Heterogeneity in benefit finding among breast cancer patients: a latent profile analysis and influencing factors

Objectives: This study investigates factors influencing benefit finding among breast cancer patients based on social cognitive theory and develops a nomogram to predict the probability of low benefit finding in breast cancer patients.

Methods: A study of 666 breast cancer patients in northern Anhui Province (January to December 2024) employed latent profile analysis to identify distinct benefit finding patterns. Potential predictors were identified through univariate analysis, least absolute shrinkage and selection operator regression, and multivariate analysis. Five machine learning algorithms were applied to predict low benefit finding, with performance evaluated via calibration and discriminative power metrics and internally validated using bootstrap resampling.

Results: A two-classification model best fits the data, identifying the low benefit finding category (35%) and the high benefit finding category (65%). XGBoost outperformed other models and was selected as the final model. The model achieved an AUC of 0.945 on the validation set. SHAP analysis quantified each variable’s contribution to predictions, revealing age, medication adherence, anxiety, social support, and depression as key determinants of benefit finding.

Conclusion: This study applied social cognitive theory to examine factors affecting benefit finding in breast cancer patients, focusing on environmental, individual, and behavioral domains. Results showed strong performance by the XGBoost classifier. The developed nomogram aids healthcare providers in swiftly identifying patients with low benefit finding, enabling personalized interventions to mitigate adverse psychological effects and improve long-term outcomes.

1 Introduction

Cancer is a major public health challenge, significantly threatening population health and serving as a critical determinant of the global disease burden (1). In 2022, data from the International Agency for Research on Cancer (IARC) indicated that there were almost 20 million new cancer diagnoses worldwide, with cancer fatalities totaling 9.7 million (2). Breast cancer, a major contributor to cancer-related deaths globally, represents a substantial risk to women’s health (3, 4). In 2020, breast cancer emerged as the leading cancer diagnosis globally in women, with around 2.26 million new cases (5). Projections indicate that the global annual incidence of new cancer cases will surpass 3 million in the near future (6). Research shows that breast cancer incidence in China is the highest worldwide, accounting for 17.6% of all cases globally (7). The diagnosis of cancer and the need for invasive treatments often result in negative outcomes for breast cancer patients, leaving them vulnerable to both psychological and physical distress (8). However, studies have demonstrated that such negative impacts can, in some cases, lead to positive life changes (9–12). This favorable change in one’s way of living allows certain individuals to recognize various personal, social, psychological, and spiritual advantages that arise after experiencing traumatic events, a concept known as benefit finding (13, 14).

Benefit finding (BF) refers to individuals’ positive coping strategies in response to traumatic events. It often involves cognitive reappraisal, which helps to elicit positive emotions and promote adaptive behaviors (15). BF encompasses various domains, such as acceptance, familial relationships, worldview, personal growth, social connections, and health (15). Studies have indicated that patients can derive benefit finding from the negative impacts they experience (16, 17). Tennen also believes that benefit finding involves recognizing potential benefits in the face of adversity. This positive transformation can foster psychological growth and promote a healthier lifestyle (18). Furthermore, relevant studies have shown that BF reflects the favorable transformations individuals may undergo post-cancer diagnosis and treatment, fostering improved psychological well-being (19).

Benefit finding is influenced by psychological, environmental, and illness-related factors. Qiu X’s research team selected 319 early-stage cancer patients undergoing treatment as the study participants and conducted a cross-sectional study (20). The research revealed a significant correlation between perceived social support and positive outcomes in benefit finding. Both Lin Y and Lassmann’s research teams have confirmed a significant correlation between anxiety, depression, and benefit finding (18, 21). Cancer staging also influences benefit finding levels (22), with higher stages of cancer associated with lower BF scores. These variables were selected not only for their predictive value, as demonstrated in prior studies, but also to capture a comprehensive understanding of the multifaceted influences on benefit finding among breast cancer patients. Appreciating these influences is essential for improving patients’ prognosis and overall outlook.

Social Cognitive Theory centers on three core components: triadic interaction theory, observational learning, and self-efficacy (23), with triadic interaction theory being the most fundamental. This theory emphasizes that environmental factors, personal factors, and behavioral responses are independent yet mutually influential. Presently, social cognitive theory is extensively utilized within oncology psychology research (24, 25). This study integrates conceptual elements of behavioral responses, individual factors, and environmental factors based on SCT. Using a conceptual framework and literature review, we perform a dynamic evaluation of benefit finding in breast cancer to accurately pinpoint its predictive factors.

BF is a complex process influenced by the interaction of multiple factors. Moreover, BF shows significant individual differences, with varying manifestations and influencing factors across different cancer patients (18, 26). Consequently, there may be potential subgroup differences among cancer patients. Existing studies often employ methods like regression analysis and mediation models, which focus on the average effects or overall associations between variables and are unable to capture the latent subgroup differences within patient populations. Latent Profile Analysis (LPA) can precisely identify these unobserved subgroup characteristics, revealing the”within-group consistency” and “between-group differences” in BF influencing factors across different populations, thus overcoming the limitations of traditional regression models in capturing the “group average effect (27, 28).” This approach offers a novel perspective for understanding individual differences in BF among breast cancer patients and for developing targeted intervention strategies.

Although LPA is highly effective in identifying latent profiles, it lacks predictive capability. Machine learning algorithms, however, can efficiently process complex data structures and learning patterns derived from large datasets, complementing LPA by providing predictive functionality (29). In this study, machine learning was employed to leverage LPA classification outcomes alongside other relevant variables to predict the likelihood of benefit finding. This dual approach, combining LPA and machine learning, delivers a nuanced understanding of benefit finding. Through meticulous patient stratification and data-driven predictive models, it enhances treatment precision, efficiency, and patient quality of life, thereby advancing the development of precision medicine.

The primary objectives of this study are: (1) to identify potential categories of breast cancer patients who may benefit from LPA, (2) to develop and validate machine learning models for predicting benefit finding, and (3) to construct simplified and effective nomograms for predicting the occurrence of benefit finding.

2 Methods

2.1 Study design and participants

This cross-sectional study, conducted between January 5, 2024, and December 10, 2024, employed a convenience sampling method to select 711 breast cancer patients from two tertiary hospitals in Bengbu City, Anhui Province. After excluding 45 cases due to loss of follow-up, a total of 666 valid cases were included for analysis. Inclusion criteria: (1) Diagnosis of breast cancer confirmed through clinical, radiological, and pathological assessments. (2) Age ≥ 18 years. (3) Written consent was secured, with individuals willingly choosing to take part in the research. (4) No history of other cancers or previous cancer treatments. Exclusion criteria: (1) Presence of other cancers or serious comorbidities. (2) Impaired consciousness, inability to cooperate, or cognitive dysfunction. (3) A history of psychiatric disorders.

The research received approval from Bengbu Medical University’s Ethics Committee (No. 2023-280), with informed consent obtained from all participants.

