The Effects of Family Characteristics, Emotional Expression and Early Social Work Intervention on Illness Adaptation Among Children with Cancer and Their Parents

The Effects of Family Characteristics, Emotional Expression and Early Social Work Intervention on Illness Adaptation Among Children with Cancer and Their Parents | 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 The Effects of Family Characteristics, Emotional Expression and Early Social Work Intervention on Illness Adaptation Among Children with Cancer and Their Parents Yiti Tung, Yuan Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7036178/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: This study investigated how family characteristics affect emotional expression and illness adaptation among children with cancer and their parents, using structural equation modeling (SEM). It also examined the moderating role of early social work intervention in this process, emphasizing its importance in addressing emotional, psychological, financial, and social support needs to strengthen coping, resilience, and quality of life during pediatric oncology treatment. Methods: The study included 68 families of pediatric cancer patients (154 children and parents). Data were collected via structured questionnaires and analyzed using SEM with the lavaan package in R. The measurement model’s validity and reliability were assessed, and direct, indirect, and moderating effects were examined. Results: The SEM showed good fit to the proposed illness adaptation model. Family characteristics significantly affected emotional expression (γ = −.27) and illness adaptation (γ = −.34). Emotional expression negatively impacted illness adaptation (β = −.53), indicating that greater emotional distress was linked to poorer adaptation. Early social work intervention had significant positive effects on emotional expression (β = .65) and illness adaptation (β = .68), and moderated the effect of family characteristics on adaptation, especially benefiting families with fewer internal resources. Conclusion: Early social work intervention plays a vital role in pediatric oncology by reducing emotional distress, buffering negative family dynamics, and promoting resilience. These findings support integrating social work services early in treatment to enhance adaptation and family well-being. Childhood Cancer Illness Adaptation Social Work Structural Equation Modeling (SEM) Figures Figure 1 Figure 2 Figure 3 Background Childhood cancer is a highly challenging and complex medical condition, profoundly impacting not only the patient but also the entire family system. The diagnosis and ensuing treatment process place considerable physical, emotional, and psychological burdens on children and their parents [ 1 – 3 ]. Families must contend with a host of stressors, including anxiety, helplessness, and disruptions to daily routines. These challenges often erode family confidence, interrupt communication between healthcare professionals and families, and may even compromise the child’s treatment process [ 1 , 4 – 5 ]. Family characteristics such as communication patterns, relational dynamics, coping resources, and overall resilience play a major role in shaping how families adapt to pediatric illness [ 7 – 8 ]. Research shows that emotional expressions within the family can either alleviate or intensify the distress associated with childhood cancer, serving as a critical determinant of adaptation outcomes [ 13 – 16 ]. Advances in medical technology have significantly improved survival rates for pediatric oncology patients. With adherence to comprehensive treatment plans, many children are now able to recover and reintegrate into school and community life following treatment. Nevertheless, the psychosocial repercussions of the illness, including emotional distress, family strain, and economic hardship, often persist throughout the disease trajectory and beyond [ 1 , 7 ]. In this context, early social work intervention has been increasingly recognized as a vital component of family support in pediatric oncology [ 6 , 9 ]. Social workers facilitate access to medical, psychological, and social resources, helping to foster resilience within the family unit. Empirical studies indicate that early, targeted social work interventions can reduce emotional distress, enhance coping strategies, and improve long-term psychosocial outcomes for both children and families [ 10 – 11 , 17 – 18 ]. Furthermore, such interventions may buffer the adverse effects of challenging family characteristics and emotional dynamics, supporting more effective adaptation throughout the disease course [ 19 – 20 ]. Taiwan’s comprehensive social welfare system reflects the government's strong commitment to citizen well-being, particularly in healthcare. The National Health Insurance (NHI) program ensures accessible, high-quality medical services for all citizens in Taiwan. Notably, the system guarantees that patients with severe illnesses are not deprived of essential medical care due to the significant financial burden of treatment costs. The Taiwan Pediatric Oncology Group (TPOG) is committed to advancing pediatric oncology research and developing collaborative treatment protocols to enhance cancer cure rates. TPOG strives for a future where no child dies from cancer and aims to achieve treatment outcomes with minimal or no side effects. Advances in medical technology offer hope that the psychosocial needs of children with cancer and their families will be addressed, which could lead to better illness adaptation and, in turn, improved treatment outcomes. A holistic, multidisciplinary approach anchored in medical and psychosocial care, particularly early social work intervention, is essential to support adaptation, resilience, and well-being in these families. The purpose of this study was to employ structural equation modeling (SEM) to investigate how family characteristics and early social work intervention influence emotional expression and illness adaptation among children with cancer and their parents. Methods Study Design This study adopted Structural Equation Modeling (SEM), conducted using the lavaan package within the R statistical programming language, to investigate the structural relationships between family characteristics, emotional expression, early social work intervention, and illness adaptation among children with cancer and their parents. Sample Participants were recruited through the Taiwan Childhood Cancer Foundation and referrals from pediatric oncology physicians at medical centers, which facilitated the distribution of study information to eligible families. The inclusion criteria were: (a) children and adolescents between 10 and 18 years of age diagnosed with any form of cancer were at least 12 months post-diagnosis, (b) currently receiving or having completed treatment at a medical center in Taiwan, and (c) able to comprehend and respond to the questionnaires. The parent sample comprised the primary caregivers of the participating children and adolescents. Data Collection Data were collected over a 24 month period. A total of 154 questionnaires were gathered from 68 families, including 68 questionnaires completed by children and adolescents and 86 questionnaires completed by their parents. Both child and parent questionnaires used the same survey design. To ensure data quality, the questionnaires were conducted by the author, who were available to clarify questions and provide support to participants as needed. All questionnaires were anonymous. Ethics Ethics approval for the study was granted by the Institutional Review Board (IRB) of the participating medical centers. Informed consent was obtained from all participating parents, and verbal or written assent was secured from all children and adolescents prior to their participation. Conceptual Framework Previous studies have indicated that family characteristics, emotional expression, and early social work intervention may interact and jointly affect the illness adaptation of children with cancer and their parents. In this context, early social work intervention may serve as a