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European Journal of Psychotraumatology logoLink to European Journal of Psychotraumatology
. 2026 Apr 8;17(1):2650093. doi: 10.1080/20008066.2026.2650093

How does self-compassion affect posttraumatic adaptation among lymphoma patients? A perspective based on network analysis

¿Cómo influye la autocompasión en la adaptación postraumática en pacientes con linfoma? Una perspectiva basada en el análisis de redes

Wei Li a,†, Dongdong Xue b,c,d,†,CONTACT, Xiaoqin Niu d, Qian Chen d, Xiaozhuang Wang d,✉
PMCID: PMC13063321  PMID: 41949341

ABSTRACT

Background: Self-compassion, defined as the ability to treat oneself with kindness during suffering, serves as a protective factor for posttraumatic adaptation in cancer patients. However, the functional distinctions between different components of self-compassion in shaping posttraumatic adaptation among lymphoma patients remain unclear.

Objective: This study employed network analysis to examine the relationships between self-compassion components and posttraumatic adaptation in lymphoma patients.

Methods: Patients diagnosed with lymphoma (n = 233) completed a cross-sectional questionnaire survey. Considering that posttraumatic adaptation is a multidimensional construct, this study operationalised it through two key concepts: posttraumatic stress (representing negative adaptation) and posttraumatic growth (representing positive adaptation). Gaussian graphical model (GGM) and directed acyclic graph (DAG) were used to construct network structures linking self-compassion components to posttraumatic stress and posttraumatic growth.

Results: Positive self-compassion (i.e. self-kindness, common humanity, and mindfulness) was positively associated with posttraumatic growth across its domains but showed no significant links to posttraumatic stress symptom clusters. Conversely, negative self-compassion (i.e. self-judgment, isolation, and over-identification) was positively associated with posttraumatic stress symptom clusters, with no significant connections to posttraumatic growth domains. DAG provided further support for the identification of the undirected network and indicated differential roles of mindfulness and over-identification in relation to posttraumatic growth and posttraumatic stress symptoms.

Conclusions: The findings suggest that distinct components of self-compassion may play distinct roles in both positive and negative posttraumatic adaptation in lymphoma patients. However, these results are preliminary and hypothesis-generating. Longitudinal and experimental studies are needed to examine whether targeting specific positive or negative components results in different changes in posttraumatic stress and growth.

KEYWORDS: Lymphoma, self-compassion, posttraumatic stress, posttraumatic growth, network analysis

HIGHLIGHTS

  • Positive self-compassion is strongly associated with post-traumatic growth, while negative self-compassion is closely linked to posttraumatic stress symptoms.

  • Mindfulness may serve as a key component in promoting posttraumatic growth, whereas over-identification may act as an important component exacerbating posttraumatic stress symptoms.

  • Distinguishing functional differences in self-compassion components may enhance understanding of posttraumatic adaptation in lymphoma patients.

1. Introduction

Being diagnosed and treated for lymphoma may be a traumatic experience associated with intense distress (Camille et al., 2020; Tan et al., 2023). Patients with lymphoma not only face the risk of disease recurrence but also endure treatment-related stress, which may contribute to the development of posttraumatic stress (PTS) symptoms. Studies show that lymphoma patients experience significant psychological distress during treatment. This includes persistent distressing intrusions, impaired cognitive functioning (Janelsins et al., 2022), and negative emotional reactions like depression and anxiety (Mojs et al., 2017). However, many cancer patients report positive psychological changes during their long-term struggle with the disease, a phenomenon known as post-traumatic growth (Sumalla et al., 2009).

