Skip to main content
NCBI home page
European Journal of Psychotraumatology logoLink to European Journal of Psychotraumatology
. 2024 Dec 18;15(1):2437957. doi: 10.1080/20008066.2024.2437957

Moral injury appraisals and PTSD symptoms in treatment-seeking refugees: a latent profile analysis

Valoración del daño moral y síntomas de TEPT en refugiados en busca de tratamiento: un análisis de perfil latente

Nora Mooren a,b,CONTACT, Paul A Boelen a,b, Anouk van Berlo c, Simone M de la Rie a,d
PMCID: PMC11656751  PMID: 39692001

ABSTRACT

Objective: Refugees flee from countries due to war, violence, or persecution and are often exposed to potentially traumatic events (PTEs). Furthermore, they might encounter situations where they are compelled to act contrary to their moral codes or witness others acting morally wrong. Consequently, they are at risk to not only develop symptoms of posttraumatic stress disorder (PTSD), but also moral injury (MI). To date, MI in traumatized refugees has received limited research attention. The present study sought to identify classes of MI appraisals and PTSD symptoms among refugees exposed to PTEs and to investigate differences between these classes in terms of demographics, general psychopathology, and depression.

Method: For this study, 136 treatment-seeking refugees completed questionnaires on demographics, self-directed and other-directed MI appraisals, PTSD symptoms, general psychopathology, and depression. Latent profile analysis was conducted to identify classes and regression analyses to explore differences between classes in terms of age, gender, general psychopathology, and depression.

Results: The following three classes were identified: a ‘below average MI and below average PTSD class’ (39%), an ‘average MI-self, below average MI-other and low PTSD class’ (10%) and an ‘above average MI and above average PTSD class’ (50%). Classes differed in terms of general psychopathology and depression but not age and gender.

Conclusions: We identified three classes, each displaying distinct manifestations of MI appraisals and PTSD symptoms. This highlights the importance of assessing and recognizing MI appraisals within treatment-seeking refugees, enabling customized treatment interventions for both MI and PTSD.

KEYWORDS: Moral injury, cognitive appraisals, refugees, PTSD, LPA

HIGHLIGHTS

  • In this study we identified different groups of refugees and found that these groups show different manifestations of moral injury (MI) appraisals and posttraumatic stress disorder (PTSD) symptoms.

  • These findings are important because they can inform treatment interventions for refugees. Trauma-focused therapy may suffice for a group of individuals presenting a high severity of PTSD symptoms without concurrent experiences of MI.

  • Some groups of individuals reporting PTSD symptoms combined with distress related to moral dilemmas may need additional interventions targeting MI and associated emotions of shame and guilt.

Refugees flee from countries due to war, violence, or persecution because of their political, religious, or social identity and are often exposed to potentially traumatic events (PTEs). PTEs that refugees may encounter such as rape and torture, can elicit typical fear-responses like avoidance and intrusions (Foa et al., 1995; McFarlane, 1992), as well as responses that lead to distortions in one's moral beliefs and identity (Nickerson et al., 2014; Silove, 1999). Events that may violate moral beliefs are referred to as potentially morally injurious events (PMIEs). PMIEs can result in moral stress and ultimately moral injury (MI) (Litz et al., 2009). Consistent with the working definition of MI (Litz et al., 2009), PMIEs are events that are appraised as moral transgressions enacted by oneself (e.g. MI-self) or by others (e.g. MI-other [Hoffman et al., 2018]) (Currier et al., 2018; Hoffman et al., 2019; Jordan et al., 2017; Nash et al., 2013). While most research on MI has traditionally focused on military contexts, an increasing number of studies indicate that MI is also highly relevant to refugee populations. Refugees often live in a context of war and violence and face transgressions that violate religious, political, or cultural moral values (Hoffman et al., 2018) and, therefore, the construct MI seems highly relevant for this population in addition to posttraumatic stress (PTS). MI appraisals have been found to be associated with PTS, anger, and depression (Hoffman et al., 2018). Moreover, longitudinal research indicated strong associations between MIappraisals and poor psychological outcomes in terms of depression and PTS in a refugee sample (Nickerson et al., 2020). In a recent qualitative study in a clinical group of traumatized refugees, Mooren et al. (2022) found that clinicians identified PMIEs such as leaving family members behind and putting them at risk of persecution, failing to prevent other people being harmed and not helping people in need as most important moral transgressions. Other studies identified transgressions such as witnessing how important others are harmed while failing to act (Brough et al., 2003), forced betrayal of others (Silove, 1999), and failing to resist during rape (Miller, 2009), and being witness of a murder and failing to act (Silove et al., 2009).

Despite the distinction between transgressions by oneself and others, some PMIEs could be appraised as both self- and other-transgressions. For example, watching how family members are attacked can be appraised as a moral transgression by oneself (MI-self) when one blames oneself for not having stopped the perpetrator, but also as a moral transgressions by others (MI-other) as the violence is inflicted by the perpetrator, or a combination of both. This suggests that MI-self and MI-other appraisals can occur separately and simultaneously.