buffering mechanism that mitigates the adverse impact of family characteristics and emotional expression on illness adaptation. To investigate these relationships, the present study proposes a structural model hypothesis (Figure 1) including four key elements: family characteristics, emotional expression, early social work intervention, and illness adaptation. This study aims to address the following seven research questions: Does the proposed illness adaptation model fit the data collected in this study? Do family characteristics have a direct effect on illness adaptation? Does early social work intervention have a direct effect on illness adaptation? Does emotional expression have a direct effect on illness adaptation? Do family characteristics have an indirect effect on illness adaptation through emotional expression? Does early social work intervention have an indirect effect on illness adaptation through emotional expression? Do family characteristics have an indirect effect on illness adaptation through early social work intervention? Hypotheses H1. Family characteristics have a significant direct effect on the emotional expression and illness adaptation of children with cancer and their parents. H2. Emotional expression has a significant direct effect on the illness adaptation of children with cancer and their parents. H3. Early social work intervention has a significant direct effect on the illness adaptation of children with cancer and their parents. Measurements Questionnaire Content and Confirmatory Factor Analysis The questionnaires comprised two sections: (1) Demographic and background information included the child’s age, sex, diagnosis, and treatment status (active treatment vs. post-treatment), as well as the parent’s age, educational level, and socioeconomic status, and (2) The study included one latent independent variable, “Family Characteristics” and three latent dependent variables: “Early Social Work Intervention” , “Emotional Expression” and “Illness Adaptation”. All measurement items utilized a five‑point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The latent independent variable was “Family Characteristics” (ξ1), measured by six indicators: social capital (X1), education (X2), communication (X3), parent–child relationship (X4), marital relationship (X5), and problem-solving ability (X6). This section of the questionnaire consisted of 18 items designed to assess family characteristics. Confirmatory factor analysis (CFA) indicated that the factor loadings across the six dimensions ranged from .41 to .71, .45 to .50, .53 to .75, .57 to .66, .31 to .42, and .43 to .62, respectively, explaining 71.2% of the total variance. The model fit indices were satisfactory (GFI = .91, NNFI = .88, CFI = .92, RMSEA = .066), and the internal consistency (Cronbach’s α) for the six dimensions was .71, .76, .82, .81, .76, and .72, indicating satisfactory reliability and validity. The three latent dependent variables were “ Early social work intervention ” (η1), “ Emotional expression” (η2), “ Illness adaptation” (η3). Early social work intervention was measured by three indicators: timing of intervention (Y1), service content (Y2), and service frequency (Y3). This section of the questionnaire consisted of 9 items designed to assess early social work intervention. The factor loadings for the three dimensions ranged from .75 to .93, .71 to .85, and .70 to .78, respectively, explaining 88.7% of the total variance. The model fit indices were satisfactory (GFI = .95, NNFI = .94, CFI = .92, RMSEA = .072), and the Cronbach’s α values were .89, .86, and .85, indicating good internal consistency. Emotional expression was measured by two indicators: emotional state (Y4) and stress state (Y5). This section of the questionnaire consisted of 6 items designed to assess emotional expression. The factor loadings for the two dimensions ranged from .63 to .70 and .56 to .63, respectively, explaining 52.3% of the total variance. The model fit indices were satisfactory (GFI = .90, NNFI = .89, CFI = .92, RMSEA = .065), and the Cronbach’s α values were .86 and .81, indicating acceptable internal consistency. Illness adaptation was assessed by four indicators: emotional adjustment (Y6), behavioral adaptation (Y7), cognitive restructuring (Y8), and family functioning (Y9). This section of the questionnaire consisted of 12 items designed to assess illness adaptation. The factor loadings for the four dimensions ranged from .73 to .85, .56 to .73, .55 to .63, and .71 to .88, respectively, explaining 62.3% of the total variance. The model fit indices were satisfactory (GFI = .92, NNFI = .93, CFI = .95, RMSEA = .060), and the Cronbach’s α values were .76, .82, .86, and .75, indicating acceptable internal consistency. Statistical Analysis Structural equation modeling (SEM) was conducted to test the proposed theoretical model, including direct, indirect, and moderating effects. All analyses were performed using the lavaan package in the R statistical programming language. Model fit was evaluated using a range of indices, including the chi-square test (χ²), Comparative Fit Index (CFI), Goodness of Fit Index (GFI), Non-Normed Fit Index (NNFI), Adjusted Goodness of Fit Index (AGFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) [21]. Results Sample Analysis This study included a total of 68 pediatric oncology patients, comprising 29 girls and 39 boys. The children were diagnosed with the following types of cancer: acute lymphoblastic leukemia (ALL) (n = 48), acute myeloid leukemia (AML) (n = 8), brain tumors (n = 5), neuroblastoma (n = 3), and lymphoma (n = 4). At the time of data collection, their treatment status was categorized as follows: undergoing active treatment (n = 45), off-treatment/remission phase (n = 15), and recurrence of disease (n = 8). Regarding parental education, 43% of the parents had a college degree or higher. In terms of perceived socioeconomic status, 46% identified their family as middle class, while 21% reported experiencing financial hardship. Additionally, 8% of the families were single-parent households. All participating parents served as the primary caregivers of their children. Model Fit Assessment In this study, the fit between the theoretical model and the observed data was evaluated across three primary dimensions: basic model fit, overall fit, and internal structural fit. The assessment followed the guidelines of Bagozzi and Yi [22], Hair Jr., Anderson, Tatham, and Black[23], Jöreskog and Sörbom[24], Marsh, Balla, and Hau [25], and Rubio, Berg-Weger, and Tebb [26]. In addition, Table 1 presents the correlation matrix of the 15 observed variables, with the majority of the coefficients reaching the p < .05 significance level, indicating strong and meaningful interrelations among the observed indicators. Table 1 . Correlation Matrix of the 15 Observed Variables Overall Model Fit The overall fit was evaluated from three perspectives — absolute fit, incremental fit, and parsimonious fit — as recommended by Hair Jr. et al. [23]. The chi‑square test of overall fit was significant (χ² = 165.465, N = 154, p = .03, p < .05), indicating a rejection of the null hypothesis that the theoretical and observed covariance matrices are equal, suggesting a potential lack of fit between the proposed model and the data. However, the chi‑square statistic is highly sensitive to sample size, and its results must be interpreted with other fit indices to obtain a more comprehensive assessment of the model. This study supplemented the chi-square test with additional fit indices, including the RMSEA, GFI, AGFI, SRMR, NFI, NNFI, CFI, IFI, RFI, PNFI, and PGFI, to comprehensively assess overall model fit. The RMSEA value was .07 (p < .000, threshold .90), both approaching the .90 standard, suggesting that the model accounted for a substantial portion of the observed variances and covariances. With respect to incremental fit measures, the NFI, NNFI, CFI, IFI, and RFI yielded values of .910, .942, .930, .921, and .920, respectively, all exceeding the .90 threshold and confirming an acceptable fit between the theoretical and observed data. The PNFI and PGFI values