Posttraumatic growth (PTG) refers to the positive psychological changes experienced as a result of the struggle with highly challenging life events (Tedeschi & Calhoun, 2004). PTG often manifests in improvements in self-perception, interpersonal relationships, and life philosophy (Heidarzadeh et al., 2018). Posttraumatic stress and posttraumatic growth tend to coexist in cancer populations (Marziliano et al., 2020), highlighting the complexity of psychological adaptation in cancer patients. Consequently, integrated psychological intervention programmes are needed to alleviate distress and promote growth, helping cancer patients achieve a constructive balance between stress and growth (Arnedo et al., 2019; Capaldi et al., 2023).

Empirical research has increasingly highlighted self-compassion as a protective factor against distress (Wei et al., 2023). Self-compassion is defined as a self-caring and compassionate attitude in the face of suffering (Neff, 2023). Individuals with high self-compassion are more likely to actively reappraise negative thoughts and regulate emotions in distressing situations, thereby reducing post-traumatic stress and facilitating post-traumatic growth (Wong & Yeung, 2017). In clinical settings, promoting self-compassion and reducing self-criticism may help cancer patients lessen the negative effects of trauma (Brooker et al., 2020) and support their psychological recovery and growth (Campo et al., 2017).

It is worth noting that self-compassion is often viewed as a positive construct, but it is in fact multidimensional, encompassing both positive and negative components (Ferrari et al., 2022). Positive self-compassion is the ability to treat oneself with kindness during suffering (self-kindness), recognise negative events as part of the human experience (common humanity), and approach the present moment with balance (mindfulness) (Liu, Xu, et al., 2023). In contrast, negative self-compassion involves harsh self-criticism during painful events (self-judgment), feeling isolated and believing one is the sole sufferer (isolation), and exaggerating pain as permanent and unmanageable (over-identification) (Liu, Xu, et al., 2023). However, positive and negative self-compassion are not two ends of the same continuum (Strickland et al., 2021) and serve different functions in psychological regulation. Empirical studies show that positive self-compassion is associated with positive psychological outcomes, whereas negative self-compassion is associated with negative outcomes (López et al., 2015). This suggests that positive and negative self-compassion play distinct roles in protecting against or posing risks to individual mental health (Muris & Petrocchi, 2017).

Several key questions need to be addressed: Is a high level of positive self-compassion enough to alleviate the posttraumatic stress symptoms of cancer patients? In which clinical context should priority be given to reducing the negative self-compassion? Furthermore, depending on treatment goals, is it necessary to implement targeted interventions for specific components of self-compassion? Previous studies have not reached consensus on these questions, underscoring the need for further research on the distinctions and connections among self-compassion components to inform more effective interventions.

The network approach offers a new perspective for understanding the internal structure and functional differences of self-compassion (Contreras et al., 2019). By constructing a network model that links self-compassion components to psychological adaptation, it helps identify distinct associations between these components and psychological adaptation. This in-depth analysis can also guide the development of targeted evidence-based interventions by identifying key areas of clinical intervention.

Despite the proven protective effects of self-compassion in cancer patients (Wei et al., 2023), research on lymphoma patients remains limited. Lymphoma patients differ from other cancer populations in key clinical characteristics, including varied treatment options, a broad age range, and high uncertainty regarding illness progression (Mojs et al., 2017). These factors may create more complex psychological challenges for lymphoma patients, highlighting the need for a deeper exploration of the unique impact of self-compassion on posttraumatic adaptation in this group.

This study aimed to explore the relationship between self-compassion components and posttraumatic adaptation in lymphoma patients through network analysis. Posttraumatic stress and posttraumatic growth were selected as key indicators of posttraumatic adaptation due to their strong theoretical basis (Jim & Jacobsen, 2008) and extensive empirical support (Kelada et al., 2025; Pat-Horenczyk et al., 2016) in cancer trauma research. Together, these constructs provide a coherent framework for capturing both maladaptive and adaptive responses and have been widely discussed in empirical and intervention research (Baník et al., 2022; Capaldi et al., 2023). Specifically, we first explore the specific correlation between the six dimensions of self-compassion and posttraumatic stress and growth through the undirected network (i.e. Gaussian graphical model). Then, we use a directed network (i.e. a directed acyclic graph) to examine potential causal relationships among these components.

