Traumatic Loss After the Sewol Ferry Disaster: Latent Profiles of Grief and Posttraumatic Stress Disorder and Their Links to Social Stress, Growth, and Quality of Life
Article information
Abstract
Objective
This study aimed to identify different symptom profiles of complicated grief/bereavement-related posttraumatic stress disorder (PTSD) and examine the associations with social life factors, posttraumatic growth, and quality of life in a sample of parents whose children died in Sewol ferry accident.
Methods
A total of 272 bereaved parents affected by the Sewol ferry accident participated and completed self-report scales about traumatic loss-related symptoms. The latent profile analysis (LPA) of complicated grief and posttraumatic symptoms was classified. To examine the predictors (interpersonal stress/familial conflict/social support) and outcomes (posttraumatic growth/quality of life) of the traumatic loss symptom profiles, an automatic three-step approach was chosen.
Results
The LPA identified three symptom profiles of complicated grief and posttraumatic stress: low symptomatology group (30.4%), moderate symptomatology group (49.6%), and high symptomatology group (20.0%). Higher perceived interpersonal stress significantly increased the odds of moderate and high symptomatology, while higher family stress was a significant predictor for high symptomatology compared to both low and moderate symptomatology groups. In addition, higher perceived social support significantly decreased the odds of being in both moderate and high symptomatology groups compared to the low group. The low symptomatology group showed the highest quality of life, followed by the moderate and high groups. Posttraumatic growth was also significantly different between the classes, with the moderate symptomatology group reporting higher growth than the low symptomatology group.
Conclusion
Our findings suggest that managing the mental health of people who have experienced a traumatic loss will be a critical component of their quality of life in the future. In addition, interventions to help reduce family conflict and interpersonal stress may be necessary to reduce difficulties associated with psychopathology.
INTRODUCTION
Traumatic loss is defined as the loss of a loved one in the context of potentially traumatizing circumstances [1]. Examples are losses due to homicide, suicide, accidents, natural disasters, or war. Though many people recover from traumatic loss, some may develop psychopathology, including (but not limited to) posttraumatic stress disorder (PTSD) and complicated grief [2]. In addition, mental health problems caused by traumatic loss can affect various aspects of life, such as quality of life, interpersonal relationships, and posttraumatic growth (PTG). Therefore, it is important to examine which psychopathological factors caused by traumatic loss are related to quality of life, social life, recovery, and growth.
Because the impact of traumatic loss may not be covered by a single psychiatric diagnosis but instead by heterogeneous symptoms, the effects of loss need to be researched not solely by measuring psychopathology. According to the World Health Organization, expanding epidemiological research on mental health by including measurements on disability and quality of life offers more accurate insight into the burden of distress caused by traumatic loss [3]. Although disability and quality of life are largely predicted by psychopathology, they represent crucial markers of mental health. In transcultural settings, where culture-specific idioms of distress seem more closely related to disability than in Western diagnostic classifications, these markers may reflect whether the Diagnostic and Statistical Manual of Mental Disorders (DSM) sufficiently addresses distress caused by traumatic loss.
Several previous studies have reported that quality of life is impaired in association with various psychopathologies after traumatic loss. According to a study of Iraqi asylum seekers, effects of traumatic and multiple losses on quality of life and disability were either partially or fully mediated by psychopathology [4]. In addition, according to a study of women who experienced miscarriage, the quality of life in the psychological domain was reported to decrease, suggesting that factors related to psychopathology may affect the quality of life [5]. Exploring psychopathology related to quality of life will provide valuable information on how to evaluate and manage individuals with traumatic loss.
In aspects of social life, social support and interpersonal distress are crucial factors related to health outcome after traumatic loss. Research has also explored social support in grief as salutary, as a mediator for proactive coping and as a means to mitigate both the intensity and duration of psychological distress and poor physiological outcomes [6]. Some studies show that both the quantity and quality of social support may influence well-being for grievers [7]. Bereaved people who have more frequent contact with family and friends tend to report better quality of life, whether this support comes through technology (email and the internet) or in-person [8]. Conversely, social isolation, lack of social support, and interpersonal conflict may be related to the persistence and exacerbation of psychiatric problems after traumatic loss. Some previous studies have found a relationship between loneliness and post-bereavement depressive symptoms, adding to the global burden of mental illness [9]. Social isolation and lack of support may be factors in mental health problems after traumatic loss, and conversely, mental health problems may be a factor in interpersonal conflict or social isolation. Therefore, it is important to identify which psychopathologies are related to these characteristics of social life.