2.2 Predictive factors

Based on Social Cognitive Theory, this study categorizes 26 predictive factors into three domains: environmental factors, personal factors, and behavioral responses. Environmental factors include: education level, monthly income, marital status, residence area, occupation, surgery status, chemotherapy status, and social support. Personal factors include: age, BMI, C-reactive protein, hemoglobin, HDL, LDL, triglycerides, fasting blood glucose, presence of comorbidities, hearing function, language function, anxiety, and depression. Behavioral responses include: smoking status, alcohol consumption, physical exercise, Sleep duration, and medication adherence.

2.3 Sample size

Although no universal approach exists for determining sample size in surveys utilizing machine learning methods, LAP generally suggests an initial sample of 500 or more (30). Moreover, the sample size can be determined using the Events Per Variable (EPV) approach (31), which recommends having a minimum of 10 events per predictor or outcome variable to guarantee reliable and stable results. This research incorporates 26 predictive elements, necessitating at least 260 subjects. With a total of 666 participants enrolled, the study satisfied both analytical requirements, thereby bolstering the reliability and clinical relevance of the outcomes.

2.4 Measure

2.4.1 General information questionnaire

The General Information Questionnaire includes two parts: demographic details and illness-related factors. The demographic section includes age, marital status, monthly income, education level, residence area, medication adherence, and sleep duration, etc. The disease-related section includes factors such as whether the patient has undergone surgery or chemotherapy.

2.4.2 Benefit finding scale

The BFS was initially created by Antoni et al. to assess benefit finding among women with early-stage breast cancer (32). The Chinese version of the BFS was adapted and revised by Liu Zhunjun et al., consisting of six dimensions with 22 items: Acceptance, Family Relationships, Personal Growth, Worldview, Social Relationships, and Health (15). The scale boasts a Cronbach’s α of 0.95 and a validity score of 0.97, demonstrating strong reliability and accuracy. It employs a 5-point Likert scale, with responses ranging from 1 (“Not at all”) to 5 (“Very much”), capturing varying degrees of agreement or intensity. The cumulative score, derived by adding the scores across all dimensions, falls within a range of 22 to 110. Higher totals signify a greater ability to find benefits in challenging situations, whereas lower scores suggest a diminished capacity for such insight. In this particular study, the Chinese adaptation of the Benefit Finding Scale achieved a Cronbach’s α of 0.834.

2.4.3 Hospital anxiety and depression scale

The HADS stands out as a go-to instrument for assessing anxiety and depression, featuring a 14-item structure split into two distinct categories: Anxiety and Depression. Scores on this scale span from 0 to 21, with a threshold set at 8. Falling below this mark typically signals an absence of symptoms, whereas hitting 8 or higher points to the likelihood of their presence (33). The Chinese adaptation of the HADS has proven to be both reliable and valid when used with cancer patients, boasting an impressive Cronbach’s α of 0.929 for the overall scale. Additionally, the Anxiety and Depression subscales each scored a solid 0.874, further underscoring the tool’s robustness in this context (34). In this research, the HADS Cronbach’s α value was 0.802.

2.4.4 Social support rating scale

The SSRS, created by Xiao Shuiyuan in 1986 and updated in 1990, measures social support, particularly among older adults (35). The assessment tool comprises three key aspects: Objective Support, Subjective Support, and Support Utilization, featuring 10 items and a top score of 66. Scoring below 22 suggests minimal social support, while a range of 22 to 44 points to moderate support. Scores exceeding 44 signify robust social support, with higher values indicating stronger support systems. In this study, the scale demonstrated strong reliability, with a Cronbach’s α coefficient of 0.845.

2.5 Data collection methods and quality control

The research team was composed of master’s students and clinical research nurses, all of whom underwent standardized training. Upon obtaining approval from the relevant hospital departments, the researchers employed a standardized script to guide eligible breast cancer patients through the process of completing the questionnaires. The researchers thoroughly explained the study’s objectives, significance, and instructions for completing the questionnaires. Following the acquisition of informed consent from the participants, the questionnaires were distributed. Any queries raised by the participants during the completion of the forms were addressed immediately on-site. Upon completion, the questionnaires were collected without delay, and those containing incorrect responses were excluded from the analysis.

To ensure scientific rigor and efficiency, the data collection process adhered to strict operational protocols. The procedures for obtaining informed consent, providing on-site guidance, and collecting completed questionnaires were carefully controlled to maintain high-quality standards. A total of 711 questionnaires were distributed, with 666 valid responses collected, yielding an effective response rate of 93.6%. These data provide a reliable foundation for subsequent analysis.

2.6 Handling of missing data

In this study, all questionnaires underwent completeness checks after collection. Any questionnaire with missing items was deemed invalid and excluded prior to data analysis. This approach ensured that the final 666 questionnaires included in the analysis were complete and contained no missing values. This method is commonly employed in cross-sectional studies to guarantee data consistency and the validity of analyses.

2.7 Statistical analysis

Latent Profile analysis was conducted using Mplus 8.3 software to investigate the underlying characteristics of benefits identified in breast cancer patients. The analysis commenced with a unidimensional model, progressively incorporating additional categories until the model fit indices were optimized. The information criteria encompass the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the adjusted Bayesian Information Criterion (aBIC), where lower values signify a superior model fit. The entropy index spans from 0 to 1 and serves as a measure of classification accuracy, where values exceeding 0.8 suggest that accuracy surpasses 90% (36). Likelihood ratio test statistics, including the Lo-Mendell-Rubin Test (LMRT) and the Bootstrap Likelihood Ratio Test (BLRT), are employed to compare alternative latent class models, with a P-value of less than 0.05 signifying that the k-class model outperforms the k-1 class model (37). The best classification model was chosen in accordance with these integrated criteria.

This study summarized participants’ general characteristics using descriptive statistics. Continuous variables not following normal distribution were presented as median and interquartile range, while categorical variables were expressed as counts and percentages. Univariate analysis employed the Mann-Whitney U test, chi-square test, or Fisher’s exact test. Univariate analysis was performed using R 4.5.2, with a two-tailed significance threshold of P < 0.05. After determining the optimal model and classification, we assessed differences in environmental factors, personal factors, and behavioral responses among benefit finding groups categorized by different classifications. Important variables from univariate analysis (p ≤ 0.05) were further analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to retain significant predictors. Then, binary logistic regression is conducted to determine the influencing factors of benefit finding. Five machine learning algorithms—Logistic Regression, Decision Tree, XGBoost, SVM, and ANN—were employed to build and compare benefit finding prediction models. All models were implemented using Python 3.13. Additionally, model training employed bootstrap resampling. Performance was evaluated using metrics including area under the receiver operating characteristic curve (ROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Calibration was assessed by comparing predicted versus observed “low benefit finding category” incidence rates. Model discriminative capability was evaluated via ROC analysis, quantified by AUC. Calibration plots assessed consistency between predicted probabilities and actual outcomes. Decision curve analysis (DCA) evaluated clinical utility and net benefit. Feature importance was assessed using Shapley additive explanations (SHAP) analysis, where higher SHAP absolute values indicate greater influence on predictions. Additionally, we explored the distribution of feature values and their relationship with model predictions to further understand model behavior. SHAP analysis was also performed using Python 3.13.

