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European Journal of Psychotraumatology logoLink to European Journal of Psychotraumatology
. 2024 Aug 14;15(1):2387477. doi: 10.1080/20008066.2024.2387477

Latent transition analysis on post-traumatic stress and post-traumatic growth among firefighters

Análisis de transición latente sobre el estrés postraumático y el crecimiento postraumático en bomberos

Yongchan Shin a, JeeEun Karin Nam b, Minho Park c, Aeyoung Lee d, Youngkeun Kim d,e,CONTACT
PMCID: PMC11328797  PMID: 39140605

ABSTRACT

Background: Firefighters, in the course of their professional responsibilities, confront an array of stressors contingent upon the distinctive characteristics of their duties.

Objective: This study investigated the longitudinal impact of trauma incidents during duty on firefighters using latent transition analysis.

Method: Data from 346 firefighters in South Korea who had experienced trauma events while on duty were utilized. Initially, latent groups were identified based on the relationship between post-traumatic stress disorder (PTSD) and post-traumatic growth (PTG). Groups were labelled based on the analysis of differences in PTSD, mental health, and growth-related factors among classified groups. Subsequently, transition probabilities and patterns from Time 1 to Time 2 were examined, followed by an investigation into variances based on demographic factors (gender, age) and occupational factors (work experience, shift pattern) using variance analysis and multinomial logistic regression analysis.

Results: First, at Time 2, a five-group model was classified into ‘Growth,’ ‘Resilience or Numbness,’ ‘Struggle,’ ‘Partial Struggle,’ and ‘PTSD’ groups. Second, upon examining the transition patterns between latent groups, four patterns emerged: ‘continued distress,’ ‘growth,’ ‘adaptation,’ and ‘escalated distress.’ Third, the ‘Struggle’ group showed a 0% probability of transitioning to the ‘Growth’ group, whereas it displayed the highest probability among the groups transitioning to the ‘PTSD’ group. Fourth, latent transition analysis results showed a strong tendency for the ‘Growth’ group and ‘Resilience or Numbness’ group to remain in the same category. Fifth, age was found to be a significant factor affecting the transition of latent groups.

Conclusion: This research represents the inaugural attempt to longitudinally investigate the interplay between PTSD and PTG among firefighters.

KEYWORDS: Firefighters, post-traumatic stress, PTSD, post-traumatic growth, latent transition analysis

HIGHLIGHTS

  • Firefighters, in the course of their professional responsibilities, confront an array of stressors contingent upon the distinctive characteristics of their duties.

  • The transition patterns of firefighters between latent groups revealed at Time 1 and Time 2 were examined, revealing four transition patterns: ‘continued distress,’ ‘growth,’ ‘adaptation,’ and ‘escalated distress.’

  • This research represents the inaugural attempt to longitudinally investigate the interplay between post-traumatic stress disorder and post-traumatic growth among firefighters.

1. Introduction

Firefighters, in the course of their professional responsibilities, confront an array of stressors contingent upon the distinctive characteristics of their duties. The irregularity inherent in their work schedules, coupled with the manifold hazards intrinsic to their occupational milieu, along with poignant experiences at the scenes of incidents, collectively precipitate both physical and psychological stress. This, in turn, can culminate in the manifestation of profound traumatic experiences of a serious nature. According to the Korean National Fire Agency (2018), the exposure to severe traumatic incidents during professional duties occurs, on average, 7.8 times annually, with a prevalence rate of experiencing such events at least once a month standing at 17%, indicating a notably elevated frequency. Furthermore, the prevalence of post-traumatic stress disorder (PTSD) among firefighters, as compared to the general population, is reported to be 10.5 times higher. PTSD poses a substantial risk of impairing job performance, diminishing job satisfaction, and overall functional deterioration, often leading to adverse health outcomes such as depression and sleep disorders (Boffa et al., 2017; Del Ben et al., 2006).

To address these risks, various studies are underway to facilitate the recovery of mental health and job adaptation for individuals coping with PTSD. In this context, the exploration of post-traumatic growth (PTG) among firefighters holds significant importance. As highlighted by Calhoun and Tedeschi (2012), individuals post-trauma may experience growth, showcasing enhanced functionality compared to their pre-traumatic state, achieved through a process of comprehending and integrating the shocking events. Stressing the development of strengths and resilience rather than exclusively concentrating on the trauma itself (Richardson, 2002), it is imperative to integrally consider PTG in the endeavour to assist firefighters in adeptly adapting to their responsibilities without succumbing to professional attrition in the aftermath of trauma.

Given that both occurrences unfold in the aftermath of distress, a noteworthy correlation exists between PTSD and PTG (Calhoun & Tedeschi, 2012). Intrusive rumination and hyperarousal triggered by traumatic events may result in profound suffering but also have the potential to stimulate personal growth by instigating a reassessment of life (Cann et al., 2011; Dekel et al., 2012). Growth may not happen unless an individual’s core beliefs and cognitive frameworks undergo some level of challenge or change (Calhoun & Tedeschi, 2012). However, the process of moving towards post-traumatic growth, influenced by the complex nature of trauma and an individual’s subjective responses to stress, presents inconsistent outcomes, including positive (Zalta et al., 2017), negative (Ssenyonga et al., 2013), and independent (Joseph et al., 1993) relationships. Therefore, for a more comprehensive understanding of the connection between PTSD and post-traumatic growth, research should delve into individual variations in responding to distress and consider how these patterns evolve over time. Prior LPA studies on the relationship between PTSD and PTG are presented in Table 1.

Table 1.