were .60 and .62, respectively (threshold > .50), further supporting the model’s parsimony and adequacy. These results demonstrate that, despite a significant chi-square value, the theoretical illness adaptation model achieved satisfactory overall fit across absolute, incremental, and parsimonious fit indices. The findings indicate that the proposed model is appropriate for explaining the observed data and can be applied to understanding the illness adaptation process in families of children with cancer. Table 2. Significance Tests and Standardized Estimates of Model Parameters Internal Model Fit The internal structure of the theoretical model was evaluated based on four criteria, as recommended by Bagozzi and Yi [22], Hair Jr. et al. [23], and Rubio et al. [26]. (1) All estimated factor loadings (λ) achieved statistical significance, indicating that the measurement indicators reliably represented their respective latent constructs (Table 2). (2) The individual reliability of the 15 observed indicators, as measured by the squared multiple correlations (R²), ranged from .22 to .77, suggesting an acceptable level of item reliability[26]. (3) The composite reliability (CR) for the four latent variables was .68, .83, .72, and .91, all exceeding the threshold of .60, confirming satisfactory internal consistency. (4) The average variance extracted (AVE) for the four latent variables was .301, .673, .400, and .512. Although the AVE for emotional expression was slightly below the ideal .50, the overall internal model fit was deemed satisfactory. In summary, these results support the reliability and convergent validity of the proposed measurement model, indicating that it is well-suited for representing the theoretical constructs and for subsequent examination of the structural relationships within the illness adaptation model. Table 3 . Results of Internal Quality Assessment of the Model Structural Model of the Illness Adaptation As shown in Table 2, the direct effect of family characteristics on illness adaptation was negative (γ₁ = −.34), indicating that weaker family characteristics were associated with lower levels of illness adaptation among children and their parents. Similarly, the direct effect of family characteristics on emotional expression was negative (γ₂ = −.27). These results highlight the important role of family characteristics in shaping both emotional expression and illness adaptation outcomes. Early social work intervention had significant direct effects on emotional expression (β₁ = .65) and illness adaptation (β₂ = .68). These findings indicate that more frequent and intensive early social work intervention was associated with better illness adaptation among children and their parents. Moreover, emotional expression had a significant direct effect on illness adaptation (β₃ = −.53), indicating that higher levels of emotional distress were associated with lower levels of illness adaptation. These results highlight the critical role of early social work intervention in supporting families, both by facilitating illness adaptation and by alleviating negative emotional expressions. Measurement Model of Illness Adaptation Direct Effects of Latent Variables on Their Observed Indicators As shown in Figure 2, the direct effects of family characteristics on its six observed indicators were .526, .470, .710, .610, .480, and .550, all reaching statistical significance (p < .05). The latent variable Emotional Expression was measured by emotional state and stress state, with direct effects of .670 and .590, respectively, both significant (p < .05). Early Social Work Intervention was measured by timing of intervention, service content, and service frequency, with direct effects of .760, .670, and .590, respectively, all significant (p < .05). Finally, Illness Adaptation was measured by four observed indicators: emotional adjustment, behavioral adaptation, cognitive adjustment, and family functioning. The direct effects were .830, .620, .570, and .810, respectively, with all indicators achieving statistical significance (p < .05). Residual Variance and Indicator Reliability According to Figure 2, the residual variances of the six observed indicators for Family Characteristics were as follows: social capital (.41), education (.71), communication (.60), parent–child relationship (.51), marital relationship (.53), and problem solving (.68). These results indicate that the latent variable Family Characteristics explained approximately 59%, 29%, 40%, 49%, 47%, and 32% of the variance for these indicators, respectively. For Emotional Expression , the residual variances were .34 for emotional state and .48 for stress state, indicating that Emotional Expression accounted for approximately 66% and 52% of the variance, respectively. For Early Social Work Intervention , the residual variances were .20 for timing of intervention, .37 for service content, and .28 for service frequency, indicating that the latent construct explained 80%, 63%, and 72% of the variance, respectively. For Illness Adaptation , the residual variances were .38 for emotional adjustment, .53 for behavioral adaptation, .20 for cognitive adjustment, and .38 for family functioning, suggesting that the latent variable explained 62%, 47%, 80%, and 62% of the variance for these indicators, respectively. Discussion The Relationship Between Family Characteristics, Early Social Work Intervention, and Illness Adaptation Early social work intervention emerged as a significant mediator. The path from family characteristics to early social work intervention was significant (β = .125), and early social work intervention demonstrated a strong direct effect on illness adaptation (β = .856). Meanwhile, the direct path from family characteristics to illness adaptation became negative (β = −.235), suggesting that early social work intervention functioned as both a mediator and potentially a moderator in the relationship between family characteristics and illness adaptation. To further explore this interaction, early social work intervention was divided into high- and low-engagement groups (Fig. 3 ). The results showed that early social work intervention weakened the direct link between family characteristics and illness adaptation. In families with weaker characteristics (e.g., limited resources or strained relationships), high-intensity early social work intervention was associated with significant improvements in illness adaptation for children and their families. Conversely, in families with stronger characteristics, early social work intervention had a relatively smaller effect, suggesting that its buffering role is most evident when family characteristics are weaker. These findings underscore the value of early social work intervention as a targeted, context-sensitive approach and highlight the importance of assessing family characteristics early in the treatment process to enable social workers and medical teams to support services. The Relationship Between Emotional Expression and Illness Adaptation Regarding the direct effect of emotional expression on illness adaptation, the results indicated a significant negative association. Specifically, higher levels of emotional expression—such as anxiety, sadness, or emotional distress—were associated with lower levels of illness adaptation. Poor emotional regulation, including persistent anxiety, depressive symptoms, or emotional exhaustion, can impair caregivers’ psychological resilience and stress-coping abilities. This, in turn, undermines their capacity to make clear medical decisions, communicate effectively, and manage daily caregiving demands, ultimately leading to poorer illness adaptation. Such disruptions weaken overall family functioning—particularly in problem-solving and support provision—which are critical for adapting to pediatric illness. Caregivers experiencing emotional distress are often less motivated or willing to seek external resources, such as social work support or community services. As a result, families may struggle to build a sense of control or envision a manageable future. Early Social Work Intervention, Emotional Expression, and