2. Methods

2.1. Participants

This study used convenience sampling to recruit lymphoma patients from a hospital in Tianjin, China. As a pilot survey, it is not part of a larger dataset, and the aim is to provide initial insights to inform future medical management and intervention strategies. Inclusion criteria: (1) lymphoma diagnosis confirmed by pathology; (2) age 18 years or older; (3) conscious and able to communicate and read normally. Exclusion criteria: (1) Patients with severe comorbid conditions (e.g. heart failure, advanced stage of another cancer) that would significantly interfere with survey participation or data interpretation; (2) refusal to participate or interruption of the survey for personal reasons; (3) inability to communicate or understand the survey (e.g. due to cognitive impairment).

2.2. Procedures

The study was approved by the local university ethics committee (approval number: 2024050902). Before the survey, all investigators underwent uniform training. The survey was administered using paper questionnaires in hospital wards. During the survey, participants were provided with standardised instructions about the study's purpose and asked to complete the questionnaire after giving their consent. Completed questionnaires were collected immediately and checked for missing or incorrect responses to ensure data integrity. Upon completing the survey, participants received rewards, including an infusion alarm device, towels, and other gifts, as a token of appreciation for their participation.

2.3. Measures

2.3.1. Self-compassion

We used the Self-Compassion Scale (Neff, 2003) to assess self-compassion in lymphoma patients. This scale comprises 6 dimensions and 26 items: self-kindness, self-judgment, common humanity, isolation, mindfulness, and over-identification. Each item is rated on a 5-point scale from 1 (almost never) to 5 (almost always), with higher scores representing higher levels of self-compassion. The Chinese version of the self-compassion scale has been tested and widely used (Chen et al., 2011). In this study, the three subscales of positive self-compassion (i.e. self-kindness, common humanity, and mindfulness) had good reliability (α = 0.84, 0.80, 0.82), and the three subscales of negative self-compassion (i.e. self-judgment, isolation, and over-identification) had good reliability (α = 0.78, 0.83, 0.72).

2.3.2. Posttraumatic stress

We used the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5; Blevins et al., 2015) to assess the level of posttraumatic stress symptoms in lymphoma patients. This checklist is divided into 4 dimensions: intrusive thoughts, avoidance, negative alterations in cognition and mood, arousal and reactivity. The 20 items are rated on a 5-point scale from 0 (not at all) to 4 (extremely), with higher scores representing higher levels of posttraumatic stress symptoms. A total score of PCL-5 greater than 33 represents the threshold for PTSD diagnosis (Bovin et al., 2016). The Chinese version of the PCL-5 has been tested (Wang et al., 2015). In this study, the four subscales of PCL-5 had good reliability: Intrusive thoughts (α = 0.89), Avoidance (α = 0.89), Negative alterations in cognition and mood (α = 0.85), Arousal and reactivity (α = 0.82).

2.3.3. Posttraumatic growth

The Posttraumatic Growth Scale (PTGI; Tedeschi & Calhoun, 1996) was used in this study to assess posttraumatic growth in lymphoma patients. The PTGI is organised into 5 dimensions: relating to others, personal strength, appreciation of life, new possibilities, and spiritual change. All 21 items are rated on a 6-point scale from 0 (I did not experience this change) to 5 (to a very great degree), with higher scores representing higher levels of posttraumatic growth. The Chinese version of PTGI has been tested and widely used (Wang et al., 2011). In this study, the five subscales of PTGI had good reliability: Relating to others (α = 0.78), Personal strength (α = 0.85), Appreciation of life (α = 0.89), New possibilities (α = 0.81), and Spiritual change (α = 0.79).