Along with quality of life and social life, PTG is a significant factor in life after traumatic loss. The relationships between grief symptoms and PTG after bereavement are currently unclear. Some studies have reported a negative association between these two outcomes, whereas more recent studies supported a positive correlation [10-12]. Some previous research has also reported an independent relationship between these two variables [13,14].
As mentioned above, traumatic loss can manifest as a combination of complicated grief and posttraumatic stress symptoms. Therefore, reaction to traumatic loss may present in various patterns. In this respect, it will be meaningful to characterize symptom profiles of posttraumatic loss and posttraumatic stress symptoms.
In the current study, we used latent profile analysis (LPA) to identify symptom profiles of complicated grief and bereavement-related PTSD in a sample of parents who lost children in the Sewol ferry accident. LPA differentiates subgroups based on characteristic patterns (profiles). Based on prior research about traumatic loss, we considered two plausible outcomes [15-17]. The first was that subgroups of bereaved parents would emerge with high complicated grief and low bereavement-related PTSD symptoms (or vice versa). This would indicate that bereaved parents can be distinguished by their emotional responses to loss. A second possible outcome was that LPA would reveal different subgroups characterized by increasing probabilities (e.g., low, moderate, high probability) of both complicated grief and bereavement-related PTSD.
In addition to the LPA analysis, we will explore how different profiles of complicated grief and bereavement-related PTSD are related to social life (social support, familial distress, and interpersonal distress), PTG, and quality of life. Prior evidence has shown that deficits in social support and interpersonal and familial distress are expected to be associated with higher levels of psychopathology, namely severe complicated grief and bereavement-related PTSD symptoms. As such symptoms are associated with different indices of social malfunction, it is anticipated that the more highly disturbed subgroups (with more pervasive problems) would score high on indices of deficit of PTG and quality of life [18,19].
METHODS
Participants
This study utilized a cross-sectional design, with data collected via an online survey conducted from October 5, 2021, to December 13, 2021. The participants were bereaved family members of the victims of the Sewol ferry disaster. A total of 272 participants voluntarily agreed to participate in the study and completed a series of self-report questionnaires. Prior to completing the survey, participants were provided with detailed information about the study. Upon completion of the survey, participants were given a gift certificate as a token of appreciation. The study was approved by the Kangwon National University Institutional Review Board (IRB No. KWNUIRB-2021-08-013-003).
Measures
Latent class variables
PTSD Checklist for DSM-5
The PTSD Checklist for DSM-5 (PCL-5) was originally developed by Weathers et al. [20] The Korean version of the PTSD Checklist for the DSM-5 was used in the present study. [21] This tool is a 20-item self-report measure that assesses the presence and severity of PTSD symptoms as outlined in the DSM-5. The present study asked participants about the effects of stress related to the Sewol ferry disaster the previous month on a 5-point Likert scale ranging from 0 (“Not at all”) to 4 (“Extremely”). The measure includes subscales for intrusion (5 items), avoidance (2 items), negative alterations in cognition and mood (7 items), and alterations in arousal and reactivity (6 items). Higher scores indicate more severe PTSD symptoms. A cut-off score of 37 is typically used to identify significant PTSD symptoms. The Cronbach’s alpha was 0.963 for the present study.
Inventory of Complicated Grief
The Inventory of Complicated Grief (ICG) was originally developed by Prigerson et al. [22] and validated by Kim and Song23 to assess factors of pathological grief such as anger, disbelief, and hallucinations. This questionnaire is a 19-item selfreport measure that assesses symptoms of bereavement. While the ICG captures core symptoms conceptually related to prolonged grief disorder (PGD) as defined in the International Classification of Diseases 11th Revision and DSM-5-TR, this study did not apply diagnostic criteria for PGD. Grief symptoms were analyzed dimensionally based on ICG total scores. The present study used the tool to assess participants regarding their grief over the loss of a family member due to the Sewol ferry disaster on a 5-point Likert scale ranging from 0 (“Not at all”) to 4 (“Always”). Higher scores indicate more severe grief symptoms. A cut-off score of 25 is typically used to identify significant complicated grief [22,24]. The Cronbach’s alpha was 0.958 for the present study.