3 Results

3.1 General data analysis of breast cancer patients

This study included 666 breast cancer patients, with demographic classification showing 343 individuals (51.50%) aged under 60. Regarding marital status, the majority (specifically 92.64%) were married, while a minority (7.36%) were unmarried, divorced, or widowed. Among participants, 281 individuals (42.19%) had a monthly income below 2,000 RMB. Additionally, 346 individuals (51.95%) resided in rural areas, 215 (32.28%) were illiterate, 440 (66.07%) underwent breast cancer surgery, and 331 (49.7%) received chemotherapy. Detailed patient information is presented in Table 1.

Table 1

Table 1. Univariate analysis of the potential categories of benefit finding in breast cancer patients [example (%)].

3.2 Latent profile analysis of benefit finding in breast cancer patients

This study is based on latent feature analysis. By comparing the fit indices of Models 1 to 4, Model 2 was ultimately selected as the optimal model. The rationale for this decision includes: information criteria (AIC, BIC, aBIC) showed a consistent downward trend; high entropy (0.970) indicated good classification accuracy; and likelihood ratio tests (LMR-LRT and BLRT) demonstrated that the two-class model significantly outperformed the one-class model. Furthermore, the two-category model demonstrated balanced category distribution and theoretical interpretability, aligning with the research objectives and facilitating in-depth analysis of subsequent influencing factors. In contrast, the three- and four-category models exhibited categories with extremely low proportions (e.g., 3%), potentially representing outliers or random variation within the sample rather than universally significant latent subgroups. The stability and reproducibility of these categories are questionable. Therefore, I selected the binary classification as the optimal model. Table 2 displays the model fit indices for models ranging from 1 to 4 categories.

Table 2

Table 2. Displays the latent profile analysis of advantages recognized in breast cancer patients.

3.3 The nomenclature of potential subtypes associated with the benefit finding in breast cancer patients

Based on the scoring characteristics of each category, this study found that the average score for Category 1 was 42.47 ± 5.74 points. The mean scores across all dimensions were the lowest among all categories. Therefore, Category 1 was designated as the low benefit finding category (35%). Category 2 had a mean score of (59.57 ± 7.09), with the highest mean scores across all dimensions among all categories. This category was named the high benefit finding category (65%). The mean scores for each dimension of the Breast Cancer Patient Benefit Assessment Scale are shown in Figure 1.

Figure 1

Figure 1. The distribution of features of potential categories associated with the benefits in breast cancer patients was identified.

3.4 Univariate analysis and LASSO regression of potential determinants of benefit finding

Univariate analysis revealed statistically significant differences (P < 0.05) between benefit finding categories across the following variables: age, medication adherence, anxiety, depression, and social support. Table 1 presents the results of the univariate analysis. LASSO regression indicated that age, medication adherence, anxiety, depression, and social support were statistically significant (see Figure 2).

Figure 2

Figure 2. Feature selection based on LASSO regression. Coefficient distribution plot (A) and penalty plot (B). The optimal value for the dashed vertical line is selected as the minimum standard deviation and one standard deviation above the minimum standard deviation.

3.5 Binary logistic regression analysis

Using the high benefit finding category (C2) as a reference, binary logistic regression analysis was employed to identify key factors influencing benefit classification. Anxiety (OR = 1.116, P < 0.001) and depression (OR = 1.064, P = 0.01) were associated with a higher likelihood of belonging to the low benefit finding category. Age < 60 years (OR = 0.148, P < 0.001), good medication adherence (OR = 0.156, P < 0.001), and social support (OR = 0.95, P = 0.01) were more likely to be associated with the high benefit finding category. The comprehensive results of the binary logistic regression are shown in Table 3.

Table 3

Table 3. Binary logistic regression examination of benefit identification in breast cancer patients.

3.6 Comparative analysis and optimization of multiple machine learning models

We evaluated five machine learning models for predicting patient benefit finding in breast cancer. The XGBoost model achieved an AUC value of 0.945 and demonstrated significantly superior sensitivity and specificity compared to the other four models, thereby being selected as the final model. Results are shown in Figure 3A. The calibration curve in Figure 3B demonstrates a close correspondence between predicted probabilities and actual outcomes. Furthermore, the decision curve analysis in Figure 3C unequivocally highlights this model’s superior predictive capability compared to others, offering substantial benefits to relevant patients.

Figure 3

Figure 3. (A) Comparison of ROC curves for five machine learning algorithms predicting low benefit finding in Bootstrap. (B) Calibration curve for predicting low benefit finding. (C) Decision curve for predicting low benefit finding.

3.7 Model interpretation

Figure 4 illustrates the impact of various features on model prediction outcomes. As shown in Figure 4A, the SHAP summary plot ranks the importance of five key features. The average absolute SHAP value histogram (Figure 4B) further corroborates this finding. These data identify which feature values significantly influence the final risk prediction score and cause its fluctuations. Additionally, this study developed a nomogram, with results presented in Figure 5.

Figure 4

Figure 4. Interpretation of XGBoost model results based on the SHAP method. SHAP summary plot (A) and bar chart (B).

Figure 5

Figure 5. Nomogram predicting the probability of low benefit finding in breast cancer patients.

4 Discussion

4.1 Latent profile analysis of benefits identified for breast cancer patients

The BFS score of 666 breast cancer patients in this study was (53.62 ± 10.52), which was significantly lower than the values reported in domestic studies (38, 39), and this might be related to multiple factors. The negative impact of mastectomy on body image and self-esteem may be amplified in Chinese culture due to the emphasis on bodily integrity, leading to more pronounced psychological distress (40). Additionally, side effects from treatments like chemotherapy may also contribute to lower BFS scores (41).

Through latent profile analysis, this study identified significant heterogeneity in benefit finding among breast cancer patients, categorizing them into two distinct categories: the low benefit finding category (35%) and the high benefit finding category (65%). The low benefit finding category scored significantly lower than the high benefit finding category on the “family relationships” dimension. This finding suggests that breast cancer patients who struggle to perceive positive meaning from their illness tend to experience higher levels of negative emotions. This, in turn, diminishes their willingness to engage with family members and reduces the frequency of emotional exchanges, ultimately leading to decreased family closeness and overall adaptive functioning (42). The high benefit finding category exhibited significantly higher BFS scores than the low benefit finding category, with particularly prominent scores in the personal growth dimension. This finding indicates that social support from family, friends, and healthcare teams not only provides emotional comfort and practical assistance to breast cancer patients, alleviating negative emotions triggered by the disease (such as anxiety and inferiority), but also reduces psychological burdens, prompting patients to re-examine their disease experiences. By uncovering positive meanings within the treatment process, interpersonal interactions, and personal growth, this support significantly enhances patients’ levels of benefit finding (43). Breast cancer patients exhibit individual differences in benefit finding. Therefore, healthcare providers need to tailor their care approaches to the specific needs of each patient group and implement targeted interventions.