2.4. Data analyses

In this study, network analysis was conducted in R (Version 4.3.2). First, the R package ‘bootnet’ (Epskamp et al., 2018) was used to construct a Gaussian Graphical Model (GGM). We used the Extended Bayesian Information Criterion (EBIC) (Epskamp et al., 2012) and the Least Absolute Shrinkage and Selection Operator (LASSO) (Tibshirani, 2011) to build the network model. This method can regularise the edge weight of the network to reduce the number of false edges and the instability of the estimation as much as possible. Meanwhile, we set the tuning parameter to 0.5 to obtain a more realistic and stable sparse network.

In the Gaussian graph network, there are 15 nodes representing the six dimensions of self-compassion, four symptom clusters of post-traumatic stress disorder, and five domains of post-traumatic growth. Edges indicated regularised partial correlations between nodes, and the thickness of edges indicated the degree of association between nodes. Blue edges indicated positive correlation and red edges indicated negative correlation. To test the accuracy of the network edge weights, we used a nonparametric bootstrap test (n = 5000) to calculate 95% confidence intervals (CIs) for the edge weights (Epskamp et al., 2018). Wider confidence intervals indicate greater instability in the edge weight estimate. In addition, the differences between the edge weights in the network were tested (α = 0.05).

In this study, the R package ‘bnlearn’ (Scutari, 2010) was used to construct a directed acyclic graph (DAG). DAG is a directed network in which edges between nodes in the network are unidirectional, indicating the direction of probabilistic dependency (i.e. no feedback loops are allowed). DAG allows for the exploration of possible causal relationships between variables based on cross-sectional data. To ensure the stability of the generated DAG, we then bootstrapped 10,000 samples, computed the network for each sample, and averaged over the generated network to generate the final network structure (Heeren et al., 2020). At the same time, the optimal cut-point method is used to retain edges to improve the specificity and sensitivity of the network (Scutari & Nagarajan, 2013). In a DAG, the node at the top of the network has a greater driving force than the other nodes. The thickness of the edge corresponds to the size of the Bayesian information criterion (BIC), with thicker edges representing its greater importance to the network structure (McNally et al., 2017).

3. Results

3.1. Sample characteristics

The study included 233 participants, aged 18–81 years (mean age = 51.73 ± 13.95 years), with a mean time since diagnosis of 11.03 months. The sample consisted of 102 males (43.78%) and 131 females (56.22%). In terms of disease type, 52 (22.32%) had Hodgkin lymphoma, while 181 (77.68%) had non-Hodgkin lymphoma. Most participants (182, 78.11%) faced lymphoma recurrence, while 51 (21.89%) were initially diagnosed with lymphoma. More detailed sample characteristics are shown in Table 1.

Table 1.

Demographic information of participants.

Demographic characteristics Group Number
(% of Total)
Gender Male 102 (43.78%)
Female 131 (56.22%)
Ethnicity Han Chinese 214 (91.85%)
Other 19 (8.15%)
Residence Urban 141 (60.52%)
Rural 92 (39.48%)
Marital status Married 197 (84.55%)
Unmarried 22 (9.44%)
Other 14 (6.01%)
Education level High school or below 121 (51.93%)
Associate degree 56 (24.03%)
Bachelor's degree 41 (17.60%)
Master's degree or higher 15 (6.44%)
Monthly income (RMB) Less than 3000 61 (26.18%)
3000–5000 94 (40.34%)
More than 5000 78 (33.48%)
Primary caregiver Spouse 143 (61.37%)
Children 41 (17.60%)
Siblings 11 (4.72%)
Parents 25 (10.73%)
Other 13 (5.58%)
Disease type Hodgkin Lymphoma 52 (22.32%)
Non-Hodgkin Lymphoma 181 (77.68%)
Diagnosis type Recurrence 182 (78.11%)
Initial diagnosis 51 (21.89%)
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3.2. Descriptive statistics

The range of scores on the PCL-5 for the participants was 0–53 (M = 10.20, SD = 10.42). Seven participants exhibited scores above 33, which suggested the potential for a PTSD diagnosis. Posttraumatic growth scores ranged from 0 to 100 (M = 45.85, SD = 22.36). In addition, the total score of positive self-compassion ranges from 13 to 65 (M = 41.69, SD = 10.03), while the total score of negative self-compassion ranges from 13 to 54 (M = 29.70, SD = 9.76).