Predictor variables
Functional Social Support Questionnaire
The Functional Social Support Questionnaire (FSSQ) was originally developed by Broadhead et al. [25] This measurement is a 14-item self-report measure that assesses the level of social support received from family and friends. The present study asked participants to rate their social support from others on a 5-point Likert scale ranging from 1 (“Much less than I want”) to 5 (“As much as I want”). Higher scores indicate higher levels of perceived social support. The Cronbach’s alpha was 0.957 for the present study.
Perceived Family & Interpersonal Stress
This measurement was developed by the authors of the present study to assess the level of stress within the family and in interpersonal relationships since the Sewol ferry disaster. Each domain was assessed using a single-item measure. The present study asked participants to rate their family stress after the Sewol ferry disaster on a 6-point Likert scale ranging from 0 (“None”) to 5 (“Extreme”). Higher scores indicate higher levels of family stress. As these items consist of only one item each, internal consistency could not compute.
Outcome variables
Korean Post-traumatic Growth Inventory-10
The Post-traumatic Growth Inventory (PTGI) was originally developed by Tedesch and Calhoun [26]. This measurement is a shortened 10-item version of the original 21-item PTGI. The present study asked participants to rate the degree of change about PTG as a result of a crisis event on a 6-point Likert scale ranging from 0 (“I did not experience this change”) to 5 (“I experienced this change to a very great degree”). Higher scores indicate greater PTG. The Korean version has been validated and shown to have good psychometric properties in traumaexposed adults [27]. The Cronbach’s alpha was 0.912 for the present study.
Korean Mental Health Continuum-Short Form
The Mental Health Continuum-Short Form (MHC-SF) was originally developed by Lamers et al. [28] and Keyes [29] and has been adapted and validated for Korean populations [30]. This tool is a 14-item self-report measure that assesses emotional, social, and psychological well-being. The present study asked participants to rate their recent quality of their life on a 6-point Likert scale ranging from 0 (“Never”) to 5 (“Every day”). Higher scores indicate better well-being. The responses categorize participants into flourishing, moderate, or languishing. The Cronbach’s alpha was 0.942 for the present study.
Data analysis
Latent class analysis (LCA) was performed using Mplus 8.0 (Muthén & Muthén). To identify the optimal number of latent classes, several information indices were employed, including Akaike Information Criterion (AIC) [31], Bayesian in formation criterion (BIC) [32], and sample-size adjusted BIC [33]. To perform the likelihood ratio tests, the Lo-Mendell-Rubin likelihood ratio (LMR LR) [34] and the bootstrapping likelihood test (BLRT) were utilized. To assess the accuracy of class assignment, we utilized relative entropy-based normalization [35]. In interpreting the model fit, lower values of AIC, BIC, and sample-size adjusted BIC (saBIC) indicate better fit of a model. Entropy values of 0.80 or higher were interpreted to represent clear classification. For the LMR LR and BLRT tests, statistical significance (e.g., p-values <0.05) is preferred. Participants were assigned to classes based on their highest posterior probability. To examine the predictors and outcomes of the bereavement profiles, an automatic three-step approach was conducted [36]. Specifically, the R3STEP program was used to investigate the influence of predictors on the bereaved profile membership [36]. The Bolck, Croon, and Hagenaars three-step approach was employed to compare outcome variables across the bereaved profile memberships, using chi-square tests to determine significant differences in means across profiles.
