odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression models, using Class 4 as the reference group. The x-axis is displayed on a logarithmic scale. Each dot represents the point estimate for a given predictor, with error bars denoting the 95% CI. Different colors indicate comparisons for Class 1 (orange), Class 2 (blue), and Class 3 (green) relative to Class 4.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7574295/v1/26bfbd344e4c7c53d5b68eea.jpg"},{"id":95063555,"identity":"d2c5036f-29d5-442b-8409-48562e6068ab","added_by":"auto","created_at":"2025-11-04 01:18:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55166,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized Comparison of Psychosocial and Caregiving Characteristics Across Classes\u003c/p\u003e\n\u003cp\u003eCaptions: Z-scores for six key variables were calculated and plotted to illustrate relative differences across caregiver classes. Higher values indicate greater levels of each attribute. Class 3 showed the highest resilience (CDRISC_Tenacity_z) and family support (MSPSS_Family_z), while Class 1 had elevated depressive symptoms (HADS_Depression_z) and lower self-efficacy (CSSE_Behavioral_z). Class 4 demonstrated the longest care duration and highest daily caregiving hours. This standardized profile highlights the distinct psychological and functional characteristics of each latent class.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7574295/v1/8372e3ad3f22070d0b2eb58b.jpg"},{"id":103056273,"identity":"1619bde7-6b37-4565-97bd-a039ed6f5099","added_by":"auto","created_at":"2026-02-20 09:02:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1723619,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7574295/v1/79ef0aed-5417-427b-b6a3-521e694ba2ed.pdf"},{"id":95224551,"identity":"fd0dbcbc-d41f-4c1f-bd6b-7beeb1de7f17","added_by":"auto","created_at":"2025-11-05 16:23:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":33025,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7574295/v1/989725f015d1e38336b6e020.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychosocial Profiles of Family Caregivers for Colorectal Cancer Patients with Permanent Ostomies: A Latent Class Analysis of Resilience, Depression, and Self-Efficacy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFamily caregivers are an indispensable component of cancer care, particularly for patients requiring complex, long-term support. In colorectal cancer, the presence of a permanent ostomy introduces unique caregiving challenges\u0026mdash;ranging from stoma management to emotional adjustment\u0026mdash;which can place significant strain on informal caregivers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Extensive literature has documented the psychological toll of caregiving, including elevated levels of depression, anxiety, and caregiver fatigue [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These adverse outcomes not only affect caregiver well-being but may also compromise care quality and patient recovery [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite this recognition, most research continues to treat caregivers as a homogeneous population, focusing primarily on risk factors for burden or distress [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This variable-centered approach often overlooks the nuanced ways in which individuals differ in their capacity to cope with caregiving demands [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In reality, caregivers vary widely in their psychological resilience, caregiving efficacy, and access to support. Some adapt effectively even under chronic stress, while others struggle despite favorable external circumstances [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eResilience, broadly defined as the ability to maintain or regain psychological well-being in the face of adversity, has emerged as a critical but underexplored construct in caregiving research [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While a growing body of evidence highlights its protective role, few studies have examined how resilience interacts with perceived burden and caregiving competence across different subgroups [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, reliance on aggregate or mean scores may obscure important patterns of heterogeneity that are clinically meaningful [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePerson-centered approaches, such as latent class analysis (LCA), offer a promising framework to address this gap [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Rather than assuming uniformity, LCA identifies distinct caregiver profiles based on shared response patterns, allowing for a more nuanced understanding of caregiving experiences. Such classifications can inform the design of targeted interventions, optimize resource allocation, and support the move toward precision psycho-oncology [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccordingly, this study aimed to identify distinct psychosocial profiles among family caregivers of colorectal cancer patients with permanent ostomies, based on their levels of resilience, psychological burden, and caregiving self-efficacy. We further examined the sociodemographic characteristics associated with each profile and discussed the implications for individualized caregiver support in oncology nursing. By capturing the complexity and variability of caregiving, this research seeks to contribute to more equitable, person-centered approaches to cancer care delivery.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study employed LCA to identify distinct psychosocial subgroups among family caregivers of colorectal cancer patients with permanent ostomies. Participants were recruited between January and July 2025 from the colorectal cancer follow-up clinics and stoma care outpatient services at four tertiary hospitals in Qingdao, China.\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Ethics Committee of Qingdao Municipal Hospital (Approval No. 2025-KY-049), and all procedures were conducted in accordance with the Declaration of Helsinki [14]. Written informed consent was obtained from all participants prior to data collection. A total of 620 questionnaires were distributed, of which 593 were returned (response rate: 95.6%). After excluding incomplete or invalid responses, 579 questionnaires were deemed valid and included in the final analysis (validity rate: 97.6%).\u003c/p\u003e\n\u003cp\u003e2.1.1 Eligible participants were:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Primary family caregivers (i.e., the person providing the majority of daily care) of patients who had undergone permanent colostomy or ileostomy for colorectal cancer;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAged 18 years or older;\u003c/li\u003e\n \u003cli\u003eAble to communicate in Mandarin and provide informed consent;\u003c/li\u003e\n \u003cli\u003eNot receiving financial compensation for caregiving (non-professional caregivers).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e2.1.2 Caregivers were excluded if they:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eHad clinically diagnosed cognitive impairment or mental illness;\u003c/li\u003e\n \u003cli\u003eWere concurrently caring for multiple patients.