3.3. Gaussian graphical model

The Gaussian graphical model is shown in Figure 1. The network includes 15 nodes and 42 non-zero edges (42/105), with a mean edge weight of 0.063.

Figure 1.

A network diagram with 15 labeled circles and 42 connecting lines, grouped into self compassion, posttraumatic growth, and stress clusters. The figure shows a network diagram representing a Gaussian graphical model of 15 labeled nodes linked by 42 edges. Circular nodes are arranged in four loose clusters linked by straight lines of varying thickness, indicating stronger or weaker connections. One cluster at the top contains nodes PSC1, PSC2, and PSC3. A second cluster to the left contains nodes G1, G2, G3, G4, and G5. A third cluster to the right contains nodes D1, D2, D3, and D4. A fourth cluster near the bottom contains nodes NSC1, NSC2, and NSC3. Some edges within each cluster are thicker than edges between clusters. On the right side, a legend lists four categories with bullet points. Under the heading Negative self compassion, the items are NSC1 colon Self judgment, NSC2 colon Isolation, and NSC3 colon Over identification. Under Positive self compassion, the items are PSC1 colon Self kindness, PSC2 colon Common humanity, and PSC3 colon Mindfulness. Under Posttraumatic growth, the items are G1 colon Spiritual change, G2 colon Relating to others, G3 colon Personal strength, G4 colon New possibilities, and G5 colon Appreciation of life. Under Posttraumatic stress, the items are D1 colon Intrusive thoughts, D2 colon Avoidance, D3 colon Negative alterations in cognition and mood, and D4 colon Arousal and reactivity. All data are approximate.

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Gaussian graphical model.

Note: Each node represents the average score for a given psychometric scale. Supplementary Table S1 provides the edge-weight matrix of the network. Blue edges indicate positive connections, and red edges indicate negative connections. Dashed lines represent non-significant edges.

Overall, the positive components of self-compassion were positively correlated with the five domains of posttraumatic growth (r = 0.015–0.079). In terms of the correlation between positive psychological indicators, the three strongest edges are ‘Mindfulness – Personal strength’ (r = 0.079), ‘Self-kindness – Relating to others’ (r = 0.066), and ‘Self-kindness – Personal strength’ (r = 0.064). However, no significant associations were observed between positive self-compassion and the four PTSD symptom clusters, as the edge weights were close to 0. In contrast, the negative components of self-compassion were positively correlated with the four symptom clusters of PTSD (r = 0.047–0.094). The three strongest edges are ‘Isolation – Intrusive thoughts’ (r = 0.094), ‘Over-identification – Arousal and reactivity’ (r = 0.087), and ‘Isolation – Avoidance’ (r = 0.057). There is almost no significant correlation between negative self-compassion components and the five domains of posttraumatic growth.

Bootstrap 95% CIs for edge weights were relatively wide and should be interpreted with caution (Supplementary Figure S1). However, CIs for most edges linking positive self-compassion to posttraumatic growth and negative self-compassion to PTSD symptom clusters did not include 0. The bootstrap difference test indicated that the six strongest edges identified above differed significantly from the remaining edges (Supplementary Figure S2).

3.4. Directed acyclic graph

The directed acyclic graph is shown in Figure 2. Overall, Mindfulness (PSC3) and over-identification (NSC3) are located at the top of the network and drive other nodes.

Figure 2.