Illness Adaptation This study shows that early social work intervention exerts significant direct positive effects on both emotional expression and illness adaptation among families of children with cancer. Social workers in pediatric oncology settings have been shown to reduce parental anxiety and depression and improve overall psychosocial outcomes [ 7 , 15 ]. The findings highlight the important role of early social work intervention in facilitating illness adaptation. In families grappling with a new cancer diagnosis, heightened emotional distress often impedes the ability to adapt effectively. Early social work intervention can buffer this effect and support better adaptation. Moreover, early social work intervention appears particularly protective for families with weaker internal characteristics, such as limited communication or coping resources, and is essential for promoting long-term family resilience in the context of pediatric cancer. Limitation This SEM study on illness adaptation in families of children with cancer has several limitations. The sample, drawn from medical centers in Taiwan, may limit the generalizability of the findings to other cultural or clinical contexts. The modest sample size may also have reduced the statistical power to detect smaller effects or subtle interactions. Conclusion This study advances the understanding of illness adaptation in families of children with cancer by highlighting the complex interplay between family characteristics, emotional expression, and early social work intervention. The results demonstrate that early social work intervention serves both as a direct support and an indirect catalyst, buffering the adverse effects of weaker family characteristics and facilitating more adaptive coping and resilience. Declarations Funding Declaration: This study received research funding from the National Science and Technology Council (NSTC), Taiwan. Author Contribution Yiti Tung wrote the main manuscript text.Yuan Lin prepared figures 1-3. All authors reviewed the manuscript. Acknowledgement I would like to express sincere gratitude to the Taiwan Childhood Cancer Foundation and the pediatric oncology physicians at the participating medical centers for their valuable support and assistance in participant recruitment and data collection for this study. References Hagger, M. S., & Orbell, S. (2022). The common sense model of illness self-regulation: A conceptual review and proposed extended model. 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Journal of the Academy of Marketing Science, 16 (1), 74–94. Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Prentice Hall. Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language . Scientific Software International. Marsh, H. W., Balla, J. R., & Hau, K. T. (1996). An evaluation of incremental fit indices: A clarification of mathematical and empirical properties. Structural Equation Modeling: A Multidisciplinary Journal, 3 (4), 315–353. Rubio, D. M., Berg-Weger, M., & Tebb, S. S. (2001). Comparing the well-being of post-caregivers and non-caregivers. American Journal of Alzheimer's Disease & Other Dementias, 16 (2), 97–101. https://doi.org/10.1177/153331750101600213 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7036178","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503726077,"identity":"48ef7cb5-0f6c-445c-9400-3e31e7b3ba9f","order_by":0,"name":"Yiti Tung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYPACGwYGZuYGGM8Av2I2ZhCZBtTCCNSSANOSQFDLYSAmVovB/f5j0gW/zkfztzM2MBf+uJPYwN68TYLxx2HcWo4xs0nP7LudO+MwUMuMhGeJDTzHyiQYEgho4e25ndsA0sKTcDixQSLHDKjlNiEt53Lnw7XIvyFCC8+PA7kbELbw4NcieSzZ2Jq3ITl3I1DLYZ60Z8ZtPGnFFglp/3Fq4Tt88OFtnj92ufPOHz74mMfmjmw/++GNNz7YpOHUonAASDC2QTgHQIgNxErAqYGBQb4BRP6B8w/gUTsKRsEoGAUjFQAAQ6BWZLOE+MYAAAAASUVORK5CYII=","orcid":"","institution":"National Taipei University","correspondingAuthor":true,"prefix":"","firstName":"Yiti","middleName":"","lastName":"Tung","suffix":""},{"id":503726078,"identity":"0916346d-f5f2-434d-850b-8720288a7248","order_by":1,"name":"Yuan Lin","email":"","orcid":"","institution":"National Taipei University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-07-03 09:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7036178/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7036178/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89668200,"identity":"d6ad11df-b259-4417-b4c0-0d0d315f53dc","added_by":"auto","created_at":"2025-08-22 12:23:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual Framework\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7036178/v1/18e0766c6cfb19e768d77222.png"},{"id":89666098,"identity":"83d0dbd1-f75b-4a48-b57c-7fadf471991a","added_by":"auto","created_at":"2025-08-22 12:07:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStructural Model of Latent Variable Relationships\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7036178/v1/f842b38708b4ca9a297efa62.png"},{"id":89667523,"identity":"9c793e66-6d25-40a1-af6e-49022cbde893","added_by":"auto","created_at":"2025-08-22 12:15:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32402,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction Effect of Early Social Work Intervention between Family Characteristics and Illness Adaptation\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7036178/v1/e2faed4380f5b60aedd5967d.png"},{"id":90267994,"identity":"7d1c17f5-761d-4205-863a-01e6aa4b668b","added_by":"auto","created_at":"2025-08-31 19:16:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1119558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7036178/v1/b3349513-81f2-4476-b6cb-f0608d8a13f0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effects of Family Characteristics, Emotional Expression and Early Social Work Intervention on Illness Adaptation Among Children with Cancer and Their Parents","fulltext":[{"header":"Background","content":"\u003cp\u003eChildhood cancer is a highly challenging and complex medical condition, profoundly impacting not only the patient but also the entire family system. The diagnosis and ensuing treatment process place considerable physical, emotional, and psychological burdens on children and their parents [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. Families must contend with a host of stressors, including anxiety, helplessness, and disruptions to daily routines. These challenges often erode family confidence, interrupt communication between healthcare professionals and families, and may even compromise the child\u0026rsquo;s treatment process [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFamily characteristics such as communication patterns, relational dynamics, coping resources, and overall resilience play a major role in shaping how families adapt to pediatric illness [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research shows that emotional expressions within the family can either alleviate or intensify the distress associated with childhood cancer, serving as a critical determinant of adaptation outcomes [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Advances in medical technology have significantly improved survival rates for pediatric oncology patients. With adherence to comprehensive treatment plans, many children are now able to recover and reintegrate into school and community life following treatment. Nevertheless, the psychosocial repercussions of the illness, including emotional distress, family strain, and economic hardship, often persist throughout the disease trajectory and beyond [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn this context, early social work intervention has been increasingly recognized as a vital component of family support in pediatric oncology [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Social workers facilitate access to medical, psychological, and social resources, helping to foster resilience within the family unit. Empirical studies indicate that early, targeted social work interventions can reduce emotional distress, enhance coping strategies, and improve long-term psychosocial outcomes for both children and families [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, such interventions may buffer the adverse effects of challenging family characteristics and emotional dynamics, supporting more effective adaptation throughout the disease course [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTaiwan\u0026rsquo;s comprehensive social welfare system reflects the government\u0026apos;s strong commitment to citizen well-being, particularly in healthcare. The National Health Insurance (NHI) program ensures accessible, high-quality medical services for all citizens in Taiwan. Notably, the system guarantees that patients with severe illnesses are not deprived of essential medical care due to the significant financial burden of treatment costs. The Taiwan Pediatric Oncology Group (TPOG) is committed to advancing pediatric oncology research and developing collaborative treatment protocols to enhance cancer cure rates. TPOG strives for a future where no child dies from cancer and aims to achieve treatment outcomes with minimal or no side effects.