4.2 Personal factors include: Age, anxiety, and depression

The results of this study indicate that breast cancer patients under the age of 60 are more likely to be classified as a high benefit finding category. Some studies have shown that younger patients are more likely to achieve positive cognitive outcomes from their disease experiences, such as personal growth and relationship enhancement (44, 45). However, other studies indicate a positive correlation between age and benefit finding, suggesting that older patients may exhibit greater psychological growth across various dimensions (e.g., in “acceptance” and “life perspective”) due to accumulated life experiences, mature coping strategies, and meaning-making processes (46). This inconsistency may arise from variations in cultural contexts and assessment tools. In Asian collectivist cultures, older patients often prioritize family harmony and role adaptation (47), resulting in distinct expressions of benefit finding compared to Western individualistic cultures that emphasize personal growth (48). From a theoretical perspective, the meaning-making process within social cognitive theory may explain age differences: younger patients, being more future-oriented, tend to view illness as a life transition point, thereby stimulating growth motivation (49); whereas older individuals may rely more heavily on existing coping resources and life narratives to integrate illness experiences into their life stories. Additionally, older patients often face multiple health issues and diminished social support, which may influence their ability to discover benefits (50, 51). For younger patients, cognitive restructuring training guided by oncology nurses or psychologists can be initiated during the initial diagnosis and adjuvant therapy phases to enhance their understanding of treatment significance. Peer support groups can also be utilized to facilitate experience sharing (52, 53). For elderly patients, maintaining their social support systems is paramount. An interdisciplinary team comprising social workers and geriatric care specialists should assist these patients in managing their disease while preserving or restoring quality of life, thereby enhancing benefit finding (54). Additionally, mindfulness-based stress reduction therapy can alleviate anxiety and fatigue during and after chemotherapy across all age groups, indirectly promoting benefit finding (55).

This study confirms that anxiety is more readily categorized under the low benefit finding category. This finding aligns strongly with current mainstream theoretical frameworks and empirical research evidence. From the perspective of cognitive adaptation theory (56), individuals facing major threats such as cancer achieve psychological adaptation through meaning reconstruction, regaining a sense of control, and self-enhancement. Benefit finding is the core manifestation of this adaptive cognitive process (56, 57). High levels of anxiety, as an emotional state that continuously depletes psychological resources, severely impede patients’ ability to initiate and sustain this positive cognitive reappraisal. It causes their cognitive focus to become fixated on the disease’s threats and uncertainties, making it difficult to uncover positive meanings such as personal growth, improved relationships, or shifts in life philosophy from their experiences (58). Moreover, under high anxiety, patients tend to focus more on the threatening aspects of illness (e.g., recurrence risk, treatment side effects) rather than potential positive changes (e.g., improved family relationships, personal growth). This leads to cognitive processing biased toward negative information, inhibiting the emergence of benefit finding (21). Therefore, this study proposes a clinical practice framework: stepwise psychosocial interventions should be systematically integrated into routine breast cancer treatment. Regarding intervention types, evidence-based approaches such as Mindfulness-Based Stress Reduction (MBSR) and Acceptance and Commitment Therapy (ACT) should be prioritized to manage anxiety and cultivate meaning finding (59). These may be supplemented with group interventions centered on benefit finding, such as narrative therapy (60). At critical stages (e.g., initial diagnosis and adjuvant therapy), stepwise psychological care should be implemented (61). The stepped care model builds upon routine care through four progressive stages: observation, guidance, face-to-face guidance, and professional intervention. Through tiered interventions including health education, WeChat group guidance, follow-up practical training, and professional psychological counseling, it effectively alleviates psychological distress and anxiety in breast cancer patients undergoing postoperative chemotherapy while significantly improving their quality of life. This approach aims to proactively promote psychological growth and long-term adaptation by specifically alleviating anxiety, ultimately enhancing the level of perceived benefits.

Depression is associated with the category of low benefit finding. According to the stress generation hypothesis (62), depressive symptoms can lead individuals to develop negative thoughts, emotions, and behaviors, which may negatively affect body image and have adverse impacts on health outcomes for cancer patients (63). This, in turn, hinders their ability to find benefit (64). Across both domestic and international contexts, studies consistently report an inverse association between depression and BF levels (18, 21, 65). Thus, the outcomes of our investigation align nicely with what has been previously documented. From a clinical perspective, these results have practical implications. First, early identification and intervention of depressive symptoms during cancer rehabilitation are crucial to prevent their progression and avoid negative effects on the psychological recovery of patients. Secondly, CBT and mindfulness-based interventions not only alleviate depressive symptoms but also enhance emotional regulation. By doing so, these methods boost psychological well-being, elevate life satisfaction, and build greater mental resilience.

4.3 Behavioral response: medication adherence

This study indicates that patients with good medication adherence tend to fall into the high benefit finding category. This finding aligns with research by Neugut et al., which shows that patients completing adjuvant chemotherapy regimens often report new perspectives on life and greater perceived treatment benefits (66). From the perspective of self-determination theory, good adherence reflects not only behavioral compliance but also likely indicates patients’ internal identification with and meaning integration of treatment (67). This intrinsic motivation helps reinforce patients’ sense of “self-efficacy,” thereby reshaping their perception of the disease and enhancing benefit finding (68). Medication adherence enhances patients’ sense of control over their illness, improves self-management capabilities, and facilitates proactive coping strategies, thereby increasing benefit finding (69). Furthermore, research indicates that positive psychological factors and social support significantly promote long-term adherence (70, 71), with such support itself serving as a crucial catalyst for benefit finding. Therefore, clinical interventions should integrate adherence management with psychosocial support. For instance, incorporating structured adherence counseling led by nurses or clinical pharmacists trained in motivational interviewing during routine oncology follow-ups can help patients connect medication adherence with overall recovery goals (72). Concurrently, family-centered positive psychological interventions effectively enhance psychological resilience, hope, perceived disease benefits, and quality of life in breast cancer patients (73).

4.4 Environmental factors: social support

Social support is associated with a high benefit finding category. The study revealed that social support enhances benefit finding both directly and indirectly by boosting patients’ hope (74). Sufficient social support can increase cancer patients’ hope (75), help them maintain an optimistic attitude toward life, and reduce the suffering caused by physical pain and unpleasant experiences, ultimately leading them to find meaning in life and enhance their BF levels (76). Additionally, other studies have demonstrated a positive link between social support and BF (20). Therefore, strengthening social support to improve BF levels is particularly important. Medical teams can provide resource navigation (such as electronic social resource lists), conduct family empowerment training (communication skills/symptom management), and strengthen both instrumental and emotional support to enhance patients’ BF.