A network diagram with labeled ovals, including PSC3 and NSC3 at the top, connected by one way arrows to D, G, NSC, and PSC nodes. The figure shows a directed network diagram laid out horizontally. Each variable is drawn as an oval containing a short label. At the top center is an oval labeled NSC3. From NSC3, several one way arrows extend downward and diagonally toward other ovals, including NSC2, G5, and NSC1. Near the upper right is an oval labeled PSC3. One way arrows link PSC3 downward and looping back toward an oval labeled PSC1 and onward to an oval labeled PSC2. Toward the middle of the diagram is an oval labeled G5, from which multiple one way arrows point to ovals labeled G3, G2, and G4, as well as back toward the lower left. On the left side are ovals labeled D3, D2, D1, and D4, connected by several one way arrows that generally move from upper left toward lower left and then toward the center, ending at ovals labeled G1 and G5. Additional ovals labeled NSC1 and NSC2 appear near the center and right, each sending and receiving one way arrows from surrounding D, G, and PSC nodes. The arrows vary in thickness, with some thicker arrows emphasizing particular links, but all arrows point in a single direction without any closed loops.

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Directed acyclic graph.

Note: The thickness of the edge corresponds to the size of the Bayesian information criterion (BIC), with thicker edges representing its greater importance to the network structure. The BIC for each edge is shown in Supplementary Table S2. NSC1: Self-judgment, NSC2: Isolation, NSC3: Over-identification, PSC1: Self-kindness, PSC2: Common humanity, PSC3: Mindfulness. G1: Spiritual change, G2: Relating to others, G3: Personal strength, G4: New possibilities, G5: Appreciation of life. D1: Intrusive thoughts, D2: Avoidance, D3: Negative alterations in cognition and mood, D4: Arousal and reactivity.

The positive self-compassion component is more strongly linked to posttraumatic growth. Specifically, common humanity (PSC2), self-kindness (PSC1), and mindfulness (PSC3) are related to new possibilities (G4), either directly or indirectly. In contrast, no direct association was observed between positive self-compassion components and the PTSD symptom clusters. These findings suggest that positive self-compassion may be associated with psychological growth.

On the other hand, the negative self-compassion component is more closely associated with PTSD. Over-identification (NSC3) and isolation (NSC2) are related to intrusive thoughts (D1), avoidance (D2), and arousal and reactivity (D4), either directly or indirectly. No direct link was found between negative self-compassion components and posttraumatic growth domains. Overall, negative self-compassion (e.g. over-identification and isolation) appears to be associated with increased PTSD symptoms.

4. Discussion

This study is the first to use network analysis to examine the associations between distinct components of self-compassion and posttraumatic adaptation among patients with lymphoma. Positive self-compassion components were more closely linked to posttraumatic growth, whereas negative components showed stronger associations with posttraumatic stress symptoms. The structural patterns of the directed acyclic graph align with those of the undirected network, further supporting differential associations of self-compassion components with posttraumatic stress and growth.

4.1. Differential associations of positive self-compassion with posttraumatic stress and posttraumatic growth

We found that positive self-compassion is positively correlated with the domains of posttraumatic growth, but not significantly correlated with PTSD symptom clusters. This may suggest that positive self-compassion mainly promotes the development of adaptive mental outcomes rather than relieves pathological symptoms (López et al., 2015). Individuals with high levels of positive self-compassion are more likely to recognise suffering as part of the common human experience, to express their feelings openly, and to actively seek material and emotional support from others when necessary (Liu et al., 2021). This positive ability has been shown to lead to better recovery and growth of the individual's psychological functioning (Tedeschi & Calhoun, 2004; Wong & Yeung, 2017).