\u003c/p\u003e\n\u003cp\u003eAdvances in medical technology offer hope that the psychosocial needs of children with cancer and their families will be addressed, which could lead to better illness adaptation and, in turn, improved treatment outcomes. A holistic, multidisciplinary approach anchored in medical and psychosocial care, particularly early social work intervention, is essential to support adaptation, resilience, and well-being in these families.\u003c/p\u003e\n\u003cp\u003eThe purpose of this study was to employ structural equation modeling (SEM) to investigate how family characteristics and early social work intervention influence emotional expression and illness adaptation among children with cancer and their parents.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopted Structural Equation Modeling (SEM), conducted using the \u003cem\u003elavaan\u003c/em\u003e package within the R statistical programming language, to investigate the structural relationships between family characteristics, emotional expression, early social work intervention, and illness adaptation among children with cancer and their parents.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSample\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were recruited through the Taiwan Childhood Cancer Foundation and referrals from pediatric oncology physicians at medical centers, which facilitated the distribution of study information to eligible families. The inclusion criteria were: (a) children and adolescents between 10 and 18 years of age diagnosed with any form of cancer were at least 12 months post-diagnosis, (b) currently receiving or having completed treatment at a medical center in Taiwan, and (c) able to comprehend and respond to the questionnaires. The parent sample comprised the primary caregivers of the participating children and adolescents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Collection\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData were collected over a 24 month period. A total of 154 questionnaires were gathered from 68 families, including 68 questionnaires completed by children and adolescents and 86 questionnaires completed by their parents. Both child and parent questionnaires used the same survey design. To ensure data quality, the questionnaires were conducted by the author, who were available to clarify questions and provide support to participants as needed. All questionnaires were anonymous. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval for the study was granted by the Institutional Review Board (IRB) of the participating medical centers. Informed consent was obtained from all participating parents, and verbal or written assent was secured from all children and adolescents prior to their participation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConceptual\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eFramework\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies have indicated that family characteristics, emotional expression, and early social work intervention may interact and jointly affect the illness adaptation of children with cancer and their parents. In this context, early social work intervention may serve as a buffering mechanism that mitigates the adverse impact of family characteristics and emotional expression on illness adaptation. To investigate these relationships, the present study proposes a structural model hypothesis (Figure 1) including four key elements: family characteristics, emotional expression, early social work intervention, and illness adaptation. This study aims to address the following seven research questions:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eDoes the proposed illness adaptation model fit the data collected in this study?\u003c/li\u003e\n \u003cli\u003eDo family characteristics have a direct effect on illness adaptation?\u003c/li\u003e\n \u003cli\u003eDoes early social work intervention have a direct effect on illness adaptation?\u003c/li\u003e\n \u003cli\u003eDoes emotional expression have a direct effect on illness adaptation?\u003c/li\u003e\n \u003cli\u003eDo family characteristics have an indirect effect on illness adaptation through emotional expression?\u003c/li\u003e\n \u003cli\u003eDoes early social work intervention have an indirect effect on illness adaptation through emotional expression?\u003c/li\u003e\n \u003cli\u003eDo family characteristics have an indirect effect on illness adaptation through early social work intervention?\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003cem\u003eHypotheses\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eH1.\u003c/strong\u003e Family characteristics have a significant direct effect on the emotional expression and illness adaptation of children with cancer and their parents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eEmotional expression has a significant direct effect on the illness adaptation of children with cancer and their parents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3.\u003c/strong\u003e Early social work intervention has a significant direct effect on the illness adaptation of children with cancer and their parents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuestionnaire Content and\u003c/em\u003e\u003c/strong\u003e \u003cem\u003eConfirmatory Factor Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaires comprised two sections: (1) Demographic and background information included the child\u0026rsquo;s age, sex, diagnosis, and treatment status (active treatment vs. post-treatment), as well as the parent\u0026rsquo;s age, educational level, and socioeconomic status, and (2)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe study included one latent independent variable, \u0026ldquo;Family Characteristics\u0026rdquo; and three latent dependent variables: \u0026ldquo;Early Social Work Intervention\u0026rdquo; , \u0026ldquo;Emotional Expression\u0026rdquo; and \u0026ldquo;Illness Adaptation\u0026rdquo;.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAll measurement items utilized a five‑point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).\u003c/p\u003e\n\u003cp\u003eThe latent independent variable was \u0026ldquo;Family Characteristics\u0026rdquo; (\u0026xi;1), measured by six indicators: social capital (X1), education (X2), communication (X3), parent\u0026ndash;child relationship (X4), marital relationship (X5), and problem-solving ability (X6). This section of the questionnaire consisted of 18 items designed to assess family characteristics. Confirmatory factor analysis (CFA) indicated that the factor loadings across the six dimensions ranged from .41 to .71, .45 to .50, .53 to .75, .57 to .66, .31 to .42, and .43 to .62, respectively, explaining 71.2% of the total variance. The model fit indices were satisfactory (GFI = .91, NNFI = .88, CFI = .92, RMSEA = .066), and the internal consistency (Cronbach\u0026rsquo;s \u0026alpha;) for the six dimensions was .71, .76, .82, .81, .76, and .72, indicating satisfactory reliability and validity.