4.5 Clinical implications

This study combines social cognitive theory, latent profile analysis, and machine learning to reveal, for the first time, significant heterogeneity in perceived benefits among breast cancer patients, identifying a “low benefit finding category” (35%) and a “high benefit finding category” (65%). It further identifies age, medication adherence, social support, anxiety, and depression as key influencing factors. Based on these findings, clinical practice should implement individualized interventions. For high-risk patients characterized by advanced age, low social support, high anxiety/depression, and poor medication adherence, healthcare teams can utilize the developed predictive model for early identification. Comprehensive measures should be implemented, including stepwise psychological interventions (e.g., CBT, mindfulness therapy), structured adherence counseling, and reinforcement of family social support. These approaches proactively promote psychological adaptation and meaning-making, ultimately enhancing overall psychological recovery and quality of life.

4.6 Study limitations

This research is not without its shortcomings. For starters, the cross-sectional approach falls short of tracking the evolving advantages for breast cancer patients as time progresses. Additionally, the fact that all subjects were drawn exclusively from Anhui Province raises questions about how well these results can be extrapolated to different areas or demographic groups. Additionally, the questionnaires in this study were voluntarily completed by breast cancer patients. Despite excluding non-compliant responses and addressing missing data during quality control, there remains a potential for response bias. Therefore, future studies could improve upon this research by increasing the sample size, enhancing regional diversity, and employing longitudinal and qualitative methods to further explore the factors influencing breast cancer patients’ perceptions of benefits.

5 Conclusion

This study represents the first attempt to integrate the social cognitive theory framework with latent profile analysis (LPA) and modern machine learning methods to develop a predictive model for breast cancer patient benefit finding. Through LPA, we identified two distinct benefit finding subgroups exhibiting significant heterogeneity, revealing different psychological adaptation patterns among patients. The XGBoost model demonstrated exceptional predictive capability (AUC = 0.945), while SHAP interpretability analysis highlighted five key influencing factors: age, medication adherence, social support, anxiety, and depression. This underscores the complex interplay of environmental, personal, and behavioral factors affecting benefit finding. The nomogram we developed provides healthcare professionals with a practical, rapid method to identify patients with low benefit finding for timely psychological interventions such as cognitive behavioral therapy, mindfulness training, and enhanced social support. This will facilitate personalized care strategies to mitigate adverse mental health outcomes and improve patients’ long-term quality of life.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the Ethics Committee of Bengbu Medical University (No. 2023-280). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

WW: Formal analysis, Writing – original draft, Methodology, Supervision, Writing – review & editing. KG: Writing – original draft, Methodology, Investigation. WD: Writing – original draft, Data curation. LC: Formal analysis, Methodology, Writing – original draft. HG: Writing – original draft, Formal analysis, Methodology, Supervision. ZZ: Conceptualization, Formal analysis, Writing – original draft. JZ: Writing – review & editing, Supervision.

Funding

The author(s) declared that financial support was received for this work and/or its publication. National innovative training program for college students, Grant/Award Number:(202410367082); National innovative training program for college students, Grant/Award Number: (202410367010); The Anhui Province Fund for High-level Teaching Teams(2018jxtd105).

Acknowledgments

The investigators are grateful to the dedicated participants and all the research staff of the study.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Qi J, Li M, Wang L, Hu Y, Liu W, Long Z, et al. National and subnational trends in cancer burden in China, 2005-20: an analysis of national mortality surveillance data. Lancet Public Health. (2023) 8:e943–55. doi: 10.1016/S2468-2667(23)00211-6

PubMed Abstract | Crossref Full Text | Google Scholar

2. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. (2024) 74:229–63. doi: 10.3322/caac.21834

PubMed Abstract | Crossref Full Text | Google Scholar

3. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. (2021) 71:209–49. doi: 10.3322/caac.21660

PubMed Abstract | Crossref Full Text | Google Scholar

4. Xu H and Xu B. Breast cancer: epidemiology, risk factors and screening. Chin J Cancer Res. (2023) 35:565–83. doi: 10.21147/j.issn.1000-9604.2023.06.02

PubMed Abstract | Crossref Full Text | Google Scholar

5. Arnold M, Morgan E, Rumgay H, Mafra A, Singh D, Laversanne M, et al. Current and future burden of breast cancer: global statistics for 2020 and 2040. Breast. (2022) 66:15–23. doi: 10.1016/j.breast.2022.08.010

PubMed Abstract | Crossref Full Text | Google Scholar

6. Cao W, Chen H, Yu Y, Li N, and Chen W. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J (Engl). (2021) 134:783–91. doi: 10.1097/CM9.0000000000001474

PubMed Abstract | Crossref Full Text | Google Scholar

7. Stein KD, Syrjala KL, and Andrykowski MA. Physical and psychological long-term and late effects of cancer. Cancer. (2008) 112:2577–92. doi: 10.1002/cncr.23448

PubMed Abstract | Crossref Full Text | Google Scholar

8. Bower JE, Meyerowitz BE, Desmond KA, Bernaards CA, Rowland JH, and Ganz PA. Perceptions of positive meaning and vulnerability following breast cancer: predictors and outcomes among long-term breast cancer survivors. Ann Behav Med. (2005) 29:236–45. doi: 10.1207/s15324796abm2903_10

PubMed Abstract | Crossref Full Text | Google Scholar

9. Mols F, Vingerhoets AJJM, Coebergh JWW, and van de Poll-Franse LV. Well-being, posttraumatic growth and benefit finding in long-term breast cancer survivors. Psychol Health. (2009) 24:583–95. doi: 10.1080/08870440701671362

PubMed Abstract | Crossref Full Text | Google Scholar

10. Petrie KJ, Buick DL, Weinman J, and Booth RJ. Positive effects of illness reported by myocardial infarction and breast cancer patients. J Psychosom Res. (1999) 47:537–43. doi: 10.1016/s0022-3999(99)00054-9

PubMed Abstract | Crossref Full Text | Google Scholar

11. Sears SR, Stanton AL, and Danoff-Burg S. The yellow brick road and the emerald city: benefit finding, positive reappraisal coping and posttraumatic growth in women with early-stage breast cancer. Health Psychol. (2003) 22:487–97. doi: 10.1037/0278-6133.22.5.487

PubMed Abstract | Crossref Full Text | Google Scholar

12. Kinsinger DP, Penedo FJ, Antoni MH, Dahn JR, Lechner S, and Schneiderman N. Psychosocial and sociodemographic correlates of benefit-finding in men treated for localized prostate cancer. Psychooncology. (2006) 15:954–61. doi: 10.1002/pon.1028

PubMed Abstract | Crossref Full Text | Google Scholar

13. Davis CG, Nolen-Hoeksema S, and Larson J. Making sense of loss and benefiting from the experience: two construals of meaning. J Pers Soc Psychol. (1998) 75:561–74. doi: 10.1037//0022-3514.75.2.561

PubMed Abstract | Crossref Full Text | Google Scholar

14. Kaufhold J, Soltani S, Birnie KA, and Noel M. The role of benefit finding in the relationship between pain and posttraumatic stress symptoms in youth with chronic pain: a longitudinal analysis. Clin J Pain. (2023) 39:367–76. doi: 10.1097/AJP.0000000000001127