Notably, the present study found no significant association between positive self-compassion and posttraumatic stress symptoms. Previous studies have shown that positive self-compassion has a weak negative association with posttraumatic stress (Li et al., 2025; Liu, Xu, et al., 2023). This discrepancy may stem from the effect of network regularisation (i.e. automatically excluding network edges with weak correlations). Additionally, only a small number of participants in our sample met the PTSD diagnostic threshold, and the overall level of post-traumatic stress was relatively low. This restricted range of symptoms may have limited the detection of edges involving protective factors. It could partly explain the lack of direct associations between positive self-compassion components and PTSD clusters. A recent meta-analysis study also found that the baseline or changes in self-compassion scores did not affect posttraumatic stress outcomes post-intervention (Luo et al., 2021). Together, these findings suggest that current conceptual assessments and interventions for self-compassion may need refinement. It is important to distinguish between the specific functions of different self-compassion dimensions, as this differentiation can offer valuable guidance for clinical practice.

4.2. Differential associations of negative self-compassion with posttraumatic stress and posttraumatic growth

Our study also found that negative self-compassion was positively associated with post-traumatic stress symptoms, but not with the domains of post-traumatic growth. This suggests that negative self-compassion may exert its influence on adjustment primarily by exacerbating psychological distress (López et al., 2015). Excessive criticism of the self tends to exacerbate the psychological distress of cancer patients, leading them to become stuck in negative thought patterns from which they cannot disengage. This, in turn, contributes to the development of more posttraumatic stress symptoms (Liu, Wang, et al., 2023).

Furthermore, we found no association between negative self-compassion and posttraumatic growth, which aligns with the conclusions drawn in prior studies (Liu, Wang, et al., 2023; Liu, Xu, et al., 2023). Negative self-compassion may not act independently on posttraumatic growth, but rather requires other mediators (e.g. cognitive processes) to act indirectly (Liu, Wang, et al., 2023; Wong & Yeung, 2017). Accordingly, future research is needed to further elucidate the specific mechanisms underlying how negative self-compassion relates to psychological growth.

4.3. Directional associations between components of self-compassion and posttraumatic stress and posttraumatic growth

Interestingly, the findings from the directed acyclic graph align closely with those from the undirected network and identify some potential driving variables. Positive self-compassion components are strongly related to posttraumatic growth in lymphoma patients, particularly in the ‘new possibilities’ domain. This suggests that adopting an attitude of understanding and kindness toward oneself may help patients cope positively with stressful events (Krok et al., 2025), leading them to find new directions in life. Mindfulness, positioned at the top of the directed network, appears to play a key role in driving other positive self-compassion components and growth experiences. This finding underscores the importance of implementing the mindful self-compassion programme to promote compassion and well-being (Neff & Germer, 2013).

Conversely, over-identification and isolation are closely linked to various posttraumatic stress symptoms. According to the directed network, over-identification may intensify the re-experiencing and arousal responses to cancer-related trauma. Isolation, on the other hand, may trigger avoidance reactions, making it harder for individuals to benefit from social support and ultimately exacerbating pain. These findings are consistent with previous studies that the negative component of self-compassion is the most important factor in symptom changes in PTSD treatment (Hoffart et al., 2015). However, given the study's cross-sectional design, causal inferences should be interpreted with caution. These findings are preliminary and exploratory, and should be considered as a basis for generating hypotheses in future research.

On the whole, our findings are also consistent with the dual-processing perspective of self-compassion (Falconer et al., 2015; Longe et al., 2010; Wang et al., 2022), which posits that self-compassion and self-criticism play distinct roles in an individual's psychological recovery. As posited by Waite et al. (2015), self-criticism maintains distressing experiences of psychosis, while self-acceptance and compassion promote recovery and growth. Early studies also found that compared with positive self-compassion, negative self-compassion showed a stronger correlation with posttraumatic stress symptoms (Seligowski et al., 2015). These findings imply that negative self-compassion is closely linked to maladaptive psychological outcomes, while positive self-compassion is associated with more favourable psychological outcomes (Muris & Petrocchi, 2017).