Prior LPA studies on the relationship between PTSD and PTG (a partial list)

Study Subjects (N) Type Scale Latent class M(SD) SE
Zhou et al. (2019) Women (1487) Earthquake PTGI Moderate PTSD/ moderate PTG (39.4%) PTSD 2.13(0.44) –
PTG 2.74(0.36) –
High PTSD/ high PTG (21.1%) PTSD 3.36(0.51) –
PTG 3.63(0.42) –
PCL-C Mild PTSD/ high PTG (17.5%) PTSD 1.63(0.41) –
PTG 4.02(0.52) –
Mild PTSD/ mild PTG (12.4%) PTSD 1.80(0.48) –
PTG 1.42(0.49) –
High PTSD/ moderate PTG (9.6%) PTSD 3.63(0.50) –
PTG 2.42(0.40) –
Shin et al. (2023) Adults (483) Firefighters’s experience of traumatic events SRGS-R Low PTSD/ high PTG (15.3%) PTSD 2.28(3.59) 0.42
PTG 27.24(7.12) 0.83
Low PTSD/ low PTG (65.2%) PTSD 2.52(3.33) 0.19
PTG 2.17(7.90) 0.45
High PTSD/ mid PTG (3.9%) PTSD 45.11(6.09) 1.40
PTG 8.26(12.63) 2.90
IES-R Mid PTSD/ mid PTG (15.5%) PTSD 21.97(4.73) 1.40
PTG 6.11(12.56) 2.90
Zhen & Zhou (2022) Adolescents (683) During COVID-19 PTGI Growth group (36.3%) PTSD 12.60(7.16) –
PTG 50.56(24.24) –
Distress group (14.8%) PTSD 44.81(9.51) –
PTG 44.64(21.68) –
PCL-5 Struggling group (48.9%) PTSD 29.10(7.41) –
PTG 50.21(18.54) –
Chen & Wu (2017) Children & adolescents (618) Earthquake PTGI Thriving (76.2%) PTSD 10.24(0.44) –
PTG 68.75(1.07) –
Stressed and Growing (14.7%) PTSD 23.96(1.22) –
PTG 63.79(2.06) –
CPSS Resilient (9.1%) PTSD 9.36(0.88) –
PTG 29.11(3.15) –
Zhou et al. (2018) Adolescents (619) Earthquake PTGI Coexistence group (50.1%) PTSD 20.13(0.41) –
PTG 68.09(1.71) –
Growth group (39.6%) PTSD 7.53(0.48) –
PTG 76.45(1.53) –
CPSS Low symptoms group (10.3%) PTSD 8.42(0.88) –
PTG 28.51(3.44) –
Chen & Tang (2021) Adults (422) Bereaved due to COVID-19 PTGI Moderate-combined (42.2%) PTSD 40.43(8.33) –
PTG 57.91(11.28) –
High-combined (27.0%) PTSD 56.43(7.34) –
PTG 79.47(7.75) –
PCL-5 Growth (20.1%) PTSD 19.57(8.04) –
PTG 73.78(10.73) –
Resilience (10.7%) PTSD 19.84(8.90) –
PTG 38.09(12.25) –
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Note. PTSD: posttraumatic stress disorder; PTG: posttraumatic growth; M: mean, SD: Standard deviation; PTGI: Posttraumatic Growth Inventory; PCL-C: PTSD Checklist-Civilian Version; SRGS-R: Stress Related Growth Scale-Revised; IES-R: Imapact of Events Scale-Revised; PCL-5: PCL for DSM-5; CPSS: Child PTSD Symptom Scale.

Even when extending the consideration beyond studies solely focused on PTG to encompass broader research on trauma-related aspects among firefighters, there remains a scarcity of longitudinal investigations. Studies have delved into the psychological and emotional consequences following traumatic incidents (Berninger et al., 2010; Pineles et al., 2013; VanderVeen et al., 2012), as well as interventions assessing changes post-intervention (Skeffington et al., 2016). However, there is a notable absence of comprehensive longitudinal studies specifically addressing PTG among firefighters. Lee (2009) examined firefighters’ PTSD and PTG levels over a seven-month period, finding that PTSD tends to decline over time, and that greater initial PTSD levels might lead to more significant PTG later. However, the study was conducted over a relatively short duration, and did not identify specific patterns of change considering individual differences.

Thus, the study aimed to comprehend the trajectory of post-traumatic changes in firefighters and pinpoint the factors influencing this process. The research followed a dual approach. Initially, it investigated the correlation between PTSD and PTG among firefighters using latent profile analysis (LPA) to discern intergroup distinctions by creating latent groups that consider individual differences. Subsequently, the study longitudinally explored the transition patterns of these latent groups across different time points through latent transition analysis (LTA). LTA involved executing LPA separately for Time 1 and Time 2, followed by scrutinizing the transition patterns of latent groups across the two time points. In a LTA study conducted on the general public, there was a high probability that the latent groups for PTSD and PTG would transition into the same group (Chen & Wu, 2017). However, considering that firefighters routinely face traumatic events owing to their line of work, and they have relatively easy access to mental health services for related experiences, it is crucial to explore the longitudinal changes in this demographic.

In this investigation, preliminary data from Shin et al. (2023) were employed as Time 1 data. At each time point, latent groups were categorized based on the levels of PTSD and PTG. Recognizing the multifaceted nature of trauma, challenges were acknowledged in distinguishing groups solely based on the general levels of PTSD and PTG. Therefore, in alignment with Shin et al.’s (2023) approach, latent group characteristics were examined through the levels of specific factors related to PTSD and PTG, such as dissociation, depression, rumination, suicidal ideation, emotion-based response, mentalization, and psychological acceptance. In examining the relevance of each factor, dissociation is a key symptom of PTSD and rumination is not only a primary symptom of PTSD but also an influential factor when progressing towards PTG (Ahn et al., 2013; Cann et al., 2011). Moreover, depression and suicidal ideation are closely linked to PTSD and are seen as particularly high-risk factors for firefighters, given the hazardous nature of their work (Jo & Park, 2012; Kim & Yook, 2018). Emotion-based response (Tedeschi & Calhoun, 2004) and mentalization (Seo & Kim, 2018) play crucial roles in the handling and comprehension of negative emotions associated with trauma, which are vital when advancing towards PTG. Furthermore, the construct of psychological acceptance (Hass, 1994) has been utilized to assess the extent to which individuals accept pain and emotions after experiencing a traumatic event.