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFollowing recommendations for LCA with continuous indicators [15], we assumed approximately 8\u0026ndash;10 parameters per latent class (e.g., class-specific means and class membership probabilities). For a 4-class model, this leads to approximately 40 estimated parameters. Based on the general rule of thumb requiring 10\u0026ndash;20 participants per parameter, a minimum sample size of 400\u0026ndash;800 was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(1) Connor\u0026ndash;Davidson Resilience Scale (Tenacity Subscale)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePsychological resilience was assessed using the Tenacity subscale of the Chinese version of the Connor\u0026ndash;Davidson Resilience Scale (CD-RISC) [16]. This subscale captures core aspects of resilience related to perseverance, goal orientation, and self-confidence under stress. It includes 13 items, each rated on a 5-point Likert scale ranging from 0 (not true at all) to 4 (true nearly all the time), with total scores ranging from 0 to 52. Higher scores reflect greater tenacity in the face of adversity. The Tenacity subscale has demonstrated strong internal consistency and construct validity in Chinese populations. Prior research has shown that this subscale is particularly relevant for assessing trait-like resilience in caregivers and individuals managing chronic illness [17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2) Hospital Anxiety and Depression Scale (Depression subscale)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepressive symptoms were measured using the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D), developed by Zigmond, Snaith [18]. The HADS-D is a self-report instrument designed to assess depressive symptoms in non-psychiatric populations, particularly in medical and cancer care settings. It avoids somatic items (e.g., fatigue, appetite) to reduce confounding with physical illness symptoms. The depression subscale consists of 7 items, each rated on a 4-point Likert scale (0\u0026ndash;3), with total scores ranging from 0 to 21. Higher scores indicate more severe depressive symptoms, with commonly used cutoffs of 0\u0026ndash;7 (normal), 8\u0026ndash;10 (borderline), and 11\u0026ndash;21 (clinical level depression). The HADS-D has been widely used in oncology and caregiver research and shows good internal consistency, test-retest reliability, and convergent validity. A validated Chinese version has demonstrated strong psychometric properties in Chinese caregiver populations [19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(3) Caregiving Self-Efficacy (Behavior Subscale)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCaregiving self-efficacy was assessed using the Behavior Subscale of the Caregiver Self-Efficacy Scale, originally developed by Steffen et al. [20]. This subscale specifically measures caregivers\u0026rsquo; perceived confidence in managing behavioral and practical challenges associated with caregiving tasks, which is particularly relevant in cancer care contexts. The subscale consists of five items, each rated on a 10-point Likert scale ranging from 0 (not at all confident) to 9 (completely confident). Items assess caregivers\u0026rsquo; confidence in handling difficult behaviors, maintaining emotional control, and implementing caregiving strategies effectively. The total score ranges from 0 to 45, with higher scores indicating greater self-efficacy in caregiving behavior management. This scale has demonstrated good internal consistency and construct validity, and the Chinese version used in this study has been validated in prior studies involving cancer caregivers [21].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(4) Multidimensional Scale of Perceived Social Support (Family Support Subscale)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerceived family support was assessed using the Family subscale of the Multidimensional Scale of Perceived Social Support (MSPSS) [22]. The MSPSS is a widely used 12-item instrument that measures perceived social support from three sources: family, friends, and significant others, with each subscale comprising 4 items. The family subscale specifically evaluates the extent to which individuals perceive their family members as available, caring, and supportive. Sample items include \u003cem\u003e\u0026ldquo;My family really tries to help me\u0026rdquo;\u003c/em\u003e and \u003cem\u003e\u0026ldquo;I get the emotional help and support I need from my family.\u0026rdquo;\u003c/em\u003e Participants rate each item on a 7-point Likert scale ranging from 1 = very strongly disagree to 7 = very strongly agree, with higher scores indicating stronger perceived support. The total score for the family subscale ranges from 4 to 28. This instrument has shown good internal consistency in Chinese populations (Cronbach\u0026rsquo;s \u0026alpha; \u0026gt; 0.85) and is sensitive to variation in caregiver psychological outcomes such as burden and distress [23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(5) Sociodemographic and Caregiving Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey collected data on caregiver age, gender, education level, relationship to the patient, duration of caregiving, and average daily caregiving hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarize caregiver characteristics. The LCA was conducted using Mplus version 8.4 to identify distinct latent subgroups based on the four psychosocial indicators: resilience, family support, depressive symptoms, and caregiving self-efficacy.\u003c/p\u003e\n\u003cp\u003eModels with 1 to 6 classes were estimated sequentially. Model fit was evaluated using the following statistical criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Sample-size Adjusted BIC (aBIC), Entropy, Lo\u0026ndash;Mendell\u0026ndash;Rubin Likelihood Ratio Test (LMR\u0026ndash;LRT) [24,25]. Model selection was based on a combination of statistical indices, parsimony, and clinical interpretability [26].\u003c/p\u003e\n\u003cp\u003eAfter latent classes were assigned using posterior probability-based classification, post hoc comparisons were conducted: Continuous variables were analyzed using ANOVA or Kruskal\u0026ndash;Wallis tests, as appropriate; Categorical variables were compared using Chi-square (\u0026chi;\u0026sup2;) tests. Missing data (\u0026lt;5%) were handled using full information maximum likelihood (FIML) estimation [27].\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted using R version 4.3.2 and Mplus version 8.4, with two-tailed p-values \u0026lt; 0.05 considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Latent Class Identification and Characteristics\u003c/h2\u003e\u003cp\u003eModels for one to six latent classes were estimated to explore heterogeneity in the psychological profiles and caregiving patterns of family caregivers. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the four-class model yielded the most optimal fit based on multiple indices. Specifically, the LMR adjusted likelihood ratio test and the bootstrap likelihood ratio test (BLRT) indicated that the four-class model provided a significantly better fit than the three-class model (LMR \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05; BLRT \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Additionally, it achieved lower AIC and BIC values compared to alternative solutions, along with an entropy value of 0.631, suggesting acceptable classification precision. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for the comparison of sociodemographic characteristics across latent caregiver subgroups.\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\u003eLatent Class Model Fit Comparison.\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=\"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=\"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\"\u003e\u003cp\u003eClasses (K)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFree Parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLog Likelihood\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" 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colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e10297.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e10268.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.631\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\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\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5074.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10230.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10302.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10266.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5067.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10231.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10337.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10299.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Sociodemographic Characteristics Across Latent Caregiver Subgroups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClass3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClass4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eF/χ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (standardized)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.08\u0026thinsp;\u0026plusmn;\u0026thinsp;12.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.99\u0026thinsp;\u0026plusmn;\u0026thinsp;4.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.69\u0026thinsp;\u0026plusmn;\u0026thinsp;14.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e359.97\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (65.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (49.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (42.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16.69\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u0026thinsp;=\u0026thinsp;Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (34.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (50.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93 (57.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u0026thinsp;=\u0026thinsp;Primary or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (40.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (33.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (8.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e103.64\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u0026thinsp;=\u0026thinsp;High school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (49.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (35.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52 (38.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u0026thinsp;=\u0026thinsp;College and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (40.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (33.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (8.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment\u0026thinsp;=\u0026thinsp;Unemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (61.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (19.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e268.65\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment\u0026thinsp;=\u0026thinsp;Part-time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (17.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment\u0026thinsp;=\u0026thinsp;Retired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (54.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment\u0026thinsp;=\u0026thinsp;Full time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (17.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u0026thinsp;=\u0026thinsp;Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (36.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e65.67\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u0026thinsp;=\u0026thinsp;Middle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (40.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (36.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57 (41.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u0026thinsp;=\u0026thinsp;High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (22.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (53.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44 (32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCare relationship\u0026thinsp;=\u0026thinsp;Spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (12.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29 (18.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (14.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e126.08\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCare relationship\u0026thinsp;=\u0026thinsp;Others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (68.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (11.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCare relationship\u0026thinsp;=\u0026thinsp;Adult child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94 (64.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114 (70.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83 (61.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Model selection indices for 1- to 6-class latent class models are presented. AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; aBIC = sample-size adjusted BIC. Entropy indicates classification accuracy, with values closer to 1 suggesting clearer separation between classes. LMR p-value = Lo\u0026ndash;Mendell\u0026ndash;Rubin adjusted likelihood ratio test; BLRT p-value = Bootstrapped likelihood ratio test. Bolded values typically indicate the optimal model based on a balance between fit statistics and parsimony. In this study, the 4-class model was selected as optimal, considering fit statistics (lower AIC, BIC, aBIC), entropy (0.631), and significant LMR and BLRT values.\u003c/p\u003e\u003cp\u003eBased on the patterns of indicator distributions, the classes were labeled as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eClass 1: Low Resilience with High Burden (21.9%)\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis group exhibited the highest average score on depression (M\u0026thinsp;=\u0026thinsp;9.96) and the lowest levels of family support (M\u0026thinsp;=\u0026thinsp;13.57) and behavioral self-efficacy (M\u0026thinsp;=\u0026thinsp;27.49). Additionally, caregivers in this class reported moderate daily caregiving time (M\u0026thinsp;=\u0026thinsp;8.58 hours) and caregiving duration (M\u0026thinsp;=\u0026thinsp;19.11 months). This class reflects a group of caregivers with emotional vulnerability and limited coping resources, who may be struggling to adapt to sustained caregiving challenges. Their psychological profile suggests the need for integrated psychosocial and instrumental support interventions.