4.4. Theoretical and practical implications

Our findings support the view that self-compassion consists of two relatively independent dimensions, positive and negative, which show different patterns of association with psychological outcomes (Muris et al., 2024). In this cross-sectional network analysis, positive and negative self-compassion relate differently to adaptive and maladaptive responses to trauma. These findings imply that increasing positive self-compassion may not necessarily coincide with a simultaneous decrease in self-criticism, and vice versa. Thus, distinguishing the functional roles of positive and negative components at an analytical level may help refine models of how self-related processes affect psychological adaptation.

From a clinical perspective, these findings are preliminary and hypothesis-generating, suggesting two potential intervention targets: enhancing positive components (e.g. mindfulness) linked to posttraumatic growth, and reducing negative components (e.g. over-identification) associated with posttraumatic stress. It should be acknowledged that developing single-targeted interventions that exclusively enhance positive self-compassion or reduce negative self-compassion may be challenging. However, the focus of the intervention can be adjusted based on the patient’s specific needs. For example, cancer patients with prominent PTSD symptoms may benefit more from interventions that primarily target self-criticism and over-identification, whereas those who are relatively stable but seeking psychological growth may benefit more from approaches that focus on strengthening positive self-compassion. Given the interrelated nature of these components, interventions such as compassion-focused therapy may influence both dimensions simultaneously rather than targeting one alone (Vidal & Soldevilla, 2023). Our results should therefore not be interpreted as implying that positive and negative components can or should be addressed completely separately in clinical practice. Instead, they highlight potentially distinct mechanisms and proximal targets that may underlie changes in posttraumatic stress and growth.

However, as this study is cross-sectional, causal conclusions cannot be drawn. Future research, particularly longitudinal and experimental studies, is needed to examine whether focusing on positive versus negative self-compassion components yields differential effects on posttraumatic growth and stress. Such studies may clarify whether interventions targeting specific components produce meaningful changes in long-term outcomes, providing valuable insights into the effectiveness of tailored interventions.

4.5. Limitations and future directions

Several limitations of this study should be considered when interpreting the results. First, participants were recruited from a single hospital, and the sample size was relatively small, which may have limited the accuracy and stability of the network analyses. In addition, the low prevalence of clinically significant PTSD and the use of convenience sampling may further restrict generalizability. As a result, the findings primarily reflect posttraumatic adaptation in a low-to-moderate distress lymphoma sample and may not generalise to patients experiencing higher levels of posttraumatic stress. Future studies could employ multicenter, cross-regional recruitment to obtain larger, more diverse samples, thereby allowing testing of whether the observed network patterns hold across broader clinical populations. Finally, the study employed a cross-sectional design, so causal relationships cannot be inferred. Although directed acyclic graphs can help explore potential causal pathways, any causal interpretations must be made with caution. Future longitudinal or intervention studies are needed to clarify how specific self-compassion components relate to posttraumatic adaptation over time, offering more robust guidance for compassion-based interventions.

5. Conclusions

This study is the first to examine the relationships between different components of self-compassion and posttraumatic adaptation in patients with lymphoma using network analysis. The findings suggest that distinguishing between the positive and negative aspects of self-compassion may be important for understanding posttraumatic stress and growth in cancer patients. Considering the cross-sectional design of the study, more research is needed to verify the functional differences of self-compassion components.

Authors contributions

WL and DX conceived the idea, performed the statistical analyses, and wrote the original manuscript. XN, QC and XW prepared the manuscript for revision. All authors approved the final version for submission and agree to be accountable for all aspects of the work.

Supplementary Material

Supplementary.docx
ZEPT_A_2650093_SM1967.docx (267KB, docx)

Acknowledgements

The authors would like to thank all the nurses who assisted with data collection.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval

The ethics committee of Tianjin Normal University approved this study (No. 2024050902).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Data availability statement

The data are available from the corresponding author upon reasonable request.

Supplemental Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/20008066.2026.2650093.

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Supplementary Materials

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Data Availability Statement

The data are available from the corresponding author upon reasonable request.

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