Demographic variables such as age, gender, years of service, and shift pattern were examined to understand the factors affecting the transition of latent groups. These variables were previously explored in Shin et al.’s (2023) study, which served as the Time 1 data for this research, revealing that years of service and shift pattern significantly impacted group classification. These factors have also been reported in previous studies to affect either PTSD or PTG. Specifically, older age was associated with a higher use of deliberate rumination, and being female increased the likelihood of experiencing PTG through emotion-based response (Jin et al., 2014; Shakespeare-Finch & Lurie-Beck, 2014; Vishnevsky et al., 2010). Work schedule, particularly shift work, has been linked to an elevated risk of PTSD due to factors like sleep quality and circadian rhythm disruption (Åkerstedt & Kecklund, 2017; Germain, 2013; Sopp et al., 2021). In contrast, the impact of work experience yielded diverse results; certain studies indicated that accumulating stress over time increases the likelihood of PTSD (Martin et al., 2017; Morris et al., 2016), while others suggested that greater expertise might facilitate PTG (Kwak & Bae, 2017).

In sum, this study analyzes the impact of traumatic experiences on firefighters, considering individual differences and the passage of time. It aims to provide distinct findings compared to previous research and offer insights into counselling and psychological interventions based on individual resources and trauma levels post-firefighters’ experiences.

2. Methods

2.1. Participants and procedures

Time 1 data were obtained from a latent profile analysis conducted in February–March 2021 as part of the 2020 4.16 Foundation Life Safety Academic Research Support Project (Shin et al., 2023). Approximately 20 months later, the Time 2 survey targeted 448 individuals from the original 483, excluding 35 due to incomplete personal information (24), retirement (5), death (2), or unclear workplace (4). Among the 403 actual respondents, 57 participants provided unreliable responses and were excluded. The demographic characteristics of 346 participants for Time 2 are summarized in Table 2. The results of the traumatic experience are as shown in Table 3.

Table 2.

Demographic characteristics of study participants at Time 2.

Characteristics Classification Frequency(person) Percent (%)
Total 346 100.0
Gender Male 327 94.5
Female 19 5.5
Age 18–29 34 9.8
30–39 131 37.9
40–49 110 31.8
50–59 70 20.2
60–69 1 0.3
Years of Service 0∼5 101 29.2
6∼10 66 19.1
11∼15 58 16.8
16∼20 47 13.6
21∼25 32 9.2
26∼30 39 11.3
31∼35 2 0.6
36 and over 1 0.3
Shift pattern Fixed 83 24.0
Rotating 261 75.0
Etc. 2 0.6
Rank Fire fighter 65 18.8
Senior Firefighter 84 24.3
Fire Sergeant 87 25.1
Fire Lieutenant 92 26.6
Fire Captain or higher 18 5.2
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Table 3.

Traumatic events experienced during duty at Time 2.

Characteristics Classification Frequency (person) Percent (%)
  Survey completed 346 100
Traumatic events as firefighter Witnessed or handled a dead body 250 72.3
Handled a body of a suicide victim 234 67.6
Witnessed victim or patient dying at the scene 213 61.6
Verbally or physically assaulted 142 41.0
Felt one’s life threatened; fear of serious injury 138 39.9
Exposed to toxic substances or risk of infection 95 27.5
Rescued a severely damaged child or handled the body of a dead child 85 24.6
Rescued or handled mass casualties 73 21.1
Witnessed severe injury to colleague 62 17.9
Threatened or attacked with a weapon 52 15.0
Sued or threatened 52 15.0
Death of a colleague 35 10.1
Life-threatening injuries to oneself 23 6.6
Sexually harassed 13 3.8
Seriously injured or killed other(s) 3 0.9
Sexually assaulted - -
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2.2. Measures

2.2.1. Survey of possible trauma experiences as firefighter

The traumatic incidents that firefighters faced during their service were surveyed using a trauma experience questionnaire developed by Choi (2010). Certain items were adjusted, such as transforming item 4 from ‘physically assaulted’ to ‘verbally or physically assaulted’ and item 6 from ‘threatened’ to ‘sued or threatened.’ This survey comprised a total of 17 questions, prompting respondents to indicate whether they have experienced various types of traumatic events (e.g. life-threatening injuries) and the frequency of those experiences (never experienced, experienced). This was done to assess the presence of traumatic experiences and the number of traumatic events encountered.

2.2.2. Intrusion, avoidance, hyperarousal, sleep & numbness

The Korean Version of the Impact of Event Scale-Revised (IES-R-K; Eun et al., 2005) was utilized. The Impact of Event Scale (IES) is a self-report measure developed based on the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria (Weiss & Marmar, 1997). IES-R-K consists of intrusion (5 items, e.g. ‘Other things kept making me think about it.’), avoidance (6 items, e.g. ‘I tried not to think about it.’), hyperarousal (6 items, e.g. ‘I was jumpy and easily startled.’), and sleep & numbness (5 items, e.g. ‘I had trouble staying asleep.’) as four subscales. It is evaluated using a 5-point Likert scale (0: Not at all, 4: Extremely), with a total of 22 items. Scores of 17 or below are considered normal, 18 to 24 as partial PTSD, and 25 or above as full PTSD (Eun et al., 2005). In Eun et al.’s (2005) study, the internal consistency was reported as 0.63 for intrusion, 0.70 for avoidance, and 0.87 for hyperarousal. In this study, to enhance clarity of individual symptom characteristics and interpretability as a sub-dimension of trauma experiences, each of the four subscales was separately used to assess the level of PTSD. In this study, Cronbach’s α was 0.92 for intrusion, 0.94 for avoidance, 0.92 for hyperarousal, and 0.80 for sleep & numbness.