RESULTS
Participant characteristics
The characteristics of the survey respondents are detailed in Table 1. Specifically, 20.2% were in their 20s, 6.6% in their 30s, 14.0% in their 40s, and 59.2% were 50 years or older. The sex distribution was 39.7% male and 60.3% female. Of the participants, 76.8% resided in Ansan city, with the remaining 23.2% living in other regions. Regarding household income, 18.8% had no income, 9.9% earned less than 1,000,000 KRW, 24.3% earned between 1,000,000 and 2,000,000 KRW, 20.2% earned between 2,000,000 and 3,000,000 KRW, 13.2% earned between 3,000,000 and 4,000,000 KRW, and 13.6% earned more than 4,000,000 KRW. Education levels were as follows: 56.6% had graduated from high school, 40.4% had a university degree, and 2.9% did not respond.
The characteristics of the survey respondents
Number of latent classes
Indicators in the LCA were intrusion, avoidance, cognition and mood, arousal and reactivity, and complicated grief. The fit indices of the latent classes are presented in Table 2. All values for AIC, BIC, and saBIC in the three-class model were lower than those in the two-class model. Although all values for AIC, BIC, and saBIC in the four-class model were lower than in the three-class model, the LMR LR p-value was nonsignificant. Finally, the three-class model was chosen as the optimal model.
Fit indices of latent class models
Figure 1 presents the results of the LCA. The line graph in Figure 1 compares the profiles across the identified latent classes using a Z-score format, highlighting the differences in key study variables between the classes. The first latent class (LC1) comprises 30.4% of total participants. Those in this class displayed low levels of PTSD symptoms across all subscales, including intrusion, avoidance, negative alterations in cognition and mood, and arousal and reactivity. Similarly, this group showed the lowest levels of complicated grief. The Z-scores for these symptoms were significantly below the mean, indicating that this group experienced fewer PTSD symptoms compared to the other classes. This latent class represents individuals who have relatively low levels of both PTSD symptoms and complicated grief. These participants are likely coping better with their traumatic experience and are less likely to exhibit severe psychological distress and were named the “Low Symptomatology Group.”
Profiles of latent classes on study variables. This graph displays standardized means of posttraumatic stress disorder symptoms clusters (intrusion, avoidance, cognition and mood, arousal and reactivity) and complicated grief across latent classes. LC1=low symptom group (30.4%), LC2=moderate symptom group (49.6%), LC3=high symptom group (20.0%).
The second latent class (LC2) included 49.6% of the participants who displayed moderate PTSD symptoms relative to the sample mean in this study, with Z-scores around or slightly above the mean. The scores of complicated grief were also moderate, higher than those in Class 1 but not as high as those in Class 3. This group is referred to as the “Moderate Symptomatology Group.” The third latent class (LC3) comprised 20.0% of the participants and exhibited the highest levels of PTSD symptoms across all subscales, with Z-scores significantly above the mean. This class represents individuals who are experiencing the most severe PTSD symptoms and complicated grief, are likely struggling with significant psychological distress, and may require the most intensive therapeutic interventions. This group was named the “High Symptomatology Group.” Table 3 shows statistical measures of each variable.
Raw and standardized mean scores for PTSD symptom clusters and complicated grief across latent classes
Predictors of latent classes membership
Table 4 shows the results of the three-step analysis using perceived social support, family stress, and interpersonal stress as predictors of latent class membership. The analysis revealed significant differences in stress and social support levels across the three latent classes.
Three-step results using perceived social support, family stress, and interpersonal stress as predictors of latent class membership (R3STEP)
Compared to LC1 (low symptom class), higher interpersonal stress significantly increased the odds of being in LC2 (odds ratio [OR]=1.82, p<0.01) and LC3 (OR=4.75, p<0.001). This means that individuals with greater interpersonal stress were 1.82 times more likely to belong to LC2 and 4.75 times more likely to belong to LC3, relative to LC1. Additionally, individuals with higher interpersonal stress were 2.61 times more likely to belong to LC 3, compared to those in LC2 (p< 0.001), indicating a clear gradient of interpersonal stress levels corresponding to symptom severity.