\u003c/p\u003e\n\u003cp\u003eThe three latent dependent variables were \u0026ldquo;\u003cstrong\u003eEarly social work intervention\u003c/strong\u003e\u0026rdquo; (\u0026eta;1), \u0026ldquo;\u003cstrong\u003eEmotional expression\u0026rdquo;\u003c/strong\u003e (\u0026eta;2), \u0026ldquo;\u003cstrong\u003eIllness adaptation\u0026rdquo;\u003c/strong\u003e (\u0026eta;3).\u003c/p\u003e\n\u003cp\u003eEarly social work intervention was measured by three indicators: timing of intervention (Y1), service content (Y2), and service frequency (Y3). This section of the questionnaire consisted of 9 items designed to assess early social work intervention. The factor loadings for the three dimensions ranged from .75 to .93, .71 to .85, and .70 to .78, respectively, explaining 88.7% of the total variance. The model fit indices were satisfactory (GFI = .95, NNFI = .94, CFI = .92, RMSEA = .072), and the Cronbach\u0026rsquo;s \u0026alpha; values were .89, .86, and .85, indicating good internal consistency.\u003c/p\u003e\n\u003cp\u003eEmotional expression was measured by two indicators: emotional state (Y4) and stress state (Y5). This section of the questionnaire consisted of 6 items designed to assess emotional expression. The factor loadings for the two dimensions ranged from .63 to .70 and .56 to .63, respectively, explaining 52.3% of the total variance. The model fit indices were satisfactory (GFI = .90, NNFI = .89, CFI = .92, RMSEA = .065), and the Cronbach\u0026rsquo;s \u0026alpha; values were .86 and .81, indicating acceptable internal consistency.\u003c/p\u003e\n\u003cp\u003eIllness adaptation was assessed by four indicators: emotional adjustment (Y6), behavioral adaptation (Y7), cognitive restructuring (Y8), and family functioning (Y9). This section of the questionnaire consisted of 12 items designed to assess illness adaptation. The factor loadings for the four dimensions ranged from .73 to .85, .56 to .73, .55 to .63, and .71 to .88, respectively, explaining 62.3% of the total variance. The model fit indices were satisfactory (GFI = .92, NNFI = .93, CFI = .95, RMSEA = .060), and the Cronbach\u0026rsquo;s \u0026alpha; values were .76, .82, .86, and .75, indicating acceptable internal consistency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStructural equation modeling (SEM) was conducted to test the proposed theoretical model, including direct, indirect, and moderating effects. All analyses were performed using the \u003cem\u003elavaan\u003c/em\u003e package in the R statistical programming language. Model fit was evaluated using a range of indices, including the chi-square test (\u0026chi;\u0026sup2;), Comparative Fit Index (CFI), Goodness of Fit Index (GFI), Non-Normed Fit Index (NNFI), Adjusted Goodness of Fit Index (AGFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) [21].\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSample Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included a total of 68 pediatric oncology patients, comprising 29 girls and 39 boys. The children were diagnosed with the following types of cancer: acute lymphoblastic leukemia (ALL) (n = 48), acute myeloid leukemia (AML) (n = 8), brain tumors (n = 5), neuroblastoma (n = 3), and lymphoma (n = 4). At the time of data collection, their treatment status was categorized as follows: undergoing active treatment (n = 45), off-treatment/remission phase (n = 15), and recurrence of disease (n = 8). Regarding parental education, 43% of the parents had a college degree or higher. In terms of perceived socioeconomic status, 46% identified their family as middle class, while 21% reported experiencing financial hardship. Additionally, 8% of the families were single-parent households. All participating parents served as the primary caregivers of their children.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Fit Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the fit between the theoretical model and the observed data was evaluated across three primary dimensions: basic model fit, overall fit, and internal structural fit. The assessment followed the guidelines of Bagozzi and Yi [22], Hair Jr., Anderson, Tatham, and Black[23], J\u0026ouml;reskog and S\u0026ouml;rbom[24], Marsh, Balla, and Hau [25], and Rubio, Berg-Weger, and Tebb [26]. In addition, Table 1 presents the correlation matrix of the 15 observed variables, with the majority of the coefficients reaching the p \u0026lt; .05 significance level, indicating strong and meaningful interrelations among the observed indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eCorrelation Matrix of the 15 Observed Variables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverall Model Fit\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall fit was evaluated from three perspectives \u0026mdash; absolute fit, incremental fit, and parsimonious fit \u0026mdash; as recommended by Hair Jr. et al. [23]. The chi‑square test of overall fit was significant (\u0026chi;\u0026sup2; = 165.465, N = 154, p = .03, p \u0026lt; .05), indicating a rejection of the null hypothesis that the theoretical and observed covariance matrices are equal, suggesting a potential lack of fit between the proposed model and the data. However, the chi‑square statistic is highly sensitive to sample size, and its results must be interpreted with other fit indices to obtain a more comprehensive assessment of the model.\u003c/p\u003e\n\u003cp\u003eThis study supplemented the chi-square test with additional fit indices, including the RMSEA, GFI, AGFI, SRMR, NFI, NNFI, CFI, IFI, RFI, PNFI, and PGFI, to comprehensively assess overall model fit. The RMSEA value was .07 (p \u0026lt; .000, threshold \u0026lt; .08), indicating an acceptable level of fit. The GFI and AGFI, which measure the proportion of explained variances and covariances, were .922 and .918, respectively (threshold \u0026gt; .90), both approaching the .90 standard, suggesting that the model accounted for a substantial portion of the observed variances and covariances.\u003c/p\u003e\n\u003cp\u003eWith respect to incremental fit measures, the NFI, NNFI, CFI, IFI, and RFI yielded values of .910, .942, .930, .921, and .920, respectively, all exceeding the .90 threshold and confirming an acceptable fit between the theoretical and observed data. The PNFI and PGFI values were .60 and .62, respectively (threshold \u0026gt; .50), further supporting the model\u0026rsquo;s parsimony and adequacy.\u003c/p\u003e\n\u003cp\u003eThese results demonstrate that, despite a significant chi-square value, the theoretical illness adaptation model achieved satisfactory overall fit across absolute, incremental, and parsimonious fit indices. The findings indicate that the proposed model is appropriate for explaining the observed data and can be applied to understanding the illness adaptation process in families of children with cancer.\u003c/p\u003e\n\u003cp\u003eTable 2. Significance Tests and Standardized Estimates of Model Parameters\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInternal Model Fit\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe internal structure of the theoretical model was evaluated based on four criteria, as recommended by Bagozzi and Yi [22], Hair Jr. et al. [23], and Rubio et al. [26]. (1) All estimated factor loadings (\u0026lambda;) achieved statistical significance, indicating that the measurement indicators reliably represented their respective latent constructs (Table 2). (2) The individual reliability of the 15 observed indicators, as measured by the squared multiple correlations (R\u0026sup2;), ranged from .22 to .77, suggesting an acceptable level of item reliability[26]. (3) The composite reliability (CR) for the four latent variables was .68, .83, .72, and .91, all exceeding the threshold of .60, confirming satisfactory internal consistency. (4) The average variance extracted (AVE) for the four latent variables was .301, .673, .400, and .512. Although the AVE for emotional expression was slightly below the ideal .50, the overall internal model fit was deemed satisfactory. In summary, these results support the reliability and convergent validity of the proposed measurement model, indicating that it is well-suited for representing the theoretical constructs and for subsequent examination of the structural relationships within the illness adaptation model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. \u003cem\u003eResults of Internal Quality Assessment of the Model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural Model of the Illness Adaptation \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, the direct effect of family characteristics on illness adaptation was negative (\u0026gamma;₁ = \u0026minus;.34), indicating that weaker family characteristics were associated with lower levels of illness adaptation among children and their parents. Similarly, the direct effect of family characteristics on emotional expression was negative (\u0026gamma;₂ = \u0026minus;.27). These results highlight the important role of family characteristics in shaping both emotional expression and illness adaptation outcomes.