2.2.3. Dissociative experiences

We utilized the Korean version of Dissociative Experiences Scale (DES; Bernstein & Putnam, 1986), a measure adapted and validated in South Korea by Park et al. (1995). The Korean version of DES consists of 28 items, grouped into four subfactors (Park et al., 1995): absorption, amnesia, depersonalization-derealization, and inattention. A total score of 20 points or higher is classified as indicative of a high likelihood of a dissociative disorder (Park et al., 1995). While the original DES used a somewhat complex scoring system with a horizontal line from 0 to 100%, this study employed the DES-II, which addresses this complexity by using an 11-point Likert scale (ranging from 0, ‘never,’ to 10, ‘always’) to represent scores on a continuum from 0 to 100%. In Park et al.’s (1995) study, Cronbach’s α was .94, and in our study, it was found to be .97, indicating high reliability.

2.2.4. Post-traumatic growth

We utilized the Revised Korean Version of the Stress Related Growth Scale (SRGS-R-K), adapted and validated by Shin and Kim (2019) from the work of Boals and Schuler (2018). The SRGS-R-K consists of a total of 13 items rated on a 7-point Likert scale (−3: Intense negative changes, 0: No change, +3: Intense positive changes). It is composed of two factors: enhanced personal resources (e.g. ‘I experienced a change in the extent to which I find meaning in life.’) and enhanced social resources (e.g. ‘I experienced a change in the extent to which I communicate honestly with others.’). A lower total score indicates negative changes, while a higher score indicates positive changes. In the study conducted by Shin and Kim (2019), the Cronbach’s α for total scale was .93. In the current study, the reliability was .95 for enhanced personal resources and .95 for enhanced social resources.

2.2.5. Intrusive rumination and deliberate rumination

We utilized the Korean version of the Event-Related Rumination Inventory (K-ERRI), adapted and validated by Ahn et al. (2013) based on the work of Cann et al. (2011). The K-ERRI comprises 20 items, with 10 items each measuring intrusive rumination (e.g. ‘Thoughts about the event came to mind and I could not stop thinking about them.’) and deliberate rumination (e.g. ‘thought about whether changes in my life have come from dealing with my experience.’), rated on a 4-point Likert scale (0: Not at all, 3: Often). Ahn et al. (2013) reported a Cronbach’s α of .93 for both intrusive and intentional rumination. In our study, the reliability was .96 for intrusive rumination and .96 for deliberate rumination.

2.2.6. Depression

To measure depression, we utilized the Integrated Korean version of the Center for Epidemiologic Studies Depression Scale (CES-D), adapted and validated by Chon et al. (2001) based on the work of Radloff (1977). The scale consists of 20 items (e.g. ‘I thought my life had been a failure.’) rated on a 4-point Likert scale (0: Rarely or none of the time, 1: Some or a little of the time, 3: Most or all of the time, 5-7 days). A total score of 16 or higher indicates severe depressive symptoms. Chon et al. (2001) reported a Cronbach’s α of .91, while our study showed .90.

2.2.7. Suicidal ideation

The Scale of Suicidal Ideation (SSI; Beck et al., 1979) adapted and validated by Park and Shin (1990) was employed. SSI consists of a total of 19 items rated on a 3-point Likert scale (0: No ideation, 2: Strong ideation). A score of 9–11 suggests a need for attention, 12–14 is considered a risk level, and 15 or above signifies high-risk for suicide. In Park and Shin’s (1990) study, Cronbach’s α was reported as 0.87, and in this study, it was found to be 0.81.

2.2.8. Emotion-based response

Emotion-based response was conceptualized based on the theory of Greenberg and Safran (1987), comprising three subfactors: emotional awareness, emotional processing, and emotional expression. Emotional awareness utilized 11 items from the Korean version of the Trait Meta-Mood Scale (TMMS; Salovey et al., 1995) adapted and validated by Lee and Lee (1997). TMMS employs a 5-point Likert scale (1: Not at all, 5: Very much). Emotional processing and emotional expression each utilized 8 items from the Emotional Approach Coping Scale (EAC; Stanton et al., 2000) adapted and validated by Kang and Yang (2015). EAC is structured on a 4-point Likert scale (1: Not at all, 4: Very much), and higher scores in both TMMS and EAC indicate higher levels of emotional awareness, processing, and expression. In the study by Seo and Kim (2018), Cronbach’s α for emotional awareness was .75, emotional processing was .88, and emotional expression was .88. In this study, emotional awareness was .71, and emotional processing and expression were .93.

2.2.9. Mentalization

The Self-Rated Mentalization Questionnaire (SRMQ), developed by Park and Chung (2019) based on Fonagy’s (1991) operational definition of reflective function as mentalization, was utilized. The SRMQ comprises four subdomains: reflection of the self and other, absolute certainty about others’ minds, deficit of emotion awareness, and concrete thinking. In this study, ‘absolute certainty about others’ minds’ was excluded due to potential interpretation variation by readers (Park & Chung, 2019). A total of 25 items were used to assess mentalization. The SRMQ employs a 5-point Likert scale (1: Not at all, 5: Very much), and when concrete thinking and deficit of emotion awareness are reverse-coded and summed with reflection of the self and other, higher total scores indicate higher levels of mentalization. In Park and Chung (2019)’s study, Cronbach’s α was .87 for concrete thinking, .84 for reflection of the self and other, and .80 for deficit of emotion awareness, while in this study, the overall Cronbach’s α was .91.