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eClass 2: High Family Support but Low Internal Resources (25.7%)\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eCaregivers in this group reported the strongest perceived family support (M\u0026thinsp;=\u0026thinsp;19.90) but demonstrated the lowest resilience (M\u0026thinsp;=\u0026thinsp;10.55). Depression levels were moderate (M\u0026thinsp;=\u0026thinsp;7.94), and caregiving time and duration were comparable to the overall sample. The profile indicates a dependence on external resources for stress buffering, yet potential internal resource deficits. Without targeted empowerment strategies, this group may be vulnerable to burnout if support structures diminish.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eClass 3: Adaptive Self-Management (27.2%)\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis subgroup showed the most favorable psychological profile, with the lowest depression (M\u0026thinsp;=\u0026thinsp;5.63), highest behavioral self-efficacy (M\u0026thinsp;=\u0026thinsp;38.28), and above-average resilience (M\u0026thinsp;=\u0026thinsp;14.35). Family support was moderate (M\u0026thinsp;=\u0026thinsp;16.11), and caregiving demands were balanced. Caregivers in this class appeared to possess adaptive capacities to regulate emotions, solve problems, and sustain engagement. They may benefit from low-intensity, maintenance-oriented interventions to preserve resilience.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eClass 4: Enduring Care with Tenacity (25.2%)\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eMembers of this class had the longest caregiving duration (M\u0026thinsp;=\u0026thinsp;24.47 months) and highest resilience scores (M\u0026thinsp;=\u0026thinsp;14.37), but moderately elevated depression (M\u0026thinsp;=\u0026thinsp;8.04). Their behavioral self-efficacy was average (M\u0026thinsp;=\u0026thinsp;31.42), and daily caregiving hours remained substantial (M\u0026thinsp;=\u0026thinsp;9.81). This pattern suggests a group of experienced caregivers with strong psychological tenacity but signs of accumulated burden.\u003c/p\u003e\u003cp\u003eCollectively, these four latent classes underscore meaningful heterogeneity in the caregiving experience and adaptation patterns. The coexistence of high external support but low internal strength (Class 2), or of long-term tenacity with emotional distress (Class 4), reflects the complex dynamics of caregiver resilience. These classifications provide a data-driven foundation for tailoring support strategies according to distinct psychosocial profiles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Multivariable Logistic Regression Analysis\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Class 1 (low resilience with high burden) vs. Class 4 (enduring care with tenacity)\u003c/h2\u003e\u003cp\u003eSeveral sociodemographic factors were associated with increased likelihood of Class 1 membership (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared to caregivers with college education or above, those with primary (OR\u0026thinsp;=\u0026thinsp;6.09, 95% CI: 2.78\u0026ndash;13.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) or high school education (OR\u0026thinsp;=\u0026thinsp;3.24, 95% CI: 1.64\u0026ndash;6.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001) had significantly higher odds. Employment status was also influential: unemployed (OR\u0026thinsp;=\u0026thinsp;4.12, 95% CI: 2.00\u0026ndash;8.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and part-time caregivers (OR\u0026thinsp;=\u0026thinsp;3.54, 95% CI: 1.35\u0026ndash;8.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006) were more likely to be in Class 1 than those employed full-time. Lower income levels were linked to higher odds of Class 1 membership: low income (OR\u0026thinsp;=\u0026thinsp;3.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and middle income (OR\u0026thinsp;=\u0026thinsp;2.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.025) compared to high income. Gender and age were not significant predictors, though a non-significant trend was observed for female caregivers (OR\u0026thinsp;=\u0026thinsp;1.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.080). Care relationship type did not remain significant after adjustment.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultinomial Logistic Regression Predicting Class Membership (Reference\u0026thinsp;=\u0026thinsp;Class 4)\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=\"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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI (OR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eExplanation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 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\" 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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (standardized)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.87, 1.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePer 1 SD increase in age\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\u003eGender\u0026thinsp;=\u0026thinsp;Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.34, 1.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Female\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\u003eEducation\u0026thinsp;=\u0026thinsp;Primary or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[2.78, 13.36]\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=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. College and above\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\u003eEducation\u0026thinsp;=\u0026thinsp;High school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[1.64, 6.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. College and above\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\u003eEmployment\u0026thinsp;=\u0026thinsp;Unemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[2.00, 8.50]\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=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Full-time\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\u003eEmployment\u0026thinsp;=\u0026thinsp;Part-time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[1.35, 8.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Full-time\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\u003eEmployment\u0026thinsp;=\u0026thinsp;Retired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.30, 2.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Full-time\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\u003eIncome\u0026thinsp;=\u0026thinsp;Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[1.56, 7.42]\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=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. High\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\u003eIncome\u0026thinsp;=\u0026thinsp;Middle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[1.10, 4.