Family stress also emerged as a significant predictor. A oneunit in family stress was associated with being 2.06 times more likely to be in LC3 versus LC1, and 1.76 times more likely to be in LC3 versus LC2 (p<0.01). This suggests that family-related stress meaningfully differentiates individuals in the highest symptom class from those in the other groups. Conversely, social support showed a protective effect. Individuals reporting higher social support were 0.66 times as likely (e.g., 34% less likely) to be in LC2 and 0.29 times as likely (i.e., 71% less likely) to be in LC3, compared to LC1 (p<0.05 and p<0.001, respectively). That is, a one-unit increase in perceived social support reduced the odds of being LC2 and LC3, compared to LC1, by 34% and 71%, respectively. Furthermore, they were 0.44 times as likely (i.e., 56% less likely) to be in LC3 relative to LC2 (p<0.01). These findings highlight the buffering role of social support in protecting against severe trauma-related symptoms.
Additionally, we examined whether the bereaved family member’s relationship to the deceased predicted latent class membership, using a three-step multinomial logistic regression model (Supplementary Table 1). The reference category for the dependent variable was LC1 (low symptom group), and the reference group for the predictor variable (relationship) was parents (i.e., individuals who lost a child).
Compared to parents, siblings were significantly less likely to be classified into both LC2 (OR=0.34, 95% confidence interval [CI]=0.17–0.68, p<0.01) and LC3 (OR=0.28, 95% CI=0.12–0.68, p<0.01), indicating that siblings had a lower likelihood of experiencing moderate to high levels of trauma-related symptoms compared to parents. In contrast, participants who had lost a parent (i.e., child bereaved individuals) showed a trend toward increased odds of being in LC3 compared to LC1 (OR=3.77, 95% CI=0.66–21.61), although this effect did not reach statistical significance (p=0.136).
These findings underscore the disproportionately elevated psychological distress experienced by bereaved parents, who formed the majority of the high-symptom group. It also suggests that losing a child may lead to more persistent or complex grief responses, compared to other kinship losses.
Latent class profiles and their effects on outcomes
Table 5 presents the results of the three-step analysis examining the effect of latent class profiles on outcomes of quality of life and PTG. The analysis found significant differences in quality of life across the classes, with LC1 having the highest quality of life, followed by LC2 and then LC3. Posttraumatic growth was also significantly different between the classes, with LC2 reporting higher growth than LC1.
Three-step results to test predictors-contingent effects of latent class profiles on outcomes (quality of life & posttraumatic growth)
To further examine differences in adjustment outcomes based on the type of familial relationship to the deceased, we conducted one-way analysis of variances (ANOVAs) on quality of life and posttraumatic growth. As shown in Supplementary Table 2, significant differences were found in quality of life scores across relationship groups (F=3.08, p<0.01). Post hoc comparisons using Tukey’s Honestly Significant Difference (HSD) test indicated that parents (i.e., bereaved parents) reported significantly lower quality of life than siblings (p<0.05). However, no significant differences emerged in posttraumatic growth scores across relationship types (F=0.45, not significant). These results suggest that while quality of life may be more sensitive to the relational context of the loss, perceived posttraumatic growth may not differ significantly across family roles.
DISCUSSION
The present study sought to explore the psychological profiles of bereaved individuals using LCA to identify distinct subgroups based on symptoms of PTSD and complicated grief following the Sewol ferry disaster. The study identified three classes of low, moderate, and high symptomatology, characterized by varying levels of psychological distress and growth. These results suggest that symptoms of PTSD and grief tend to occur in similar rather than separate patterns in individuals who experienced traumatic loss in the Sewol ferry disaster. This is consistent with previous studies of traumatic loss that have reported correlations between the severity of PTSD symptoms and the severity of complicated grief symptoms [37,38]. However, other studies using cluster analysis have shown a somewhat different pattern than this study, such as a group dominated by PTSD symptoms and a group dominated by grief symptoms, depending on the nature of the trauma [39,40]. In the Sewol ferry disaster, almost all bereaved families experienced the trauma indirectly, watching the ship sink on TV rather than at the scene. Unlike other disasters in which individuals experience bereavement while suffering varying degrees of physical harm, most bereaved families experienced a relatively homogeneous form of secondary trauma exposure without physical injury. In such populations exposed to a relatively uniform traumatic event, it is likely that symptom clusters are formed not based on different symptom patterns, but rather on variations in symptom severity. Furthermore, high degree of comorbidity and overlap between traumatic grief and PTSD symptoms in this population, which may reflect the shared traumatic context of the loss [41]. Additionally, the prolonged and public nature of the Sewol ferry disaster, coupled with ongoing sociopolitical distress, may have contributed to a more generalized symptom elevation across both domains [42].