2.2.10. Psychological acceptance

The Korean version of the Acceptance and Action Questionnaire II (AAQ-II) revised by Heo et al. (2009) was used. This version is based on the translation and validation of the original instrument by Hayes et al. (2004), which was subsequently revised by Bond et al. (2011). The AAQ-II consists of a single factor with a total of 8 items (e.g. ‘My painful memories prevent me from having a fulfilling life.’), rated on a 7-point Likert scale (1: Never true, 4: Somewhat true, 7: Always true). Higher total scores indicate higher levels of psychological acceptance. In Heo et al.’s (2009) study, Cronbach’s α was .85, while in this study, it was .75.

2.3. Data analysis

This study employed SPSS 21.0 and Mplus 8.0 for statistical analysis. Initially, descriptive statistics (mean, standard deviation, range) for the utilized factors were examined. Additionally, Confirmatory Factor Analysis (CFA) was conducted to determine if the sub-factors of PTSD and PTG – excluding demographic factors and related factors – fit the covariance matrix of the research model. Subsequently, to conduct LPA for Time 2, the analysis focused on the sub-factors of PTSD (intrusion, hyperarousal, avoidance, sleep & emotional numbness) and PTG (enhanced personal resources, enhanced social resources). To determine the number of latent groups, information criterion indices (BIC, sample size adjusted BIC), Entropy, and Bootstrap Likelihood Ratio Test (BLRT) were employed. Group proportions and interpretability were also considered in the decision-making process (Kwon, 2011; Kwon & Yang, 2014). Lower BIC and saBIC values, Entropy above 0.8, and significant BLRT (p < .05) were considered appropriate. Group proportions were set based on a threshold of 1% of the sample size (Hill et al., 2000).

Subsequently, the study examined the characteristics and differences of each latent group based on the levels of PTSD-related factors (intrusive rumination, deliberate rumination, depression, suicidal ideation) and PTG-related factors (emotion-based response, mentalization, psychological acceptance), naming each group accordingly. At this time, the relevant factors were examined by looking at the group mean and standard error, and significant differences between groups were investigated using the DCON command through a difference analysis. The study then examined the transition patterns between latent groups at Time 1 and Time 2. The variances of the LPA indicators for the two time points were estimated with no constraints imposed.

Although an attempt was made to explore the influence of age, gender, work experience, and shift pattern on group transitions, the estimation of models including these factors proved infeasible due to the complexity, preventing the identification of the global maximum of the likelihood function. Consequently, the analysis of these factors was not possible.

Therefore, to meet the study’s objectives of understanding group-specific transition features, an alternative approach was taken. Initially, the groups were classified based on the transition characteristics from Time 1 to Time 2. Then, descriptive statistics and one-way ANOVA were conducted for each group, examining gender, age, work experience, and shift pattern. Additionally, Multinomial Logistic Regression was employed to explore the impact of age, gender, work experience, and shift pattern. The transition characteristics of each group were treated as independent variables in the ANOVA and as dependent variables in the logistic regression analysis. Gender and shift work were treated as binary variables, while age and length of service were analyzed as continuous variables.

3. Results

3.1. Descriptive statistics and confirmatory factor analysis

The mean (standard deviation, range) for each factor included in the analysis is as follows: PTSD sub-factors ranged from 1.40 to 2.40 (2.54∼3.88, 14∼21), PTG sub-factors ranged from 2.67 to 3.48 (5.44∼6.38, 36∼42), dissociative symptom was 14.70 (23.92, 164), intrusive rumination was 3.95 (6.00, 30), deliberate rumination was 4.87 (6.48, 27), depression was 10.30.43 (8.72, 49), suicidal ideation was 2.15 (3.11, 25), emotion awareness was 39.66 (6.27, 28), emotion processing and expression were 43.68 (10.70, 48), mentalization was 62.30 (13.22, 100), and psychological acceptance was 47.03 (5.95, 33). Before performing LPA, skewness and kurtosis were examined to assume normal distribution. The results did not exceed the absolute values of 3 and 10, respectively, therefore the analysis proceeded (Kline, 2005). The CFA results confirmed that all factors were adequately represented, with factor loadings ranging from β = .50 to .98, standard errors (SE) from.031 to .237, and critical ratios (C.R.) from 5.08 to 31.23 (p < .001). Additionally, the Tucker-Lewis Fit Index (TLI) was .94, the Comparative Fit Index (CFI) was .96, and the Root Mean Square Error of Approximation (RMSEA) was .058. According to the fit index criteria established by Browne and Cudeck (1993), the analysis was deemed suitable.

3.2. LPA according to PTSD and PTG at Time 2

The fit indices for each latent profile model in the LPA are presented in Table 4. Upon examining the fit, class proportions, and interpretability while incrementing the number of latent groups from 2 to 6, both BIC and saBIC showed a decreasing trend as the number of groups increased, and the BLRT was significant for all groups. Beyond 6 groups, a distinctly meaningful interpretation for the groups was not feasible. The 3-group model exhibited the most substantial decrease in both BIC and saBIC. However, the 5-group model was considered the most appropriate, as it structurally resembled the Time 1 model (Figure 1) and provided clear interpretability for both PTG and PTSD. Table 5 presents the means, standard deviations, and standard errors for each indicator of PTSD and PTG in the 5-group model. The distribution of subfactors at Time 2 is illustrated in Figure 2.

Table 4.

Fit indices by latent class model.

Model BIC saBIC LMR BLRT Proportion by class
2 10501.276 10441.002 * *** 88.8 11.2        
3 10024.187 9941.707 0.06 *** 80.1 15.8 4.1      
4 9754.542 9649.856 0.08 *** 75.8 13.8 6.9 3.5    
5 9532.841 9405.950 * *** 62.9 13.5 13.3 6.9 3.5  
6 9411.939 9262.841 0.08 *** 61.5 13.3 13.1 6.9 3.5 1.7
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Note. BIC: Bayesian Information Criterion; saBIC: the adjusted BIC; LMR: Lo-Mendell-Rubin likelihood ratio test; BLRT: Bootstrapped Likelihood Ratio Test.