PubMed Abstract | Crossref Full Text | Google Scholar

15. Liu Z, Zhang L, and Gudenkauf L. Cross-cultural adaptation of the benefit finding scale (BFS) in Chinese cancer patients. Chin J Nurs. (2015) 50:6. doi: 10.3761/j.issn.0254-1769.2015.05.010

Crossref Full Text | Google Scholar

16. Conley CC and Andersen BL. Lemons to lemonade: effects of a biobehavioral intervention for cancer patients on later life changes. Health Psychol. (2019) 38:206–16. doi: 10.1037/hea0000717

PubMed Abstract | Crossref Full Text | Google Scholar

17. Hoogland A, Jim HSL, Schoenberg NE, Watkins JF, and Rowles GD. Positive psychological change following a cancer diagnosis in old age a mixed-methods study. Cancer Nurs. (2021) 44:190–96. doi: 10.1097/NCC.0000000000000766

PubMed Abstract | Crossref Full Text | Google Scholar

18. Lassmann I, Dinkel A, Marten-Mittag B, Jahnen M, Schulwitz H, Gschwend JE, et al. Benefit finding in long-term prostate cancer survivors. Support Care Cancer. (2021) 29:4451–60. doi: 10.1007/s00520-020-05971-3

PubMed Abstract | Crossref Full Text | Google Scholar

19. Casellas-Grau A, Ochoa C, and Ruini C. Psychological and clinical correlates of posttraumatic growth in cancer: a systematic and critical review. Psychooncology. (2017) 26:2007–18. doi: 10.1002/pon.4426

PubMed Abstract | Crossref Full Text | Google Scholar

20. Qiu X, Zhang K, Zhang Y, and Sun L. Benefit finding and related factors of patients with early-stage cancer in China. Int J Environ Res Public Health. (2022) 19:4284. doi: 10.3390/ijerph19074284

PubMed Abstract | Crossref Full Text | Google Scholar

21. Lin Y, Luo X, Li J, Xu Y, and Li Q. The dyadic relationship of benefit finding and its impact on quality of life in colorectal cancer survivor and spousal caregiver couples. Support Care Cancer. (2021) 29:1477–86. doi: 10.1007/s00520-020-05602-x

PubMed Abstract | Crossref Full Text | Google Scholar

22. Lv Q and Liu M. Study on the level of benefit finding and its influencing factors in female patients with breast cancer during chemotherapy. Modern Prev Med. (2017) 44:2537–40. Available online at: https://kns.cnki.net/kcms2/article/abstract?v=CB7PaufGGSfSInyOMSumCafKBiuJBNajil_-bBKZx9yZIxPYtmqG4BYcKmwv7DRFkGFVgD3JiXJXoUe5BEAHl4R2hbWMYm1zptosnizb93_mfk2XFnTuMObvhbRjruw6Gwp4bWKzZckxvat7t3rKKPgqG70eLUUZneFtHrdy23cTA2fMCs1Mzg==&uniplatform=NZKPT&language=CHS.

Google Scholar

23. Bandura A. Social cognitive theory: an agentic perspective. Annu Rev Psychol. (2001) 52:1–26. doi: 10.1146/annurev.psych.52.1.1

PubMed Abstract | Crossref Full Text | Google Scholar

24. Jones TL, Edbrooke L, Rawstorn JC, Hayes SC, Maddison R, Denehy L, et al. Self-efficacy, motivation, and habits: psychological correlates of exercise among women with breast cancer. Support Care Cancer. (2023) 31:584. doi: 10.1007/s00520-023-08040-7

PubMed Abstract | Crossref Full Text | Google Scholar

25. Auster-Gussman LA, Gavin KL, Siddique J, Welch WA, Solk P, Whitaker M, et al. Social cognitive variables and physical activity during chemotherapy for breast cancer: an intensive longitudinal examination. Psychooncology. (2022) 31:425–35. doi: 10.1002/pon.5820

PubMed Abstract | Crossref Full Text | Google Scholar

26. Liu Z, Thong MSY, Doege D, Koch-Gallenkamp L, Weisser L, Bertram H, et al. Benefit finding, posttraumatic growth and health-related quality of life in long-term cancer survivors: a prospective population-based study. Acta Oncol. (2023) 62:1124–31. doi: 10.1080/0284186X.2023.2245560

PubMed Abstract | Crossref Full Text | Google Scholar

27. Wang M, Fang J, Hu X, Cai T, Wu F, and Lin Y. Chemotherapy-related symptoms in children with leukemia: application of latent profile analysis and network analysis. Support Care Cancer. (2024) 32:207. doi: 10.1007/s00520-024-08410-9

PubMed Abstract | Crossref Full Text | Google Scholar

28. Jing F, Zhu Z, Qiu J, Tang L, Xu L, and Xing W. Symptom profiles and related factors among breast cancer patients undergoing endocrine therapy: a latent profile analysis. Cancer Nurs. (2023) 46:E297–304. doi: 10.1097/NCC.0000000000001125

PubMed Abstract | Crossref Full Text | Google Scholar

29. Huettmann F, Craig EH, Herrick KA, Baltensperger AP, Humphries GR, Lieske DJ, et al. Use of machine learning (ML) for predicting and analyzing ecological and ‘presence only’data: an overview of applications and a good outlook. Mach Learn Ecol Sustain Natural Resource Manage. (2018) 24:27–61. doi: 10.1007/978-3-319-96978-7_2

Crossref Full Text | Google Scholar

30. Nylund-Gibson K, Garber AC, Carter DB, Chan M, Arch DAN, Simon O, et al. Ten frequently asked questions about latent transition analysis. Psychol Methods. (2023) 28:284–300. doi: 10.1037/met0000486

PubMed Abstract | Crossref Full Text | Google Scholar

31. Riley RD, Ensor J, Snell KIE, Harrell FEJ, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. Bmj. (2020) 368:m441. doi: 10.1136/bmj.m441

PubMed Abstract | Crossref Full Text | Google Scholar

32. Antoni MH, Lechner S, Diaz A, Vargas S, Holley H, Phillips K, et al. Cognitive behavioral stress management effects on psychosocial and physiological adaptation in women undergoing treatment for breast cancer. Brain Behav Immun. (2009) 23:580–91. doi: 10.1016/j.bbi.2008.09.005

PubMed Abstract | Crossref Full Text | Google Scholar

33. Beekers N, Husson O, Mols F, van Eenbergen M, and van de Poll-Franse LV. Symptoms of anxiety and depression are associated with satisfaction with information provision and internet use among 3080 cancer survivors: results of the PROFILES registry. Cancer Nurs. (2015) 38:335–42. doi: 10.1097/NCC.0000000000000184

PubMed Abstract | Crossref Full Text | Google Scholar

34. Li Q, Lin Y, Hu C, Xu Y, Zhou H, Yang L, et al. The chinese version of hospital anxiety and depression scale: psychometric properties in chinese cancer patients and their family caregivers. Eur J Oncol Nurs. (2016) 25:16–23. doi: 10.1016/j.ejon.2016.09.004

PubMed Abstract | Crossref Full Text | Google Scholar

35. Shuiyuan X. The theoretical basis and research applications of the “social support rating scale. J Clin Psychiatry. (1994) 02):98–100. Available online at: https://kns.cnki.net/kcms2/article/abstract?v=CB7PaufGGSfHqnvnhZYBTjYgbJnuElUXRngYXJoYBhig2rJ6f488rJDpia1P5xi_LF6AEkYBBQ6rNsdyMc_Bza7L594dT_hj6fb9-m6t5mSaXu6K9UsXR53wojkZHbwCnBT3Ys0xMPT50lUM2crYAyUHc3kz0yxBRD4MBMT623PRjkAOsIMsiw==&uniplatform=NZKPT&language=CHS.