Several studies investigated the relationship of MI-self and MI-other appraisals with psychopathology in refugees. Overall, findings suggested that both MI-self appraisals and MI-other appraisals were associated with symptoms of depression and anger whereas MI-other but not MI-self appraisals were related to elevated PTSD symptoms (Hoffman et al., 2018; Nickerson et al., 2014; Nickerson et al., 2015). In contrast to MI-other appraisals, MI-self appraisals were associated with lower re-experiencing and there was no clear association with avoidance, alterations in arousal and reactivity, and negative alterations in cognitions and mood symptoms (Hoffman et al., 2018). Research so far has primarily focused on military samples and there is limited empirical research exploring the co-occurrence of PTSD and MI and individual differences in how refugees respond to PMIEs. Additionally, research efforts aimed at differentiating profiles based on cognitive appraisals, particularly the distinction between MI-self and MI-other appraisals, remain notably limited. Yet, it is important to enhance knowledge about the cooccurrence of MI-appraisals and PTSD symptoms in refugees, as this may provide valuable insights to improve treatment interventions for trauma-related mental illness. For some individuals, trauma-focused therapy may be sufficient, especially those who present high severity of PTSD symptoms without concurrent MI appraisals. However, individuals reporting both PTSD symptoms and distress stemming from moral dilemmas may require interventions explicitly designed to address MI to attain the most favourable treatment outcomes.

Several studies have used latent profile analysis (LPA) to identify subgroups of individuals. LPA is a person-centered statistical method that enables the study of population variation by categorizing individuals into latent profiles based on continuous indicators of symptoms (Collins & Lanza, 2009). One study in refugees identified three different classes of MI appraisals: No-MI appraisals, MI-other appraisals, and both MI-self and MI-other appraisals and these classes differed in terms of psychological outcomes (Hoffman et al., 2019). However, to our knowledge, there are no studies in refugees that included PTSD symptoms in addition to MI appraisals to identify classes.

Accordingly, the primary objective of the present study was to identify classes among treatment-seeking refugees that differ in terms of MI appraisals and PTSD symptoms, using LPA. Based on research in military people and police officers (e.g. Mensink et al., 2022), we expected that different classes would emerge, including a class with below average MI appraisals and above average PTSD symptoms, a class with above average levels of MI appraisals and below average PTSD symptoms, a class with above average levels of MI appraisals (MI-self appraisals or MI-other or a combination of both) and above average PTSD symptoms, and a class with below average levels MI appraisals and below average PTSD symptoms. Our second objective was to explore differences between emerging classes in terms of age, gender, general psychopathology, and depression. Based on the literature, we expected classes to differ in terms of general psychopathology and depression but not age and gender (Mensink et al., 2022). More specifically, it was anticipated that individuals included in the class with elevated PTSD symptoms and MI appraisals would report the highest levels of general psychopathology and depression.

1. Method

1.1. Participants and procedure

In total, 156 refugees and asylum seekers, all referred for treatment to ARQ Centrum’45, were invited for an intake assessment from January 2022–June 2023. ARQ Centrum’45 is a specialized centre for diagnostics and treatment of patients with complex psychotrauma complaints. Prior to the assessment procedure, patients were provided an information letter and were asked to provide informed consent to use their data pseudo-anonymously for research purposes. From the total group of 156 refugees, 136 did so. During the assessment procedure, multiple questionnaires were administered to measure PTSD severity, general psychopathology, and MI appraisals. Inclusion criteria for the current study were (a) aged at least 18 years, (b) having an asylum-seekers or refugee status, and (c) being able to complete the questionnaires in one of the following languages: Dutch, English, French, Servo-Croatian, Arabic or Farsi. There were no exclusion criteria. The research was approved by the Faculty Ethics Review Committee (FETC) of Utrecht University under file number 20-297.

1.2. Measures

All questionnaires were available in the aforementioned languages. For the PTSD Checklist for DSM-5 (PCL-5; Wortmann et al., 2016), the Patient Health Questionnaire-9 (PHQ-9; Spitzer et al., 1999) and the Brief Symptom Inventory (BSI; Derogatis & Melisaratos, 1983), official translations were available. The Moral Injury Appraisals Scale (MIAS; Hoffman et al., 2018) was translated through the official translation procedure according to the WHO guidelines. First, the MIAS was translated from English to the relevant language by official translators with a psychological background, and were then translated back by official bilingual translators without a psychological background. Discrepancies were discussed in consensus groups, after which the final version was used in this study.

1.2.1. MIAS

The MIAS is a 9-item questionnaire (Hoffman et al., 2018) measuring cognitive appraisals of MI. It has two subscales: MI-self and MI-other. MI-self appraisals refer to being troubled by moral transgressions of oneself (e.g. ‘I am troubled because I did things that were morally wrong’). MI-other appraisals refer to being troubled by moral violations of others (e.g. ‘I am troubled by morally wrong things done by other people’). Respondents are instructed to indicate to what extent they agree with the statements on a 4-point Likert Scale, ranging from 1 = ‘strongly disagree’ to 4 = ‘strongly agree’. Internal consistency of this instrument in our sample was good (.88 for the full scale and for the MI-self and MI-other items, alphas were .94 and .85 respectively).