*p < .05; **p < .01;***p < .001.

Figure 1.

Figure 1.

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Latent class classification at Time 1.

Note. PTSD: posttraumatic stress disorder.

Table 5.

Means, standard deviations, and standard errors by indicator for the Time 2 5-group model LPA.

Factor Class1 (13.5%) Class2 (13.3%) Class3 (3.5%) Class4 (62.9%) Class5 (6.9%)
Growth Partial Struggle PTSD Resilience or Insensitive Struggle
M(SE) M(SE) M(SE) M(SE) M(SE)
Intrusion 0.73(0.21) 3.98(0.30) 12.42(0.74) 1.02(0.11) 6.65(0.43)
Sleep & numbness 0.58(0.17) 3.54(0.27) 10.50(0.55) 0.76(0.07) 5.60(0.45)
Hyperarousal 0.06(0.36) 2.64(0.25) 14.00(0.59) 0.12(0.03) 6.75(0.36)
Avoidance 0.93(0.33) 4.69(0.44) 13.92(0.76) 0.12(0.03) 9.63(0.99)
Enhanced personal resources 14.90(0.79) 2.54(0.90) −2.08(1.32) 1.40(0.29) 5.03(1.36)
Enhanced social resources 12.09(0.76) 2.05(0.81) −3.08(1.39) 0.87(0.22) 5.04(1.15)
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Figure 2.

Figure 2.

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Latent class classification at Time 2.

Note. PTSD: posttraumatic stress disorder.

3.3. Differences in relevant factors among latent groups at Time 2

Table 6 displays the outcomes of the analysis of variance concerning the naming of groups based on their characteristics.

Table 6.

Analysis of differences in PTSD, mental health, and growth-related factors according to latent groups at Time 2.

Factor Time 2 Overall test Difference between class
Growth (class1) Partial Struggle (class2) PTSD (class3) Resilience or Insensitive (class4) Struggle (class5)
M(SE) M(SE) M(SE) M(SE) M(SE)
Dissociation 9.39(1.82) 21.65(3.37) 76.17(15.06) 8.09(0.74) 37.36(7.23) 215.35*** 1, 4<2, 5<3
Intrusive rumination 1.58(0.45) 6.57(0.85) 19.00(1.67) 1.86(0.23) 13.44(1.21) 129.74*** 1, 4<2<5<3
Deliberate rumination 5.46(0.93) 7.81(1.02) 16.42(1.62) 2.61(0.30) 12.13(1.41) 50.86*** 4<1, 2<5<3
Depression 8.19(0.96) 11.46(1.09) 32.58(2.07) 7.93(0.44) 21.24(1.64) 193.91*** 1<4<2<5<3
Suicidal ideation 1.25(0.23) 2.00(0.36) 7.75(2.14) 1.80(0.15) 4.51(1.05) 19.96** 1, 2, 4<3, 5
Emotion awareness 41.86(0.90) 38.38(0.87) 32.42(0.96) 40.29(0.41) 35.83(1.06) 76.47*** 2, 3, 5< 1, 4
Emotion processing and expression 50.28(1.40) 45.22(1.45) 36.25(3.36) 42.68(0.70) 41.46(2.19) 31.18*** 2, 3, 4, 5<1
Mentalization 63.92(1.82) 66.11(1.89) 71.00(4.67) 60.41(0.88) 64.57(2.54) 13.79** 4<1, 2, 3, 5
Acceptance 50.15(0.60) 46.26(0.79) 35.50(2.33) 47.84(0.32) 41.30(1.48) 66.25*** 3, 5<2, 4<1
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Note. **p < .01;***p < .001.

Group 1, constituting 13.5% of the total, earned the label ‘Growth Group’ due to its highest PTG level and lowest PTSD level, indicating an adaptive experience of PTG. Group 1 exhibited the lowest levels of dissociation, intrusion, and depression, which are PTSD-related factors, and higher levels of emotion-based response and acceptance, which are PTG-related factors.

Group 2, accounting for 13.3% of the total, was named the ‘Partial Struggle Group’ as it experienced a moderate level of both PTSD and PTG, indicating a struggle to resolve some aspects of trauma. Group 2 displayed somewhat lower PTG-related factors than the ‘Growth Group,’ but lower PTSD-related factors compared to Groups 3 and 5, suggesting a mild struggle.

Group 3, comprising 3.5%, had the highest level of PTSD and a negative experience of PTG, leading to its classification as the ‘PTSD Group.’ Group 3 exhibited the highest levels of dissociation, intrusive rumination, deliberate rumination, depression, and suicidal thoughts, while acceptance was low, indicating a very high level of distress from the traumatic event.

Group 4, representing 62.9%, showed minimal experience with both PTG and PTSD. As the exact cause of this phenomenon could not be determined in this study, Group 4 was named the ‘Resilience or Insensitive Group.’ Group 4 had low PTSD-related factors and PTG-related factors that were either lower or similar to those of Group 1, suggesting less impact from the traumatic event.

Lastly, Group 5, comprising 6.9%, demonstrated a high PTSD level but experienced average growth, indicating a struggle to move beyond PTSD. Hence, it was named the ‘Struggle Group.’ Group 5 showed lower levels of dissociation, rumination, and depression than Group 3 but still high; it also showed an average level of PTG but low in acceptance and emotion-based response, indicating simultaneous experiences of pain from trauma and growth.

3.4. Transition probability

Table 7 outlines the likelihood of latent group types at Time 1 transitioning to latent group types at Time 2. Most groups with similar characteristics showed the highest transition probabilities, and notably, there was a 0% probability of transitioning from the ‘Struggle’ group at Time 1 to the ‘Growth’ group at Time 2.