The analysis of predictors highlighted the roles of family and interpersonal stresses as well as social support on PTSD symptoms and complicated grief. As expected, higher levels of family and interpersonal stress were strongly associated with greater psychological distress. The ORs indicated that interpersonal stress had the most substantial impact on symptom severity, especially in differentiating the high and low symptomatology groups. This suggests that interpersonal dynamics, such as conflict or lack of support, may exacerbate psychological distress following a traumatic event. This is consistent with numerous studies that have explored the relationships of family conflict with interpersonal stress, posttraumatic stress symptoms, and grief reactions [43]. Dysfunctional families may exhibit more severe psychopathologic symptoms and have difficulty accessing community resources, which can greatly interfere with the grieving process [44]. In addition, family conflict and interpersonal stress can exacerbate psychopathology by acting as secondary stresses related to the traumatic loss, meaning that family members exposed to trauma may experience increased family conflict, isolation, role confusion, and lack of support and safety due to the stress associated with the traumatic loss [45,46]. Therefore, a variety of interventions to reduce family conflict and interpersonal stress may be necessary for recovery after a traumatic loss.
Notably, social support also emerged as a significant protective factor. Individuals with greater perceived social support were significantly less likely to belong to moderate- and highsymptom groups (LC2 and LC3), indicating a buffering effect against both PTSD symptoms and complicated grief. This finding is consistent with a substantial body of previous studies indicating that perceived social support is one of the most robust protective factors against trauma-related psychological symptoms and complicated grief [47-49]. In the context of the Sewol ferry disaster, where survivors and bereaved families faced prolonged exposure to public scrutiny, stigma, and politicization, the presence of stable and supportive interpersonal relationships may have played a crucial role in promoting resilience.
Higher social support likely contributed to emotion regulation, adaptive meaning-making, and access to coping resources in the face of traumatic bereavement. These results stress the importance of sustained social connection, even many years after a mass trauma, and suggest that enhancing perceived social support may be a viable target for long-term strategies.
An unexpected yet insightful finding was that the moderate symptomatology group reported higher levels of PTG compared to the low symptomatology group. This aligns with the theoretical understanding that engagement with trauma can lead to inner growth when the distress is not overwhelming. It appears that the moderate level of symptoms may have provided an “optimal” challenge, prompting reflection and growth without being incapacitating. Conversely, LC3, while also facing significant distress, did not exhibit the same levels of growth. This could be due to the overwhelming nature of their symptoms, which may hinder their ability to engage in the cognitive and emotional processes necessary for growth.
These findings also suggest that PTG is a phenomenon distinct from other forms of psychopathology, such as post-traumatic stress symptoms or complicated grief reactions [50-52]. This indicates that PTG is not necessarily inversely related to the severity of other psychopathological responses. In other words, individuals who exhibit PTG may still experience considerable posttraumatic symptoms regardless of their perceived internal growth. Therefore, PTG does not inevitably lead to symptom reduction or improvement in quality of life, and from the perspective of psychological suffering, it cannot be regarded as uniformly positive. Accordingly, interventions that emphasize PTG among bereaved individuals who have experienced trauma should take into account that such approaches may not necessarily alleviate their psychological distress.
As expected, quality of life significantly differed by severity of psychopathology. These results suggest that mental health status may be an important factor in quality of life after traumatic loss. This finding is consistent with numerous studies that have documented the relationships between quality of life, psychopathology, and mental health status [4,53]. Therefore, the results of this study once again demonstrate the importance of systematic assessment of mental health status and intervention to improve quality of life after traumatic loss. Therefore, to improve the quality of life of bereaved families, mental health assessment and intervention should be ongoing, expanding the focus beyond physical health and economic stability.