1.2.2. PCL-5

The PCL-5 measures PTSD symptoms based on the DSM-5 (Wortmann et al., 2016) and differentiates four clusters of PTSD; re-experiencing (criterion B), avoidance (criterion C), negative alterations in cognition and mood (criterion D), and hyperarousal (criterion E). It consist of 20-items and participants are asked to rate the presence of PTSD symptoms during the past month on a 5-point Likert scale (0 = ‘not at all’ to 4 = ‘extremely’). The total score indicates PTSD symptom severity (0–80). The psychometric properties of the questionnaire are adequate and scores higher than 33 indicate the presence of PTSD (Blevins et al., 2015; Krüger-Gottschalk et al., 2017; Weathers et al., 2018). In our study Cronbach’s α of the total scale was .93 and of the four symptom clusters these were .89, .86, .86, and .67, respectively.

1.2.3. BSI-53

General psychopathology was measured with the BSI (Derogatis & Melisaratos, 1983). This self-report questionnaire consists of 53 items. Respondents are instructed to rate the presence of psychological symptoms during the past seven days on a 5-point Likert scale (0 = ‘totally disagree’ to 4 = ‘totally agree’). This instrument has nine subscales (e.g. depressive mood, interpersonal sensitivity) and its total score (used in the present study) provides an index of general psychopathology. The psychometric properties of the BSI are good (de Beurs & Zitman, 2006). In the present study’s sample, Cronbach's α was .96.

1.2.4. PHQ-9

The PHQ-9 (Spitzer et al., 1999) is a 9-item self-report questionnaire measuring depressive symptoms. Participants are asked to rate how often they experienced depressive symptoms (e.g. ‘Little interest or pleasure in doing things’) for the past two weeks on a 4-point Likert scale (0 = ‘not at all’ to 3 = ‘almost every day’). The total score indicates the severity of the depressive symptoms. The PHQ-9 has good psychometric properties (Kroenke et al., 2001). In the current sample Cronbach's α was .83.

1.3. Statistical analysis

LPA was conducted to identify latent subgroups of refugees based on their self-reported PTSD symptoms and MI-appraisals. In this study we used the subscales of the PCL-5 (e.g. re-experiencing, avoidance, negative alterations in cognition and mood, and hyperarousal) and the two subscales of the MIAS (MI-self and MI-other) in the analyses. To obtain a similar scale for each instrument all scores were transformed into z-scores. Analyses were conducted in Mplus 8.6 (Muthén & Muthén, 2009). The one-class model was estimated first, followed by models with increasing numbers of classes. Model selection was based on fit statistics, interpretability, and parsimony. To avoid local likelihood maxima in BLRT, 500 bootstrap samples were requested with 50 sets of starting values in the first and 20 in each bootstrap sample. The following fit statistics criteria were used to select the optimal class solution: (a) lower values of the Akaike’s information criterion (AIC) and Bayesian information criterion (BIC), (b) a p-value of .05 for the Lo-Mendell-Rubin likelihood ratio test (LMR LRT; indicating that adding a class yields a significantly better-fitting model compared to a model with a class less), and (c) higher entropy values (with values closer to 1 indicating better class separation and values) (Nylund et al., 2007). Lastly, it was investigated whether the classes were different in terms of age, gender, general psychopathology, and depression. This was tested by conducting a series of four independent multinomial logistic regression models in Mplus using the three-step procedure (Asparouhov & Muthén, 2014) with age, gender, general psychopathology, and depression consecutively included in separate models. In addition gender differences between MI-self and MI-other appraisals were analyzed with a one-way ANOVA using SPSS version 27. The missing data analysis indicated less than 4% missing data for the MIAS, BSI and PHQ and 6% missing data for the PCL-5. For the LPA missing data were handled with full information maximum likelihood (FIML) by default. For the regression analysis the missing data were handled with listwise deletion.

2. Results

2.1. Descriptives

The sample consisted of 94 men (69%) and 42 women (31%). The average age was 43.7 years (SD = 12.2, range 19–66 years). The questionnaires were completed in the following languages: Dutch (55%), English (19%), Arabic (15%), and Farsi (11%). Furthermore, the region of origin was Africa in 11% of the refugees, 15% came from Europe, 64% of the Eastern Mediterranean region (e.g. Turkey, Syria, Lebanon, Egypt) and 4% of South East of Asia. The majority of patients (N = 100, 74%) met the criteria for PTSD according to the PCL-5 and the average PTSD severity in our sample was relatively high (M = 53.4, SD = 16.3). There were no gender differences with respect to MI-self appraisals, F(1, 134) < 1, p = .867, and MI-other appraisals, F(1, 134) < 1, p = .528. All patient characteristics in this sample are described in Table 1.

Table 1.