Table 7.

Transition probabilities of latent groups by PTSD and PTG at Time 1 & 2.

Factor Time 2
Growth Resilience or Insensitive Partial Struggle Struggle PTSD
Time 1 Growth 45.4 34.8 12.5 5.4 1.9
Resilience or Insensitive 6.3 77.3 11.2 3.5 1.8
Partial Struggle 7.2 39.0 26.9 19.5 7.4
Struggle 0.0 45.8 7.6 23.7 22.9
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3.5. Group classification and descriptive statistics based on transition pattern

Instead of investigating factors affecting the transition between latent groups, we first classified groups based on transition (from Time 1 to Time 2) features, and then conducted descriptive statistics and one-way ANOVA. The groups were identified as ‘continued distress,’ ‘escalated distress,’ ‘growth,’ and ‘adaptation.’ The classification criteria are detailed in Table 8.

Table 8.

Group classification based on transition features.

  Transition
Group Time 1 → Time 2
Continued Distress (N = 18) Partial Struggle → Partial Struggle
Struggle → Struggle
Growth (N = 71) Partial Struggle → Growth
Struggle → Partial Struggle
Partial Struggle → Resilience or Insensitive
Resilience or Insensitive → Growth
Struggle → Resilience or Insensitive
Growth → Growth
Adaptation (N = 193) Growth → Resilience or Insensitive
Resilience or Insensitive → Resilience or Insensitive
Escalated Distress (N = 64) Partial Struggle → PTSD
Partial Struggle → Struggle
Growth → PTSD
Growth → Partial Struggle
Growth → Struggle
Struggle → PTSD
Resilience or Insensitive → PTSD
Resilience or Insensitive → Partial Struggle
Resilience or Insensitive → Struggle
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The results of group-specific descriptive statistics and ANOVA are presented in Table 9. It appeared that there were no significant differences among the groups for all variables examined.

Table 9.

Means, standard deviations, and analysis of variance results between groups.

  Continued Distress Growth Escalated Distress Adaptation F Scheffe
M(SD) M(SD) M(SD) M(SD)
Gender 1.00(0.00) 1.06(0.23) 1.03(0.18) 1.07(0.25) 0.77 –
Age 2.72(0.75) 2.51(0.94) 2.63(0.98) 2.61(0.92) 0.36 –
Years of service 3.17(1.58) 2.79(1.72) 3.06(1.88) 2.90(1.73) 0.40 –
Shift pattern 1.78(0.43) 1.77(0.45) 1.75(0.44) 1.77(0.44) 0.42 –
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Note. Gender: 1 = Male, 2 = Female; Age: 1 = 18–29, 2 = 30–39, 3 = 40–49, 4 = 50–59; Years of service: 1 = 0∼5, 2 = 6∼10, 3 = 11∼15, 4 = 16∼20, 5 = 21∼25, 6 = 26∼30, 7 = 31∼35, 8 = 36 and over; Shift pattern: 1 = Fixed, 2 = Rotating. *p < .05, **p < .01.

3.6. Multinomial logistic regression analysis

We conducted a multinomial logistic regression analysis to examine the influence of gender, age, work experience, and shift pattern on group classification. The significant results are presented in Table 10. When using the ‘escalated distress’ group as a reference, significant age differences were found in the ‘growth’ group (B = −0.96, p < .05) and the ‘adaptation’ group (B = −0.73, p < .05). This suggests that a lower age increases the likelihood of an individual belonging to either the ‘growth’ group or the ‘adaptation’ group as opposed to the ‘escalated distress’ group.

Table 10.

Results of multinomial logistic regression analysis.

  Escalated Distress group
Growth group Adaptation group
  B SE Exp(B) OR(95% CI) B SE Exp(B) OR(95% CI)
Lower limit Upper limit Lower limit Upper limit
Gender 0.37 0.90 1.44 0.25 8.34 0.63 0.78 1.87 0.40 8.66
Age −0.96* 0.39 0.38 0.18 0.82 −0.73* 0.33 0.48 0.25 0.93
Years of service 0.35 0.21 1.43 0.95 2.13 0.28 0.17 1.32 0.95 1.85
Shift pattern 0.15 0.40 1.16 0.53 2.54 0.95 0.33 1.10 0.57 2.11
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Note. Gender: 1 = Male, 2 = Female; Age: 1 = 18–29, 2 = 30–39, 3 = 40–49, 4 = 50–59 5 = 60∼70; Years of service: 1 = 0∼5, 2 = 6∼10, 3 = 11∼15, 4 = 16∼20, 5 = 21∼25, 6 = 26∼30, 7 = 31∼35, 8 = 36 and over; Shift pattern: 1 = Fixed, 2 = Rotating. *p < .05.