Finally, our supplementary analysis revealed that the bereaved individuals’ relationship to the deceased significantly predicted class membership. Specifically, siblings were much less likely to belong to the moderate or high symptom classes compared to parents, suggesting that parental bereavement is associated with markedly greater psychological distress. This pattern aligns with previous findings indicating that the loss of a child represents one of the most devastating and enduring forms of bereavement, often characterized by intense and prolonged grief reactions [54,55]. Although the small number of participants in some kinship categories limited statistical power, the observed trend that children who lost a parent tended to show higher odds of belonging to the high symptom class supports the notion that kinship proximity and emotional dependency may shape bereavement responses.
In line with these findings, additional analyses indicated that parents reported significantly lower quality of life than siblings, further underscoring the heightened psychological burden experienced by those who lost a child.
Taken together, these findings underscore the importance of considering the relational context of loss in understanding the heterogeneity of grief and trauma outcomes among the Sewol ferry bereaved families. Tailored interventions that address the unique emotional experiences of bereaved parents, as well as the distinct adjustment needs of siblings or children, may be particularly beneficial.
While the study provides valuable insights, several limitations should be noted. The cross-sectional design of the study limits the ability to draw causal inferences, and the reliance on self-report measures may introduce response biases. In addition, some constructs-such as perceived family and interpersonal stress-were assessed using single-item measures, which may not fully capture the multidimensional nature of these variables. Future investigations would benefit from employing validated multi-item scales to enhance measurement precision. Longitudinal designs are also needed to examine the trajectories of psychological distress and growth over time. Additionally, the study’s focus on a specific population of the bereaved families of the Sewol ferry disaster may limit the generality of the findings. Replicating this analysis in different populations and cultural contexts would be valuable in confirming the robustness of the identified latent classes. Finally, while our study identified symptom-based profiles and examined their associations with PTG and quality of life as outcomes, future research could explore alternative latent profile models incorporating PTG or quality of life as indicators. This may reveal different classification patterns based on broader adaptation trajectories, providing a complementary perspective to our symptom-focused approach.
Conclusion
This study conducted a LPA of bereaved families who lost a child in the Sewol ferry disaster to explore symptom profiles of complicated grief and grief-related post-traumatic symptoms. We also aimed to determine if there were significant differences in factors related to social life, PTG, and quality of life among these subgroups. The LPA analysis identified low, moderate, and high symptomatology groups. Family conflict, interpersonal stress, and perceived social support were significantly associated with complicated grief and posttraumatic stress symptoms after traumatic loss. In addition, the degree of psychopathology related to traumatic loss was significantly associated with quality of life. PTG was highest in the moderate symptomatology group, suggesting that PTG may be independent of the severity of psychopathology. Based on these findings, managing the mental health of people who have experienced a traumatic loss will be a critical component of improving their quality of life in the future. In addition, interventions to help reduce family conflict and interpersonal stress may be necessary to reduce difficulties associated with psychopathology. The present study is significant in that it examined the relationships between psychopathology and related factors in bereaved families who experienced the same trauma after a relatively long period of time, 7 years after the traumatic loss. Future studies should follow the relationships between these variables in a longitudinal framework.
Supplementary Materials
The Supplement is available with this article at https://doi.org/10.30773/pi.2025.0126.
Multinominal logistic regressionpredicting latent class membership based on by relationships to the deceased (R3STEP analysis)
Differences in quality of life and posttraumatic growth by the relationship to the deceased (one-way analysis of variance)
Notes
Availability of Data and Material
Data sharing will be available on request.
Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Author Contributions
Conceptualization: Jong-Sun Lee, Hyu Jung Huh, Jinhee Hyun. Data curation: Hyu Jung Huh, Jong-Sun Lee. Kyonghwa Kang. Formal analysis: Yu-Ri Lee, Jong-Sun Lee. Funding acquisition: Jinhee Hyun, Sunju Sohn. Investigation: Sunju Sohn, Hyu Jung Huh. Methodology: all authors. Supervision: Jinhee Hyun, Sunju Sohn. Writing—original draft: Jong-Sun Lee, Hyu Jung Huh. Writing—review & editing: all authors.
Funding Statement
None
Acknowledgments
Special thanks to the Ansan Mental Health Trauma Center for helping us make the investigation.