Descriptive statistics of demographic variables and clinical characteristics (N = 136).

Characteristics N % Mean SD Range
Age     43.73 12.2 19–66
Gender          
 Male 94 69.1%      
 Female 42 30.9%      
Asylum status          
 Permit resident 103 75.7%      
 Asylum seeker 26 19.1%      
 Undocumented 3 2.2%      
 Unknown 4 2.9%      
Moral injury appraisals (MIAS)     25.6 7.0 9–36
PTSD severity (PCL-5)     53.4 16.3 4–79
General psychopathology (BSI)     2.5 0.7 0.3–3.8
Depression (PHQ-9) 132   20.6 6.0 0–30
Open in a new tab

Note: PTSD = Posttraumatic Stress Disorder; MIAS = Moral Injury Appraisals Scale; PCL-5 = PTSD Checklist for DSM-5; BSI = Brief Symptom Inventory; PHQ-9 = Patient Health Questionnaire-9.

2.2. Latent profile analysis

Table 2 shows the fit indices for the one to five class solutions. Based on fit statistics, interpretability, and parsimony of the class solutions, the three-class solution was retained. Although the BIC value decreased and entropy increased from the one-class to five-class solution, the LMR LRT indicated that adding a fourth class did not result in a significantly better-fitting model compared to the three-class model. Figure 1 shows the classes identified in the three-class solution and displays the mean z-scores for each construct. The first class included 52 participants (39%) with below average levels of MI appraisals (both MI-self and MI-other) and below average levels of PTSD symptoms (on all clusters). This class was called the ‘below average MI and below average PTSD class’. The second class included 13 participants (10%) evidencing average MI-self appraisals, below average MI-other appraisals, and relatively low levels of PTSD symptoms (on all clusters). This class was labelled as the ‘average MI-self, below average MI-other and low PTSD class’. The third and largest class included 71 participants (50%) evidencing above average scores on MI-appraisals (both MI-self and MI-other) and above average levels of PTSD symptoms (on all clusters) and was labelled as the ‘above average MI and above average PTSD class’.

Table 2.

Goodness-of-fit indices for one- to five-class solutions.

Model Log-likelihood AIC BIC SSBIC B-LRT p-value LMR-A p-value Entropy
1 class −1100.925 2225.850 2260.802 2222.840 – – – – –
2 classes −1004.358 2046.715 2102.056 2041.950 193.134 <.0001 187.677 .0001 0.944
3 classes −959.420 1970.839 2046.568 1964.318 89.876 <.0001 87.337 .0461 0.829
4 classes −940.201 1946.402 2042.520 1938.126 38.437 <.0001 37.351 .3563 0.875
5 classes −920.881 1921.763 2038.269 1911.731 38.639 <.0001 37.547 .1667 0.916
Open in a new tab

Note: AIC = Akaike information criterion; BIC = Bayesian information criterion; SS-BIC = sample size-adjusted Bayesian information criterion; LMR-A = Lo-Mendell-Rubin-likelihood ratio adjusted; B-LRT = bootstrap-likelihood ratio test of model fit.

Figure 1.

Figure 1.

Open in a new tab

Symptom endorsement probability for the three-class solution.

Note: PTSD = Posttraumatic Stress Disorder; MI = Moral Injury.

2.3. Correlates of class membership

Table 3 represents the descriptive statistics of the variables age, gender, general psychopathology, and depression for the three classes. Results of the multinomial logistic regression analyses are presented in Table 4. Participants with higher levels of general psychopathology were significantly more likely to be included in the ‘above average MI and above average PTSD class’ compared to the ‘below average MI and below average PTSD class’ and the ‘average MI-self, below average MI-other and low PTSD class’. Also, participants with higher levels of general psychopathology were significantly more likely to be included in the ‘average MI-self, below average MI-other and low PTSD class’ compared to the ‘below average MI and below average PTSD class’. Furthermore, participants with higher levels of depression were more likely to be included in the ‘above average MI and above average PTSD class’ than the ‘average MI-self, below average MI-other and low PTSD class’. Also, participants with higher levels of depression were more likely to be included in the ‘average MI-self, below average MI-other and low PTSD class’ than in the ‘below average MI and below average PTSD class’. There were no significant differences between the classes in terms of age and gender.

Table 3.

Descriptive statistics for the three-class solution.