\u003c/p\u003e\n\u003cp\u003eEarly social work intervention had significant direct effects on emotional expression (\u0026beta;₁ = .65) and illness adaptation (\u0026beta;₂ = .68). These findings indicate that more frequent and intensive early social work intervention was associated with better illness adaptation among children and their parents. Moreover, emotional expression had a significant direct effect on illness adaptation (\u0026beta;₃ = \u0026minus;.53), indicating that higher levels of emotional distress were associated with lower levels of illness adaptation. These results highlight the critical role of early social work intervention in supporting families, both by facilitating illness adaptation and by alleviating negative emotional expressions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement Model of Illness Adaptation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDirect Effects of Latent Variables on Their Observed Indicators\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, the direct effects of family characteristics on its six observed indicators were .526, .470, .710, .610, .480, and .550, all reaching statistical significance (p \u0026lt; .05). The latent variable \u003cem\u003eEmotional Expression\u003c/em\u003e was measured by emotional state and stress state, with direct effects of .670 and .590, respectively, both significant (p \u0026lt; .05). \u003cem\u003eEarly Social Work Intervention\u003c/em\u003e was measured by timing of intervention, service content, and service frequency, with direct effects of .760, .670, and .590, respectively, all significant (p \u0026lt; .05). Finally, \u003cem\u003eIllness Adaptation\u003c/em\u003e was measured by four observed indicators: emotional adjustment, behavioral adaptation, cognitive adjustment, and family functioning. The direct effects were .830, .620, .570, and .810, respectively, with all indicators achieving statistical significance (p \u0026lt; .05).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResidual Variance and Indicator Reliability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAccording to Figure 2, the residual variances of the six observed indicators for \u003cem\u003eFamily Characteristics\u003c/em\u003e were as follows: social capital (.41), education (.71), communication (.60), parent\u0026ndash;child relationship (.51), marital relationship (.53), and problem solving (.68). These results indicate that the latent variable \u003cem\u003eFamily Characteristics\u003c/em\u003e explained approximately 59%, 29%, 40%, 49%, 47%, and 32% of the variance for these indicators, respectively. For \u003cem\u003eEmotional Expression\u003c/em\u003e, the residual variances were .34 for emotional state and .48 for stress state, indicating that \u003cem\u003eEmotional Expression\u003c/em\u003e accounted for approximately 66% and 52% of the variance, respectively. For \u003cem\u003eEarly Social Work Intervention\u003c/em\u003e, the residual variances were .20 for timing of intervention, .37 for service content, and .28 for service frequency, indicating that the latent construct explained 80%, 63%, and 72% of the variance, respectively. For \u003cem\u003eIllness Adaptation\u003c/em\u003e, the residual variances were .38 for emotional adjustment, .53 for behavioral adaptation, .20 for cognitive adjustment, and .38 for family functioning, suggesting that the latent variable explained 62%, 47%, 80%, and 62% of the variance for these indicators, respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eThe Relationship Between Family Characteristics, Early Social Work Intervention, and Illness Adaptation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eEarly social work intervention emerged as a significant mediator. The path from family characteristics to early social work intervention was significant (β\u0026thinsp;=\u0026thinsp;.125), and early social work intervention demonstrated a strong direct effect on illness adaptation (β\u0026thinsp;=\u0026thinsp;.856). Meanwhile, the direct path from family characteristics to illness adaptation became negative (β = \u0026minus;.235), suggesting that early social work intervention functioned as both a mediator and potentially a moderator in the relationship between family characteristics and illness adaptation.\u003c/p\u003e\u003cp\u003eTo further explore this interaction, early social work intervention was divided into high- and low-engagement groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results showed that early social work intervention weakened the direct link between family characteristics and illness adaptation. In families with weaker characteristics (e.g., limited resources or strained relationships), high-intensity early social work intervention was associated with significant improvements in illness adaptation for children and their families. Conversely, in families with stronger characteristics, early social work intervention had a relatively smaller effect, suggesting that its buffering role is most evident when family characteristics are weaker.\u003c/p\u003e\u003cp\u003eThese findings underscore the value of early social work intervention as a targeted, context-sensitive approach and highlight the importance of assessing family characteristics early in the treatment process to enable social workers and medical teams to support services.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe Relationship Between Emotional Expression and Illness Adaptation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eRegarding the direct effect of emotional expression on illness adaptation, the results indicated a significant negative association. Specifically, higher levels of emotional expression\u0026mdash;such as anxiety, sadness, or emotional distress\u0026mdash;were associated with lower levels of illness adaptation. Poor emotional regulation, including persistent anxiety, depressive symptoms, or emotional exhaustion, can impair caregivers\u0026rsquo; psychological resilience and stress-coping abilities. This, in turn, undermines their capacity to make clear medical decisions, communicate effectively, and manage daily caregiving demands, ultimately leading to poorer illness adaptation.\u003c/p\u003e\u003cp\u003eSuch disruptions weaken overall family functioning\u0026mdash;particularly in problem-solving and support provision\u0026mdash;which are critical for adapting to pediatric illness. Caregivers experiencing emotional distress are often less motivated or willing to seek external resources, such as social work support or community services. As a result, families may struggle to build a sense of control or envision a manageable future.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEarly Social Work Intervention, Emotional Expression, and Illness Adaptation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis study shows that early social work intervention exerts significant direct positive effects on both emotional expression and illness adaptation among families of children with cancer. Social workers in pediatric oncology settings have been shown to reduce parental anxiety and depression and improve overall psychosocial outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The findings highlight the important role of early social work intervention in facilitating illness adaptation. In families grappling with a new cancer diagnosis, heightened emotional distress often impedes the ability to adapt effectively. Early social work intervention can buffer this effect and support better adaptation.