Google Scholar

36. Wang M, Qiaowen D, Xiangyang BI, Ye H, and Yang W. Performance of the entropy as an index of classification accuracy in latent profile analysis: a Monte Carlo simulation study. Acta Psychologica Sin. (2017) 49:1473–82. doi: 10.3724/SP.J.1041.2017.01473

Crossref Full Text | Google Scholar

37. Jie-Ting Z, Can J, and Min-Qiang Z. Application of latent class analysis in psychological research. Adv psychol Sci. (2010) 18:1991–98. Available online at: https://journal.psych.ac.cn/adps/EN/Y2010/V18/I12/1991.

Google Scholar

38. Sun M. Latent profile analysis and influencing factors of benefit finding in early stage of postoperative colorectal cancer patients undergoing chemotherapy. Chin Nurs Res. (2024) 38:3052–58. JHYH. doi: 10.12102/j.issn.1009-6493.2024.17.007

Crossref Full Text | Google Scholar

39. Li Z, Liu Z, and Zhang L. Benefit finding and influencing factors in patients with early-stage colorectal cancer. Chin Gen Pract. (2020) 23:4459–64. doi: 10.12114/j.issn.1007-9572.2020.00.597

Crossref Full Text | Google Scholar

40. Shahbazi S, Heidari M, and Ghodusi M. Evaluation of body esteem and mental health in patients with breast cancer after mastectomy. J Midlife Health. (2015) 6:173. doi: 10.4103/0976-7800.172345

PubMed Abstract | Crossref Full Text | Google Scholar

41. Choi EK, Kim I, Chang O, Kang D, Nam S, Lee JE, et al. Impact of chemotherapy-induced alopecia distress on body image, psychosocial well-being, and depression in breast cancer patients. Psychooncology. (2014) 23:1103–10. doi: 10.1002/pon.3531

PubMed Abstract | Crossref Full Text | Google Scholar

42. Ding Z, Fan Y, Li E, Ai F, and Cui H. Latent profile analysis of family adaptation in breast cancer patients-cross-sectional study. Sci Rep. (2024) 14:21357. doi: 10.1038/s41598-024-72410-2

PubMed Abstract | Crossref Full Text | Google Scholar

43. Liu R, Jiang L, Wang F, Tan X, and Sun L. Benefit finding: the mediator linking social support to self - management efficacy in gynecological cancer. Cancer Manag Res. (2025) 17:1935–45. doi: 10.2147/CMAR.S540258

PubMed Abstract | Crossref Full Text | Google Scholar

44. Hurria A and Muss H. Special issues in older women with breast cancer. Adv Exp Med Biol. (2015) 862:23–37. doi: 10.1007/978-3-319-16366-6_3

PubMed Abstract | Crossref Full Text | Google Scholar

45. Cordova MJ, Giese-Davis J, Golant M, Kronenwetter C, Chang V, and Spiegel D. Breast cancer as trauma: posttraumatic stress and posttraumatic growth. J Clin Psychol Med Settings. (2007) 14:308–19. doi: 10.1007/s10880-007-9083-6

Crossref Full Text | Google Scholar

46. Wang Y, Zhu X, Yang Y, Yi J, Tang L, He J, et al. What factors are predictive of benefit finding in women treated for non-metastatic breast cancer? A prospective study. Psychooncology. (2015) 24:533–39. doi: 10.1002/pon.3685

PubMed Abstract | Crossref Full Text | Google Scholar

47. Zhu T, Liu D, van der Heide A, Korfage IJ, and Rietjens JA. Preferences and attitudes towards life-sustaining treatments of older Chinese patients and their family caregivers. Clin Interv Aging. (2023) 18:467–75. doi: 10.2147/cia.s395128

PubMed Abstract | Crossref Full Text | Google Scholar

48. Chen AX and Fung H. Individualism increases the influence of perceived competence of older adults on attitudes toward them. Innov Aging. (2020) 4:325. doi: 10.1093/geroni/igaa057.1043

Crossref Full Text | Google Scholar

49. Mohd Sabri NS and Mohamed Hussin NA. Making life meaningful after cancer: a qualitative study of meaning in life among young adults with cancer in Malaysia. Illness Crisis Loss. (2024) 33:454–73. doi: 10.1177/10541373241260617

Crossref Full Text | Google Scholar

50. Van der Vlegel-Brouwer W. Which factors enable older adults with multimorbidity to adapt and self-manage to increase health? A narrative literature review. Int J Integr Care. (2016) 16:203. doi: 10.5334/ijic.2751

Crossref Full Text | Google Scholar

51. Zhang Y, Wei X, Zhang N, Zhang Y, Li N, Zhang B, et al. Frailty and social isolation in breast cancer patients: the moderated mediating role of self-perception of aging and menopausal symptoms. J Adv Nurs. (2025) 81:8655–65. doi: 10.1111/jan.16833

PubMed Abstract | Crossref Full Text | Google Scholar

52. Legu ET, Gayatri D, and Waluyo A. Mindfulness-based cognitive therapy terhadap penurunan kecemasan pada pasien kanker. J Telenursing (JOTING). (2024) 6:1634–42. doi: 10.31539/joting.v6i1.10362

Crossref Full Text | Google Scholar

53. Hu J, Wang X, Guo S, Chen F, Wu Y, Ji F, et al. Peer support interventions for breast cancer patients: a systematic review. Breast Cancer Res Treat. (2019) 174:325–41. doi: 10.1007/s10549-018-5033-2

PubMed Abstract | Crossref Full Text | Google Scholar

54. Garman KS, McConnell ES, and Cohen HJ. Inpatient care for elderly cancer patients: the role for geriatric evaluation and management units in fulfilling goals for care. Crit Rev Oncol Hematol. (2004) 51:241–47. doi: 10.1016/j.critrevonc.2004.03.002

PubMed Abstract | Crossref Full Text | Google Scholar

55. Zhang Q, Zhao H, and Zheng Y. Effectiveness of mindfulness-based stress reduction (MBSR) on symptom variables and health-related quality of life in breast cancer patients-a systematic review and meta-analysis. Support Care Cancer. (2019) 27:771–81. doi: 10.1007/s00520-018-4570-x