Measure Below average MI and below average PTSD class Average MI-self, below average MI- other and low PTSD class Above average MI and PTSD class
N (%) M SD M z-score N (%) M SD M z-score N (%) M SD M z-score
Age   44.1 13.9     41.4 0.51     44.4 11.1  
Gender 52       13       71      
 Female 13 (25.0)       5 (38.5)       24 (33.8)      
 Male 39 (75.0)       8 (61.5)       47 (66.2)      
Moral injury appraisals   22.9 6.7 −0.4   25.6 9.6 −76.9   27.6 6.1 0.3
 MI-Self   11.1 5.1 −0.3   13.2 6.4 −76.9   13.8 5.4 0.2
 MI-Other   11.8 3.2 −0.4   12.4 4.1 −76.8   13.8 2.4 0.3
PTSD severity (PCL-5)   47.2 5.6 −77.2   16.5 8.4 −2.3   65.3 5.5 −69.7
General psychopathology (BSI)   2.1 0.5 −192.5   1.2 0.7 −308.6   2.9 0.5 −182.4
Depression (PHQ-9)   18.6 5.3 −19.5   14.2 10.2 −77.8   23.2 3.9 −27.7
Open in a new tab

Note: PTSD = Posttraumatic Stress Disorder; MI = Moral Injury; MIAS = Moral Injury Appraisals Scale; PCL-5 = PTSD Checklist for DSM-5; BSI = Brief Symptom Inventory; PHQ-9 = Patient Health Questionnaire-9.

Table 4.

Summary of multinomial regression analyses with age, gender, depression, and general psychopathology predicting class-membership.

  Reference profile
Class 1: Below average MI and PTSD class (N = 52; 39%) Class 2: Average MI-self, below average MI- other and low PTSD class (N = 13; 10%) Class 3: Above average MI and PTSD class (N = 71; 50%)
 Comparison profile B SE CI P- value B SE CI P- value B SE CI P- value
Class 2: Average MI-self, below average MI- other and low PTSD class
Age 0.037 0.024 1.536 .124                
Gender −0.311 0.647 −0.481 .631                
General psychopathology 2.269 0.753 3.015 .003                
Depression 1.843 0.509 3.623 .000                
Class 3: Above average MI and PTSD class
Age 0.039 0.022 1.792 .073 0.002 0.016 0.149 .881        
Gender −0.196 0.623 −0.315 .753 0.115 0.416 0.275 .783        
General psychopathology 5.702 1.174 4.855 .000 3.433 0.916 3.747 .000        
Depression 0.599 0.396 1.514 .130 −1.244 0.256 −4.853 .000        
Open in a new tab

Note: PTSD = Posttraumatic Stress Disorder; MI = Moral Injury.

3. Discussion

The primary objective of this study was to identify classes of MI appraisals and PTSD symptoms among refugees exposed to PTEs. The LPA identified three classes with the ‘above average MI and above average PTSD class’ as largest class, followed by the ‘below average MI and below average PTSD class’, and the ‘average MI-self, below average MI-other and low PTSD class’ as smallest class. These results indicate that MI appraisals (in co-occurrence with PTSD or not) are relatively common in refugees. This is in line with other studies on MI appraisals in refugees (Hoffman et al., 2019; Nickerson et al., 2018). Also, these results suggest that self-directed and other-directed MI appraisals are distinct constructs, which was also supported in prior research of MI appraisals in refugees (Hoffman et al., 2018; Hoffman et al., 2019; Nickerson et al., 2018; Nickerson et al., 2020). The LPA did not point at the presence of a class of refugees with high (or above average) levels of PTSD symptoms and low (or below average) levels of MI appraisals. This suggest that, in this sample, increased PTSD symptoms always occur together with MI appraisals, whereas MI appraisals can be reported in absence of severe PTSD symptoms. One earlier study among refugees identified a MI-other class and a MI-other and MI-self class, but not an MI-self class (Hoffman et al., 2019). However, in our study, we identified a class with average levels of MI-self appraisals and below average levels of MI-other appraisals but not a separate MI-other appraisals class. As such, our results could be more in line with studies in military populations, evidencing a high prevalence of MI-self transgressions (Held et al., 2017). Although we had no data on the professional background of participants, it might be possible that our sample included a large number of military personnel who was active in combat in their home country and, therefore, might be more similar to the military populations described in other studies.

We also investigated whether age, gender, general psychopathology, and depression were associated with class membership. Our findings showed that there were significant differences between classes in terms of general psychopathology and depression, but not age and gender. Specifically, our results suggested that the co-occurrence of MI- self and MI-other appraisals with PTSD symptoms coincided with relatively higher levels general psychopathology and depression. This is in line with other studies in refugees (Hoffman et al., 2019) and military groups (Currier et al., 2015; Nash et al., 2013; Nash & Litz, 2013) showing that experiencing MI adds additional burden on top of experiencing PTSD in terms of concurrent psychopathology. Also, it appears that the class with both MI-self and MI-other appraisals (in addition to PTSD symptoms) evidenced higher levels of general psychopathology and depression in comparison to the class with above average MI-self appraisals, below average MI-other appraisals but low PTSD symptoms. This could indicate that the combination of MI-other appraisals and MI-self appraisals generates more psychological distress than MI-self appraisals alone. It is hypothesized that self-directed transgressions could be associated with higher levels of control compared to other-directed transgressions, mitigating psychological distress (Nickerson et al., 2018). Yet, more research is needed to replicate these results in other refugee populations to enhance knowledge about the degree to which MI-self and MI-other appraisals are differentially related to psychological problems.