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. High\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\u003eCare relationship\u0026thinsp;=\u0026thinsp;Spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.41, 1.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Adult child\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\u003eCare relationship\u0026thinsp;=\u0026thinsp;Others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.55, 3.11]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Adult child\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 2\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (standardized)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[1.20, 2.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePer 1 SD increase in age\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\u003eGender\u0026thinsp;=\u0026thinsp;Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.50, 1.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Female\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\u003eEducation\u0026thinsp;=\u0026thinsp;Primary or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[5.26, 35.09]\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=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. College and above\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\u003eEducation\u0026thinsp;=\u0026thinsp;High school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[3.18, 16.60]\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=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. College and above\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\u003eEmployment\u0026thinsp;=\u0026thinsp;Unemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.57, 3.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Full-time\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\u003eEmployment\u0026thinsp;=\u0026thinsp;Part-time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[1.12, 10.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Full-time\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\u003eEmployment\u0026thinsp;=\u0026thinsp;Retired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[2.54, 14.60]\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=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Full-time\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\u003eIncome\u0026thinsp;=\u0026thinsp;Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[1.56, 9.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. High\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\u003eIncome\u0026thinsp;=\u0026thinsp;Middle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[1.22, 6.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. High\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\u003eCare relationship\u0026thinsp;=\u0026thinsp;Spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[5.12, 22.31]\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=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Adult child\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\u003eCare relationship\u0026thinsp;=\u0026thinsp;Others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.43, 3.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Adult child\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 3\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (standardized)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.02, 0.12]\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=\"left\" colname=\"c8\"\u003e\u003cp\u003ePer 1 SD increase in age\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\u003eGender\u0026thinsp;=\u0026thinsp;Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.89, 4.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Female\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\u003eEducation\u0026thinsp;=\u0026thinsp;Primary or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.02, 0.31]\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=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. College and above\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\u003eEducation\u0026thinsp;=\u0026thinsp;High school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.18, 1.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. College and above\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\u003eEmployment\u0026thinsp;=\u0026thinsp;Unemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.02, 0.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Full-time\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\u003eEmployment\u0026thinsp;=\u0026thinsp;Part-time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.20, 2.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Full-time\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\u003eEmployment\u0026thinsp;=\u0026thinsp;Retired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.01, 0.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Full-time\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\u003eIncome\u0026thinsp;=\u0026thinsp;Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.08, 0.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. High\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\u003eIncome\u0026thinsp;=\u0026thinsp;Middle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.05, 0.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. High\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\u003eCare relationship\u0026thinsp;=\u0026thinsp;Spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.07, 0.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Adult child\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\u003eCare relationship\u0026thinsp;=\u0026thinsp;Others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.29, 2.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003evs. Adult child\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2.2 Class 2 (high family support but low internal resources) vs. Class 4 (enduring care with tenacity)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis model compared Class 2 (caregivers with strong family support but limited internal coping resources, typically elderly spouses) with Class 4 (chronically overburdened but psychologically resilient caregivers). Spousal caregivers were significantly more likely than adult children to be classified into Class 2 (OR\u0026thinsp;=\u0026thinsp;10.69, 95% CI: 5.12\u0026ndash;22.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Education was a strong differentiator: caregivers with primary education or below had over 13 times the odds of belonging to Class 2 (OR\u0026thinsp;=\u0026thinsp;13.58, 95% CI: 5.26\u0026ndash;35.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and those with high school education had over seven times the odds (OR\u0026thinsp;=\u0026thinsp;7.27, 95% CI: 3.18\u0026ndash;16.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), compared to those with college education or above. Other demographic variables\u0026mdash;employment status, income level, gender, and age\u0026mdash;did not reach statistical significance but demonstrated directional trends. Specifically, caregivers in Class 2 were more likely to be retired, have lower income, and be older than those in Class 4. These results reinforce the characterization of Class 2 as a profile of older, less-educated, and dependent spousal caregivers, in contrast to the more functionally and psychologically resilient Class 4.