PubMed Abstract | Crossref Full Text | Google Scholar

56. Taylor SE. Adjustment to threatening events: a theory of cognitive adaptation. Am Psychol. (1983) 38:1161. doi: 10.1037/0003-066X.38.11.1161

Crossref Full Text | Google Scholar

57. Tedeschi RG and Calhoun LG. Posttraumatic growth: conceptual foundations and empirical evidence. Psychol Inq. (2004) 15:1–18. doi: 10.1207/s15327965pli1501_01

Crossref Full Text | Google Scholar

58. Zhu P, Chen C, Liu X, Gu W, and Shang X. Factors associated with benefit finding and mental health of patients with cancer: a systematic review. Support Care Cancer. (2022) 30:6483–96. doi: 10.1007/s00520-022-07032-3

PubMed Abstract | Crossref Full Text | Google Scholar

59. Chen L, Ren T, Tan Y, and Li H. Global trends of research on depression in breast cancer: a bibliometric study based on VOSviewer. Front Psychol. (2022) 13:969679. doi: 10.3389/fpsyg.2022.969679

PubMed Abstract | Crossref Full Text | Google Scholar

60. Yuan P, Lu W, Yong X, Yang W, Zhong X, Wang M, et al. Evidence summary of meaning in life intervention for cancer patients. Front Oncol. (2025) 15:1603349. doi: 10.3389/fonc.2025.1603349

PubMed Abstract | Crossref Full Text | Google Scholar

61. Tu C, He Y, and Ma X. Factors influencing psychological distress and effects of stepwise psychological care on quality of life in patients undergoing chemotherapy after breast cancer surgery. Am J Transl Res. (2022) 14:1923–33. Available online at: https://pmc.ncbi.nlm.nih.gov/articles/PMC8991156/.

PubMed Abstract | Google Scholar

62. Hammen C. Stress generation in depression: reflections on origins, research, and future directions. J Clin Psychol. (2006) 62:1065–82. doi: 10.1002/jclp.20293

PubMed Abstract | Crossref Full Text | Google Scholar

63. Yu X, Huang Q, Yang Y, Wang L, Wu F, Ding Y, et al. Impact of social support on body image during chemotherapy in patients with breast cancer: the chain mediating role of depression and self-efficacy. Asia Pac J Oncol Nurs. (2025) 12:100664. doi: 10.1016/j.apjon.2025.100664

PubMed Abstract | Crossref Full Text | Google Scholar

64. Zhu L, Ranchor AV, Helgeson VS, van der Lee M, Garssen B, Stewart RE, et al. Benefit finding trajectories in cancer patients receiving psychological care: predictors and relations to depressive and anxiety symptoms. Br J Health Psychol. (2018) 23:238–52. doi: 10.1111/bjhp.12283

PubMed Abstract | Crossref Full Text | Google Scholar

65. Jansen L, Hoffmeister M, Chang-Claude J, Brenner H, and Arndt V. Benefit finding and post-traumatic growth in long-term colorectal cancer survivors: prevalence, determinants, and associations with quality of life. Br J Cancer. (2011) 105:1158–65. doi: 10.1038/bjc.2011.335

PubMed Abstract | Crossref Full Text | Google Scholar

66. Neugut AI, Hillyer GC, Kushi LH, Lamerato L, Buono DL, Nathanson SD, et al. A prospective cohort study of early discontinuation of adjuvant chemotherapy in women with breast cancer: the breast cancer quality of care study (BQUAL). Breast Cancer Res Treat. (2016) 158:127–38. doi: 10.1007/s10549-016-3855-3

PubMed Abstract | Crossref Full Text | Google Scholar

67. Vansteenkiste M, Soenens B, and Vandereycken W. Motivation to change in eating disorder patients: a conceptual clarification on the basis of self-determination theory. Int J Eat Disord. (2005) 37:207–19. doi: 10.1002/eat.20099

PubMed Abstract | Crossref Full Text | Google Scholar

68. Grimaldi A. How to help the patient motivate himself? Diabetes Metab. (2012) 38 Suppl 3:S59–64. doi: 10.1016/S1262-3636(12)71536-7

PubMed Abstract | Crossref Full Text | Google Scholar

69. Rosenberg SM, Petrie KJ, Stanton AL, Ngo L, Finnerty E, and Partridge AH. Interventions to enhance adherence to oral antineoplastic agents: a scoping review. J Natl Cancer Inst. (2020) 112:443–65. doi: 10.1093/jnci/djz244

PubMed Abstract | Crossref Full Text | Google Scholar

70. Torres E, Richman A, Koutlas JB, DeAntonio PA, Liles DK, Romero-Trejo K, et al. Medication adherence to oral chemotherapeutic agents: a qualitative study of patients’ and cancer care professionals’ perspectives. J Psychosocial Oncol Res Pract. (2023) 5:114. doi: 10.1097/or9.0000000000000114

Crossref Full Text | Google Scholar

71. Lin C, Clark R, Tu P, Bosworth HB, and Zullig LL. Breast cancer oral anti-cancer medication adherence: a systematic review of psychosocial motivators and barriers. Breast Cancer Res Treat. (2017) 165:247–60. doi: 10.1007/s10549-017-4317-2

PubMed Abstract | Crossref Full Text | Google Scholar

72. Cohen SD, Bouvier F, Tissot C, Viallet A, Leiterer C, Tacco F, et al. Structured pharmaceutical interviews enhance knowledge and medication adherence in DOAC therapy: insights from the EDUC-AOD study. Patient Educ Couns. (2025) 143:109423. doi: 10.1016/j.pec.2025.109423

PubMed Abstract | Crossref Full Text | Google Scholar

73. Zhang Y, Tang R, Bi L, Wang D, Li X, Gu F, et al. Effects of family-centered positive psychological intervention on psychological health and quality of life in patients with breast cancer and their caregivers. Support Care Cancer. (2023) 31:592. doi: 10.1007/s00520-023-08053-2

PubMed Abstract | Crossref Full Text | Google Scholar

74. Zhao M, Jiang Y, Xu G, and Lin X. The mediating effect of hope level between social support and benefit finding in patients with advanced lung cancer. Cancer Nurs. (2025) 48:213–20. doi: 10.1097/NCC.0000000000001295

PubMed Abstract | Crossref Full Text | Google Scholar

75. Applebaum AJ, Stein EM, Lord-Bessen J, Pessin H, Rosenfeld B, and Breitbart W. Optimism, social support, and mental health outcomes in patients with advanced cancer. Psychooncology. (2014) 23:299–306. doi: 10.1002/pon.3418

PubMed Abstract | Crossref Full Text | Google Scholar

76. Li Y, Zhou Z, Ni N, Li J, Luan Z, and Peng X. Quality of life and hope of women in China receiving chemotherapy for breast cancer. Clin Nurs Res. (2022) 31:1042–49. doi: 10.1177/10547738211046737

PubMed Abstract | Crossref Full Text | Google Scholar