Strengths of the present study include the examination of both MI appraisals and PTSD symptoms in a refugee sample and the use of a person-centered approach. Still, the findings need be interpreted in light of several limitations. First, the sample consisted of refugees with relative high levels of psychopathology and, therefore, it is uncertain to what extent the findings are generalizable to other groups of refugees (with less psychological complaints). For instance, the average PTSD severity score for the ‘below average MI and below average PTSD class’ was still relatively high in comparison to the cutoff score for PTSD (≥ 33). Replication of the findings in other samples is needed. Second, this study lacked measures on PTEs and PMIEs. Therefore, no insight can be given in the relationship between the nature of the events that participants encountered and the symptoms they developed afterwards. More research is needed to examine the extent to which the severity and cooccurrence of MI and PTSD differs as a function of the nature of PMIEs and PTEs that people experienced. For example, PMIEs in relation to active combat could be associated with more MI-self transgressions than traumatic events civilians are exposed to. Also, the relation between trauma-load and psychological symptoms for the different classes could be investigated. Third, this study used the MIAS which only measures cognitive appraisals and furthermore this study did not include measures on emotions associated with MI such as shame, guilt and anger. Including these emotions in future studies is relevant to understand the interplay between PTSD symptoms, MI-appraisals, and emotional processes in relation to MI. In addition, it would be interesting to examine the possible differences between MI-self and MI-other appraisals in terms of their linkages with different emotional outcomes. For example, there is some evidence that MI-self transgressions are more associated with internalizing emotions such as shame and guilt, whereas MI-other transgressions are more associated with externalizing emotions such as anger and resentment (Litz & Kerig, 2019). Lastly, differences between MI-self and MI-other appraisals may be influenced by culture and context. The way individuals interpret events as self-directed or other-directed may depend on various factors such as gender roles, cultural norms within ethnic groups, religious beliefs, or combat experiences. Consequently this could result in different psychopathological outcomes across different contexts. For example, research shows that women tend to experience PMIEs in different circumstances than men, leading them to more frequently report witnessing- and betrayal-based PMIEs (Maguen et al., 2020). Additionally, evidence suggests that MI-other appraisals are linked to post-migration challenges, such as financial instability, experiences of discrimination, and the process of securing residency in the host country (Nickerson et al., 2018). Lastly, our sample was relatively small, leading to possible reduced statistical power and increased risk of type II errors. Also, it might have been more difficult to identify additional classes in the LPA and to detect possible gender differences with a smaller sample size. Furthermore, to gain a clearer understanding of the overlap between these events and their psychological effects, future research should incorporate both PMIEs and PTEs in their assessments. Specifically, future studies could investigate the relationship between class membership and the type of event experienced. This could help identify whether certain types of events are more likely to lead to specific patterns of MI or PTSD symptoms within distinct populations. To our knowledge, this is the first study that examined profiles of MI appraisals and PTSD symptoms in a treatment-seeking refugee population. The results highlight the need to draw attention to MI in addition to PTSD symptoms in this population. In our sample, the majority of the treatment-seeking refugees reported stress associated with both PTSD symptoms and MI appraisals. An important clinical implication of our findings may be that one should be attentive for individuals that might need additional treatment interventions addressing PMIEs or MI. Specifically, our findings suggest that assessment and screening of psychological symptoms in this group should not solely focus on PTSD and other psychopathology (e.g. depression) but on MI appraisals as well. In terms of treatment interventions, there are several new approaches that might be promising. For instance, the Brief Eclectic Psychotherapy for Moral Trauma (BEP-MT; de la Rie et al., 2021), the Trauma-Informed Guilt Reduction Therapy (Norman, 2022), and an online supportive treatment module for MI (Ter Heide et al., 2022). These interventions focus on changing distorted beliefs of the PMIE and mitigating emotions of the PMIE (e.g. shame, guilt). Providing treatment interventions in addition to trauma-focused therapies is important to improve mental-wellbeing in treatment-seeking traumatized refugees.

Acknowledgements

We are grateful to all patients who participated in the study. In particular we thank Niels van der Aa for his assistance with the statistical analyses.

Disclosure statement

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

Transparency and openness

This study was not preregistered. We reported on data exclusions and all measures in the study. We do not have any previously published or currently in press works stemming from this dataset.

Data availability statement

The data are not publicly available due to their containing information that could compromise the privacy of the participants. Also, participants were not asked to give consent of saving their data in a public data repository.