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Class 3 (adaptive self-management) vs. Class 4 (chronic overload with tenacity)\u003c/h2\u003e\u003cp\u003eCompared to Class 4, caregivers in Class 3\u0026mdash;defined by resilient and adaptive coping\u0026mdash;were significantly more likely to be adult children. Spousal caregivers had markedly lower odds of being in Class 3 (OR\u0026thinsp;=\u0026thinsp;0.20, 95% CI: 0.07\u0026ndash;0.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003), while those categorized as \u0026ldquo;others\u0026rdquo; did not differ significantly. Education again emerged as a robust predictor. Primary education or below was associated with a significantly decreased likelihood of Class 3 membership (OR\u0026thinsp;=\u0026thinsp;0.08, 95% CI: 0.02\u0026ndash;0.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). High school education was marginally associated with lower odds (OR\u0026thinsp;=\u0026thinsp;0.43, 95% CI: 0.18\u0026ndash;1.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.053). No statistically significant associations were observed for employment status, income level, gender, or age in this model. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the combined forest plot comparing predictors of class 1, 2, and 3 versus class 4 (log scale).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Class Comparisons on Psychosocial and Caregiving Variables\u003c/h2\u003e\u003cp\u003eSignificant differences were observed across classes on multiple psychosocial and caregiving-related indicators (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Resilience (CDRISC_Tenacity) differed markedly between groups (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with Class 3 demonstrating the highest standardized scores. Depressive symptoms (HADS_Depression) also varied significantly (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with the highest levels reported by Class 1. In addition, significant class differences were found in perceived family support (MSPSS_Family; \u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and caregiving self-efficacy (CSSE_Behavioral; \u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), both favoring Class 3. Care burden indicators\u0026mdash;including daily care hours (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and overall caregiving duration (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002)\u0026mdash;also varied significantly, with Class 4 assuming more intensive and prolonged care responsibilities. Post hoc Bonferroni-adjusted comparisons indicated that these differences were primarily driven by contrasts involving Class 1 and Class 3, as well as Class 1 and Class 4. Together, these findings validate the class distinctions and underscore the heterogeneity in psychosocial functioning and care demands across caregiver subgroups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Key Psychosocial and Caregiving Variables Across Latent Classes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHADS_Depression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCDRISC_Tenacity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMSPSS_Family\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCSSE_Behavioral\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCare Duration_Months\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDaily Care_Hours\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.15\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e229.791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e300.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.369\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Values are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. P-values from Kruskal\u0026ndash;Wallis tests.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study identified four distinct latent classes among caregivers of colorectal cancer patients with permanent ostomies, based on resilience, psychological burden, and caregiving self-efficacy. By integrating both vulnerability and strength-based indicators, this study provides a comprehensive framework to understand caregiver heterogeneity and inform targeted supportive strategies in oncology nursing. The use of LCA offers not only statistical precision, but also clinical insight into how demographic and psychosocial factors combine to shape caregiving experiences.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Characterizing Heterogeneous Caregiver Subtypes\u003c/h2\u003e\u003cp\u003eThe findings revealed that caregivers do not constitute a uniform population. Instead, they can be empirically grouped into four classes, each with distinct risk profiles, resources, and demographic compositions.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.1.1 Class 1: Low Resilience with High Burden (n\u0026thinsp;=\u0026thinsp;146)\u003c/h2\u003e\u003cp\u003eThis group demonstrated the most psychologically vulnerable profile, with the highest levels of depressive symptoms, low perceived family support, and the lowest caregiving self-efficacy. They were predominantly composed of caregivers with lower education levels, lower income, and higher rates of unemployment or part-time employment. Most were women (\u0026asymp;\u0026thinsp;70%) aged around 50, and a significant proportion were spouses or other non-adult-child caregivers.\u003c/p\u003e\u003cp\u003eThese characteristics align with established predictors of caregiver distress, including limited education, economic strain, and gender-based caregiving expectations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Importantly, the combination of structural disadvantages and weak internal resources may lead to compounding vulnerability [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This group mirrors what Liu et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] have described as \"high-risk caregivers,\" warranting proactive identification and early psychosocial intervention.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.1.2 Class 2: High Support but Low Internal Resources (n\u0026thinsp;=\u0026thinsp;136)\u003c/h2\u003e\u003cp\u003eOf particular interest is the discrepancy observed in Class 2 (high support but low internal resources): despite reporting high levels of perceived family support, these caregivers showed low internal efficacy and elevated depressive symptoms. Demographically, these were mostly older adults (mean age\u0026thinsp;\u0026asymp;\u0026thinsp;62), with over half being retired spousal caregivers\u0026mdash;typically women with limited formal education. Despite the presence of external support, their internal psychological resources appeared depleted.