\u003c/p\u003e\u003cp\u003eMoreover, early social work intervention appears particularly protective for families with weaker internal characteristics, such as limited communication or coping resources, and is essential for promoting long-term family resilience in the context of pediatric cancer.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis SEM study on illness adaptation in families of children with cancer has several limitations. The sample, drawn from medical centers in Taiwan, may limit the generalizability of the findings to other cultural or clinical contexts. The modest sample size may also have reduced the statistical power to detect smaller effects or subtle interactions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study advances the understanding of illness adaptation in families of children with cancer by highlighting the complex interplay between family characteristics, emotional expression, and early social work intervention. The results demonstrate that early social work intervention serves both as a direct support and an indirect catalyst, buffering the adverse effects of weaker family characteristics and facilitating more adaptive coping and resilience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding Declaration: This study received research funding from the National Science and Technology Council (NSTC), Taiwan.\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eYiti Tung wrote the main manuscript text.Yuan Lin prepared figures 1-3. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eI would like to express sincere gratitude to the Taiwan Childhood Cancer Foundation and the pediatric oncology physicians at the participating medical centers for their valuable support and assistance in participant recruitment and data collection for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHagger, M. S., \u0026amp; Orbell, S. (2022). The common sense model of illness self-regulation: A conceptual review and proposed extended model. \u003cem\u003eHealth Psychology Review, 16\u003c/em\u003e(3), 347\u0026ndash;377. https://doi.org/10.1080/17437199.2021.1878050\u003c/li\u003e\n\u003cli\u003eKazak, A. E., Rourke, M. T., \u0026amp; Navsaria, N. (2009). 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To live close to a person with cancer\u0026mdash;Experiences of family caregivers. \u003cem\u003ePsycho‑Oncology, 23\u003c/em\u003e(12), 1377\u0026ndash;1385. https://doi.org/10.1002/pon.3573\u003c/li\u003e\n\u003cli\u003eWarmerdam, J., Van Steensel, F. J. A., Vancleef, L. M. G., \u0026amp; Crocetti, E. (2019). Anxiety, depression, and posttraumatic stress symptoms in parents of children with cancer: A systematic review. \u003cem\u003ePsycho‑Oncology, 28\u003c/em\u003e(4), 607\u0026ndash;616. https://doi.org/10.1002/pon.4998\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLjungman, L., Cernvall, M., Gr\u0026ouml;nqvist, H., von Essen, L., \u0026amp; Ljungman, G. (2014).\u003c/strong\u003e Long‑term positive and negative psychological late effects for parents of childhood cancer survivors: A systematic review. \u003cem\u003ePLOS ONE, 9\u003c/em\u003e(7), e103340. https://doi.org/10.1371/journal.pone.0103340\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eJones, B. L., Currin‑McCulloch, J., Pelletier, W., Sardi‑Brown, V., Brown, P., \u0026amp; Wiener, L. (2018).\u003c/strong\u003e Psychosocial standards of care for children with cancer and their families: A national survey of pediatric oncology social workers. \u003cem\u003eSocial Work in Health Care, 57\u003c/em\u003e(4), 221\u0026ndash;249. https://doi.org/10.1080/00981389.2018.1441212\u003c/li\u003e\n\u003cli\u003eSteils, N., Moriarty, J., \u0026amp; Manthorpe, J. (2021). What do hospital social workers do? An international scoping review. \u003cem\u003eBritish Journal of Social Work, 51\u003c/em\u003e(5), 1681\u0026ndash;1699. https://doi.org/10.1093/bjsw/bcaa160 \u003c/li\u003e\n\u003cli\u003ePetruzzi, C., Meyer, A., Lane, C., \u0026amp; McCorkle, R. (2024). 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Prentice Hall. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eJ\u0026ouml;reskog, K. G., \u0026amp; S\u0026ouml;rbom, D. (1993).\u003c/strong\u003e\u003cem\u003eLISREL 8: Structural Equation Modeling with the SIMPLIS Command Language\u003c/em\u003e. Scientific Software International. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMarsh, H. W., Balla, J. R., \u0026amp; Hau, K. T. (1996).\u003c/strong\u003e An evaluation of incremental fit indices: A clarification of mathematical and empirical properties. \u003cem\u003eStructural Equation Modeling: A Multidisciplinary Journal, 3\u003c/em\u003e(4), 315\u0026ndash;353. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eRubio, D. M., Berg-Weger, M., \u0026amp; Tebb, S. S. (2001).\u003c/strong\u003e Comparing the well-being of post-caregivers and non-caregivers. \u003cem\u003eAmerican Journal of Alzheimer\u0026apos;s Disease \u0026amp; Other Dementias, 16\u003c/em\u003e(2), 97\u0026ndash;101. https://doi.org/10.1177/153331750101600213\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":"Childhood Cancer, Illness Adaptation, Social Work, Structural Equation Modeling (SEM)","lastPublishedDoi":"10.21203/rs.3.rs-7036178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7036178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e This study investigated how family characteristics affect emotional expression and illness adaptation among children with cancer and their parents, using structural equation modeling (SEM). It also examined the moderating role of early social work intervention in this process, emphasizing its importance in addressing emotional, psychological, financial, and social support needs to strengthen coping, resilience, and quality of life during pediatric oncology treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The study included 68 families of pediatric cancer patients (154 children and parents). Data were collected via structured questionnaires and analyzed using SEM with the \u003cem\u003elavaan\u003c/em\u003e package in R. The measurement model’s validity and reliability were assessed, and direct, indirect, and moderating effects were examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The SEM showed good fit to the proposed illness adaptation model. Family characteristics significantly affected emotional expression (γ = −.27) and illness adaptation (γ = −.34). Emotional expression negatively impacted illness adaptation (β = −.53), indicating that greater emotional distress was linked to poorer adaptation. Early social work intervention had significant positive effects on emotional expression (β = .65) and illness adaptation (β = .68), and moderated the effect of family characteristics on adaptation, especially benefiting families with fewer internal resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Early social work intervention plays a vital role in pediatric oncology by reducing emotional distress, buffering negative family dynamics, and promoting resilience. These findings support integrating social work services early in treatment to enhance adaptation and family well-being.\u003c/p\u003e","manuscriptTitle":"The Effects of Family Characteristics, Emotional Expression and Early Social Work Intervention on Illness Adaptation Among Children with Cancer and Their Parents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 12:07:44","doi":"10.21203/rs.3.rs-7036178/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":"b282c6c3-1437-4d5b-913f-9c21511964c4","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-31T19:08:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 12:07:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7036178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7036178","identity":"rs-7036178","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}