References

  1. Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341. 10.1080/10705511.2014.915181 [DOI] [Google Scholar]
  2. Blevins, C. A., Weathers, F. W., Davis, M. T., Witte, T. K., & Domino, J. L. (2015). The Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): development and initial psychometric evaluation. Journal of Traumatic Stress, 28(6), 489–498. 10.1002/jts.22059 [DOI] [PubMed] [Google Scholar]
  3. Brough, M., Gorman, D., Ramirez, E., & Westoby, P. (2003). Young refugees talk about well-being: A qualitative analysis of refugee youth mental health from three states. Australian Journal of Social Issues, 38(2), 193–208. 10.1002/j.1839-4655.2003.tb01142.x [DOI] [Google Scholar]
  4. Currier, J. M., Farnsworth, J. K., Drescher, K. D., McDermott, R. C., Sims, B. M., & Albright, D. L. (2018). Development and evaluation of the expressions of moral injury scale-military version. Clinical Psychology & Psychotherapy, 25(3), 474–488. 10.1002/cpp.2170 [DOI] [PubMed] [Google Scholar]
  5. Collins, L. M., & Lanza, S. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences (Vol. 718). John Wiley & Sons. [Google Scholar]
  6. Currier, J. M., Holland, J. M., & Malott, J. (2015). Moral injury, meaning making, and mental health in returning veterans. Journal of Clinical Psychology, 71(3), 229–240. 10.1002/jclp.22134 [DOI] [PubMed] [Google Scholar]
  7. de Beurs, E., & Zitman, F.G. (2006). De drief symptom inventory (BSI): De betrouwbaarheid en validiteit van een handzaam alternatief voor de SCL-90. Maandblad Geestelijke Volksgezondheid, 61, 120–141. [Google Scholar]
  8. de la Rie, S. M., van Sint Fiet, A., Bos, J. B. A., Mooren, N., Smid, G., & Gersons, B. P. R. (2021). Brief Eclectic Psychotherapy for Moral Trauma (BEP-MT): Treatment protocol description and a case study. European Journal of Psychotraumatology, 12(1), 1929026. 10.1080/20008198.2021.1929026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Derogatis, L. R., & Melisaratos, N. (1983). The brief symptom inventory: An introductory report. Psychological Medicine, 13(3), 595–605. 10.1017/S0033291700048017 [DOI] [PubMed] [Google Scholar]
  10. Foa, E. B., Riggs, D. S., Massie, E. D., & Yarczower, M. (1995). The impact of fear activation and anger on the efficacy of exposure treatment for posttraumatic stress disorder. Behavior Therapy, 26(3), 487–499. [Google Scholar]
  11. Held, P., Klassen, B. J., Zalta, A. K., & Pollack, M. H. (2017). Understanding the impact and treatment of moral injury among military service members. Focus, 15(4), 399–405. 10.1176/appi.focus.20170023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Hoffman, J., Liddell, B., Bryant, R. A., & Nickerson, A. (2018). The relationship between moral injury appraisals, trauma exposure, and mental health in refugees. Depression and Anxiety, 35(11), 1030–1039. 10.1002/da.22787 [DOI] [PubMed] [Google Scholar]
  13. Hoffman, J., Liddell, B., Bryant, R. A., & Nickerson, A. (2019). A latent profile analysis of moral injury appraisals in refugees. European Journal of Psychotraumatology, 10(1), 1686805. 10.1080/20008198.2019.1686805 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Jordan, A. H., Eisen, E., Bolton, E., Nash, W. P., & Litz, B. T. (2017). Distinguishing war-related PTSD resulting from perpetration- and betrayal-based morally injurious events, 627–634. Educational Publishing Foundation. [DOI] [PubMed]
  15. Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. 10.1046/j.1525-1497.2001.016009606.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Krüger-Gottschalk, A., Knaevelsrud, C., Rau, H., Dyer, A., Schäfer, I., Schellong, J., & Ehring, T. (2017). The German version of the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): psychometric properties and diagnostic utility. BMC Psychiatry, 17(1), 379. 10.1186/s12888-017-1541-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Litz, B. T., & Kerig, P. K. (2019). Introduction to the special issue on moral injury: Conceptual challenges, methodological issues, and clinical applications. Journal of Traumatic Stress, 32(3), 341–349. 10.1002/jts.22405 [DOI] [PubMed] [Google Scholar]
  18. Litz, B. T., Stein, N., Delaney, E., Lebowitz, L., Nash, W. P., Silva, C., & Maguen, S. (2009). Moral injury and moral repair in war veterans: A preliminary model and intervention strategy. Clinical Psychology Review, 29(8), 695–706. 10.1016/j.cpr.2009.07.003 [DOI] [PubMed] [Google Scholar]
  19. Maguen, S., Griffin, B. J., Copeland, L. A., Perkins, D. F., Finley, E. P., & Vogt, D. (2020). Gender differences in prevalence and outcomes of exposure to potentially morally injurious events among post-9/11 veterans. Journal of Psychiatric Research, 130, 97–103. 10.1016/j.jpsychires.2020.06.020 [DOI] [PubMed] [Google Scholar]
  20. McFarlane, A. C. (1992). Avoidance and intrusion in posttraumatic stress disorder. Journal of Nervous and Mental Disease, 180(7), 439–445. 10.1097/00005053-199207000-00006 [DOI] [PubMed] [Google Scholar]