\u003c/p\u003e\u003cp\u003eThis dissociation suggests that perceived support is not always protective, particularly in the absence of intrapersonal strengths. This phenomenon aligns with the concept of \u0026ldquo;silent burden\u0026rdquo; in spousal caregivers, who may suppress emotional needs due to normative expectations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Their high vulnerability, often under-recognized, underscores the need for psychological empowerment interventions beyond family support systems. These patterns highlight the need to assess both environmental and intrapsychic resources shaping adaptation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.1.3 Class 3: Resilient Caregivers (n\u0026thinsp;=\u0026thinsp;161)\u003c/h2\u003e\u003cp\u003eCaregivers in this class reported the highest scores in resilience and caregiving efficacy, along with the lowest depression levels. They were younger (mean age\u0026thinsp;\u0026asymp;\u0026thinsp;47), more educated (over 40% with college or higher), and more likely to be adult children of the patient. Employment rates were high, and many reported middle to high income.\u003c/p\u003e\u003cp\u003eThis group reflects what Gaugler et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] describe as \u0026ldquo;adaptive caregivers\u0026rdquo; who leverage social, cognitive, and educational resources to buffer the psychological demands of caregiving. Their resilience may stem from access to instrumental support, flexible coping strategies, and greater autonomy. Importantly, they demonstrate that caregiving does not inevitably lead to psychological distress, supporting a strengths-based approach to caregiver assessment [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.1.4 Class 4: Enduring Care with Tenacity (n\u0026thinsp;=\u0026thinsp;136)\u003c/h2\u003e\u003cp\u003eThis group exhibited moderate resilience and psychological functioning, despite reporting the longest caregiving duration and highest daily care hours. Demographically, they were balanced in age (mean\u0026thinsp;\u0026asymp;\u0026thinsp;54), often adult children, and held diverse educational and occupational statuses.\u003c/p\u003e\u003cp\u003eWhile currently stable, their prolonged intensity of care may predispose them to delayed exhaustion. This aligns with the literature on \u0026ldquo;chronic caregivers\u0026rdquo; who maintain functional stability but are at risk of attrition without timely support [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Oncology nurses must be attuned to subtle signs of wear-and-tear, even in caregivers who do not self-report distress.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2 A Dual-Axis Framework: Burden and Resilience Interact, Not Compete\u003c/h2\u003e\u003cp\u003eThe inverse profiles of Class 1 and Class 3 illustrate a central insight: resilience and burden are not endpoints of a single continuum, but distinct dimensions that interact to shape psychological outcomes. This supports a dual-axis model, wherein caregivers can be high in both burden and resilience (Class 3), or low in both (Class 1), depending on personal and contextual factors [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis conceptualization resonates with the stress-appraisal-coping theory [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], as well as recent models proposing that resilience reflects not just recovery from stress, but ongoing adaptive capacity in the face of adversity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It challenges oncology teams to assess not only distress symptoms, but also protective mechanisms like tenacity, problem-solving efficacy, and perceived mastery.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Implications for Oncology Nursing Assessment and Intervention\u003c/h2\u003e\u003cp\u003eThese findings hold significant relevance for clinical oncology nursing. First, they validate the need for proactive and multidimensional caregiver assessment, going beyond burden screening to include indicators of internal capacity and role identity. Nurses are in a unique position to initiate early stratification based on class-like profiles using brief instruments embedded into intake or survivorship planning.\u003c/p\u003e\u003cp\u003eSecond, the findings suggest stratified intervention pathways:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eClass 1 (low resilience with high burden) caregivers may benefit from multicomponent support including psychoeducation, counseling, and socioeconomic linkage.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eClass 2 (high support but low internal resources) caregivers need psychological empowerment strategies\u0026mdash;such as motivational interviewing or structured skills training\u0026mdash;to rebuild self-efficacy.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eClass 3 (resilient) caregivers require ongoing monitoring and access to peer or respite programs to sustain long-term care provision.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eClass 4 (enduring care with tenacity) caregivers represent an asset group\u0026mdash;individuals who could serve as mentors or resource models within community-based caregiver networks.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis class-based logic supports the move toward precision psycho-oncology, where the right support is delivered to the right caregiver at the right time [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It also aligns with caregiver-inclusive care models advocated by the World Health Organization and the European Oncology Nursing Society [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Toward Integrated, System-Level Application\u003c/h2\u003e\u003cp\u003eBeyond individual triage, this classification has implications for systemic policy. The profiles could be integrated into interdisciplinary rounds, shared across oncology care teams, and used to coordinate referrals to psycho-oncology, social work, and community organizations. Class-stratified data could also inform institutional caregiver education curricula and care quality indicators.\u003c/p\u003e\u003cp\u003eFurthermore, this approach offers an empirical foundation for longitudinal caregiver monitoring, allowing health systems to detect shifting needs over time, particularly as care trajectories evolve into survivorship or terminal phases.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Methodological Contributions, Limitations, and Future Directions\u003c/h2\u003e\u003cp\u003eThis study advances caregiver research in several ways. First, it applies LCA to a clinically relevant population\u0026mdash;family caregivers of colorectal cancer patients with ostomies\u0026mdash;rarely analyzed with person-centered methods. Second, it integrates burden and resilience indicators, countering the historical overemphasis on negative outcomes. Third, the large and demographically diverse sample enhances generalizability across care relationships and social strata.\u003c/p\u003e\u003cp\u003eHowever, several limitations must be acknowledged. The cross-sectional design precludes causal inference or longitudinal trajectory mapping. Although class profiles were statistically robust, their temporal stability and responsiveness to intervention remain unknown. Additionally, caregiver outcomes were self-reported, potentially subject to social desirability or underreporting biases, particularly in older spousal caregivers.\u003c/p\u003e\u003cp\u003eFuture studies should pursue longitudinal validation of class transitions and test intervention effectiveness by class. Integrating biological or behavioral indicators (e.g., cortisol, sleep patterns) could enrich understanding of resilience under sustained caregiving stress. Moreover, cross-cultural replication would be valuable, as caregiving norms and support structures vary widely across health systems.