  21. Mensink, B., van Schagen, A., van der Aa, N., & Ter Heide, F. J. J. (2022). Moral injury in trauma-exposed, treatment-seeking police officers and military veterans: Latent class analysis. Frontiers in Psychiatry, 13, 904659. 10.3389/fpsyt.2022.904659 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Miller, S. C. (2009). Moral injury and relational harm: Analyzing rape in darfur. Journal of Social Philosophy, 40(4), 504–523. [Google Scholar]
  23. Mooren, N., Boelen, P. A., & de la Rie, S. M. (2022). The impact of morally injurious events in a refugee sample: A quantitative and qualitative study. Frontiers in Psychiatry, 13, 904808. 10.3389/fpsyt.2022.904808 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Muthén, L. K., & Muthén, B. O. (2009). Statistical analysis with latent variables. Mplus User’s Guide, 1998-2012. [Google Scholar]
  25. Nash, W. P., & Litz, B. T. (2013). Moral injury: A mechanism for war-related psychological trauma in military family members. Clinical Child and Family Psychology Review, 16(4), 365–375. 10.1007/s10567-013-0146-y [DOI] [PubMed] [Google Scholar]
  26. Nash, W. P., Marino Carper, T. L., Mills, M. A., Au, T., Goldsmith, A., & Litz, B. T. (2013). Psychometric evaluation of the moral injury events scale. Military Medicine, 178(6), 646–652. 10.7205/MILMED-D-13-00017 [DOI] [PubMed] [Google Scholar]
  27. Nickerson, A., Bryant, R. A., Rosebrock, L., & Litz, B. T. (2014). The mechanisms of psychosocial injury following human rights violations, mass trauma, and torture. Clinical Psychology: Science and Practice, 21(2), 172–191. 10.1111/cpsp.12064 [DOI] [Google Scholar]
  28. Nickerson, A., Byrow, Y., Hoffman, J., O'Donnell, M., Bryant, R. A., Mastrogiovanni, N., McMahon, T., Benson, G., Mau, V., & Liddell, B. J. (2020). The longitudinal association between moral injury appraisals and psychological outcomes in refugees. Psychological Medicine, 52(12), 2352–2364. 10.1017/S0033291720004262 [DOI] [PubMed] [Google Scholar]
  29. Nickerson, A., Hoffman, J., Schick, M., Schnyder, U., Bryant, R. A., & Morina, N. (2018). A longitudinal investigation of moral injury appraisals amongst treatment-seeking refugees. Frontiers in Psychiatry, 9, 667. 10.3389/fpsyt.2018.00667 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Nickerson, A., Schnyder, U., Bryant, R. A., Schick, M., Mueller, J., & Morina, N. (2015). Moral injury in traumatized refugees. Psychotherapy and Psychosomatics, 84(2), 122–123. 10.1159/000369353 [DOI] [PubMed] [Google Scholar]
  31. Norman, S. (2022). Trauma-informed guilt reduction therapy: Overview of the treatment and research. Current Treatment Options in Psychiatry, 9(3), 115–125. 10.1007/s40501-022-00261-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. 10.1080/10705510701575396 [DOI] [Google Scholar]
  33. Silove, D. (1999). The psychosocial effects of torture, mass human rights violations, and refugee trauma: Toward an integrated conceptual framework. The Journal of Nervous and Mental Disease, 187(4), 200–207. [DOI] [PubMed] [Google Scholar]
  34. Silove, D., Brooks, R., Steel, C. R. B., Steel, Z., Hewage, K., Rodger, J., & Soosay, I. (2009). Explosive anger as a response to human rights violations in post-conflict Timor-Leste. Social Science & Medicine, 69(5), 670–677. [DOI] [PubMed] [Google Scholar]
  35. Spitzer, R. L., Kroenke, K., & Williams, J. B. (1999). Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. Primary care evaluation of mental disorders. Patient health questionnaire. Jama, 282(18), 1737–1744. 10.1001/jama.282.18.1737 [DOI] [PubMed] [Google Scholar]
  36. Ter Heide, F. J. J., de Goede, M. L., van Dam, S., & Ekkers, S. (2022). Development of an online supportive treatment module for moral injury in military veterans and police officers. Frontiers in Psychiatry, 13, 890858. 10.3389/fpsyt.2022.890858 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Weathers, F. W., Bovin, M. J., Lee, D. J., Sloan, D. M., Schnurr, P. P., Kaloupek, D. G., Keane, T. M., & Marx, B. P. (2018). The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5): Development and initial psychometric evaluation in military veterans. Psychological Assessment, 30(3), 383–395. 10.1037/pas0000486 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Wortmann, J. H., Jordan, A. H., Weathers, F. W., Resick, P. A., Dondanville, K. A., Hall-Clark, B., Foa, E. B., Young-McCaughan, S., Yarvis, J. S., Hembree, E. A., Mintz, J., Peterson, A. L., & Litz, B. T. (2016). Psychometric analysis of the PTSD checklist-5 (PCL-5) among treatment-seeking military service members. Psychological Assessment, 28(11), 1392–1403. 10.1037/pas0000260 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data are not publicly available due to their containing information that could compromise the privacy of the participants. Also, participants were not asked to give consent of saving their data in a public data repository.

Articles from European Journal of Psychotraumatology are provided here courtesy of Taylor & Francis

RESOURCES