\u003c/p\u003e\u003cp\u003eFinally, the development of class-informed clinical screening tools and their integration into nursing workflow remains a practical next step. Brief tools aligned with latent profiles\u0026mdash;using validated indicators such as the HADS, CDRISC, or self-efficacy scales\u0026mdash;could support triage and early referral without overwhelming frontline staff.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study highlights the substantial psychosocial heterogeneity among family caregivers of colorectal cancer patients with permanent ostomies. Using a person-centered analytic approach, we identified distinct patterns of resilience, psychological burden, and caregiving efficacy that cannot be captured by aggregate measures alone. These findings underscore the need to move beyond binary assessments of distress and toward a more nuanced, multidimensional understanding of caregiver functioning. For oncology nursing, this evidence supports the integration of psychosocial profiling into routine caregiver assessments. Tailoring interventions to reflect caregivers\u0026apos; specific strengths and needs\u0026mdash;rather than relying on one-size-fits-all approaches\u0026mdash;may improve early identification of risk, optimize resource allocation, and enhance caregiver well-being. This paradigm also aligns with broader movements in precision psycho-oncology and person-centered care. Future research should examine how caregiver profiles evolve over time, respond to targeted interventions, and influence patient outcomes. Embedding class-informed frameworks into multidisciplinary cancer care has the potential to strengthen the sustainability, humanity, and impact of caregiver support across the oncology trajectory.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS. H. J. and J. Q. contributed equally to this work and are co-first authors. S. M. C. is the corresponding author. S. H. J. and J. Q.: study design, data collection, statistical analysis, and manuscript drafting. S.M. C.:supervision, critical review, and manuscript editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received non-financial support from the China Social Welfare Foundation (Grant No. HLCXT-20230705). The Foundation provided administrative and logistical assistance but did not provide direct financial funding. It had no role in study design, data collection, data analysis, data interpretation, manuscript preparation, or the decision to submit the manuscript for publication. The views expressed are solely those of the authors and do not necessarily reflect the policies or endorsements of the China Social Welfare Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration Of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the participants who contributed their time and insights to this study. We also thank the clinical staff for their assistance in recruitment and coordination efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo AI tools/services were used during the preparation of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYang F, Cui S, Cai M, Feng F, Zhao M, Sun M, Zhang W (2024) The experiences of family resilience in patients with permanent colostomy and their spouses: A dyadic qualitative study. European journal of oncology nursing : the official journal of European Oncology Nursing Society 70:102590. doi:10.1016/j.ejon.2024.102590\u003c/li\u003e\n\u003cli\u003eThomas Hebdon MC, Coombs LA, Reed P, Crane TE, Badger TA (2021) Self-efficacy in caregivers of adults diagnosed with cancer: An integrative review. 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Springer New York, New York, NY, pp 1913-1915. doi:10.1007/978-1-4419-1005-9_215\u003c/li\u003e\n\u003cli\u003eKent EE, Rowland JH, Northouse L, Litzelman K, Chou WY, Shelburne N, Timura C, O\u0026apos;Mara A, Huss K (2016) Caring for caregivers and patients: Research and clinical priorities for informal cancer caregiving. Cancer 122 (13):1987-1995. doi:10.1002/cncr.29939\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (2018) Integrating palliative care and symptom relief into the response to humanitarian emergencies and crises: a WHO guide. Geneva: WHO. https://apps.who.int/iris/handle/10665/274565. \u003c/li\u003e\n\u003cli\u003eEuropean Oncology Nursing Society (2021) EONS Strategy 2021\u0026ndash;2025: Cancer Nursing Across the Cancer Continuum. https://cancernurse.eu/our-work/eons-strategy-2021-2025/. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Family caregivers, Ostomy care, Resilience, Self-efficacy, Latent class analysis","lastPublishedDoi":"10.21203/rs.3.rs-7574295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7574295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo identify latent psychosocial profiles among colorectal cancer caregivers based on resilience, depression, and caregiving efficacy, and to examine the sociodemographic predictors and outcome differences associated with each subgroup.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional survey was conducted among 579 family caregivers in China. Standardized instruments assessed resilience, depression, caregiving self-efficacy, and perceived family support. Latent class analysis was used to identify caregiver profiles. Multinomial logistic regression examined sociodemographic predictors. Differences in psychological outcomes were compared across classes using Kruskal\u0026ndash;Wallis tests with Bonferroni-adjusted post hoc analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFour distinct caregiver profiles emerged, characterized respectively by (1) low resilience with high burden, (2) high perceived support but low internal resources, (3) high resilience and self-efficacy with low distress, and (4) enduring caregiving with moderate emotional strain. Education, income, employment status, and caregiving relationship were significant predictors of class membership. Psychological outcomes\u0026mdash;including depression, efficacy, resilience, and family support\u0026mdash;varied significantly across profiles (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eCaregivers differ widely in coping patterns and risk levels. Psychosocial profiling offers a more person-centered understanding of caregiver needs than traditional unidimensional assessments. Targeted support based on profile-specific risks may improve caregiver outcomes and optimize oncology nursing care.\u003c/p\u003e","manuscriptTitle":"Psychosocial Profiles of Family Caregivers for Colorectal Cancer Patients with Permanent Ostomies: A Latent Class Analysis of Resilience, Depression, and Self-Efficacy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 01:18:03","doi":"10.21203/rs.3.rs-7574295/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"878fa8b4-b688-41ec-9abe-73f477c479b3","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T10:24:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 01:18:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7574295","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7574295","identity":"rs-7574295","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}