4. Discussion

This study used LTA to examine how the levels of PTSD and PTG among firefighters change over time. The LPA results at Time 2 revealed the categorization of latent profiles into five groups: ‘Growth’ group (13.5%),’ ‘Resilience or Insensitive’ group (62.9%), ‘Struggle’ group (6.9%), ‘Partial Struggle’ group (13.3%), and ‘PTSD’ group (3.5%). Except the addition of the PTSD group, characterized by the highest PTSD symptoms and negative changes in PTG variables, this classification replicated the 4-group composition observed at Time 1 (Shin et al., 2023). These results indicate that viewing the experiences of individuals after trauma through a dichotomy of high and low PTSD symptoms may overly simplify the post-traumatic experiences. Notably, the PTSD levels in the ‘PTSD’ group were comparable to or slightly higher than those in the ‘Struggle’ group at Time 1. However, a crucial distinction was made based on whether individuals were undergoing negative changes or experiencing a concurrent positive growth. Upon examining the numerical values related to the ‘Struggle’ group at Time 1 (Shin et al., 2023) and those pertaining to the ‘PTSD’ group at Time 2, several distinctions become apparent. The ‘PTSD’ group displays increased levels of dissociation, rumination, depression, and suicidal ideation when compared to the ‘Struggle’ group, while also exhibiting decreased levels of factors traditionally associated with PTG. These findings illustrate the complex characteristics of trauma as delineated by Calhoun and Tedeschi (2006). Additionally, the results indicate that individuals experiencing a comparable degree of distress may follow divergent paths: some may engage in a struggle that catalyzes growth and development (Fletcher et al., 2023); others may undergo what is described as deceptive self-enhancement (Maercker & Zoellner, 2004) or experience a negative growth trajectory (Boals & Schuler, 2018). Thus, the present study suggests that PTSD symptom levels alone are insufficient to fully comprehend the scope of an individual’s traumatic experience. This underscores that even with similar PTSD levels, the subjective perceptions of how individuals navigate trauma can significantly differ.

Next, LTA results across time points revealed that the ‘Growth’ group and ‘Resilience or Insensitive’ group showed a strong tendency to consistently remain in the same group. Specifically, the ‘Resilience or Insensitive’ group showed remarkable stability, with 77.3% maintaining their status unchanged. In a study examining the relationship between PTG and PTSD in earthquake survivors through LTA, Chen and Wu (2017) found that individuals were most likely to remain within their initial group. Notably, the ‘Resilient’ group had the highest probability of consistency, with a retention rate of 88%. This finding supports research indicating that resilience reflects a steady trait, unaffected by changes in individual characteristics, demographics, and social environments (Bonanno et al., 2011). Contrary to the findings of Chen and Wu (2017), our study indicates that both the ‘Struggle’ group and the ‘Partial Struggle’ group exhibited the highest likelihood of transitioning to the ‘Resilience or Insensitivity’ category. This phenomenon implies that constructive changes occurring at the onset of an incident can play a crucial role in bolstering resilience. Furthermore, the findings reveal that for firefighters, a decrease in psychological pathologies is more indicative of an increase in resilience rather than growth. In the Korean context, there is a consistent expansion of efforts to survey, provide counselling, implement stress resilience enhancement programmes, and offer support for firefighters classified as high-risk groups for conditions such as PTSD, depression, and sleep disorders (Korean National Fire Agency, 2023). This ongoing initiative seems to have a positive impact on increasing and sustaining resilience among firefighters. This indicates that although individuals may initially become vulnerable due to the circumstances of their experience, the utilization of various resources appears to support the recovery of resilience without challenging their existing schemas and core beliefs, thereby preventing progression towards growth (Calhoun & Tedeschi, 2012). Considering the dynamic nature of resilience evolving through the interplay between individuals and their environment (Laureys & Easton, 2020), continuous support in this aspect emerges as crucial.

Additionally, the analysis revealed that the ‘Struggle’ group had a 0% probability of transitioning to the ‘Growth’ group, while displaying the highest likelihood of transitioning to the ‘Resilience or Insensitive’ group. The interpretation here hinges on the idea that threatening events, while causing psychological distress, may not lead to growth if they fall within a manageable and predictable range without shattering one’s beliefs and cognitive frameworks (Calhoun & Tedeschi, 1999, 2012). Trauma, in this context, occurs when an event is unexpectedly shocking, challenging to control, and difficult to cope with (Weaver & Clum, 1995). Furthermore, the fact that there was no transition to the ‘Growth’ group but a shift to the ‘Resilience or Insensitive’ group could be interpreted as individuals in this group successfully alleviating psychological distress during duty-related events using their available resources, without undergoing a growth experience.

In contrast, the ‘Struggle’ group exhibited the highest probability of transitioning to the ‘PTSD’ group. This implies that individuals who moved from the ‘Struggle’ group at Time 1 to the ‘PTSD’ group at Time 2 initially experienced both growth and distress but are now encountering an escalation in distress and negative changes. While PTSD symptoms resulting from traumatic events are crucial predictors of growth (Cann et al., 2011; Michl et al., 2013), sustaining growth may become challenging if ongoing distress fails to foster positive mood changes, as suggested by research (Dekel et al., 2012; Zoellner & Maercker, 2006). Another study proposes that post-traumatic growth may involve a deceptive element, generating self-enhancing illusions leading individuals to report growth as a coping strategy, even when actual growth may not occur (Maercker & Zoellner, 2004). The worsening of symptoms over time, a pattern noted in other studies (Adams et al., 2019; Goodman-Williams et al., 2022; Meijer et al., 2019), suggests an experience of severe symptoms to the point where the perception of ‘things are changing’ is turning negative. The ‘PTSD’ group, characterized by high levels of deliberate rumination in an attempt to move beyond traumatic experiences, represents a group facing worsening symptoms. Therefore, future research should delve into factors predicting differences among individuals transitioning to the ‘Resilience or Insensitive’ group, such as cumulative trauma experiences and the types of traumatic events encountered. This exploration could yield valuable insights for implementing practical measures to enhance firefighters’ job adaptation, return to work, and resilience.

To explore differences and impacts of gender, age, work experience, and shift patterns at Time 2 among groups classified according to transition patterns, both ANOVA and logistic regression analyses were conducted. While the ANOVA results were not significant, the logistic regression analysis, which included all predictors and controlled for the influence of other variables, yielded noteworthy findings. It demonstrated that younger individuals have a higher probability of being in the ‘adaptation’ or ‘growth’ groups as opposed to the ‘escalated distress’ group. The finding that older individuals are more likely to belong to a group experiencing pain compared to the general group can be understood in the context of studies such as Cao et al. (2018). Examining prior studies, including research on age-related increases in PTSD levels among children and adolescents (Chen & Wu, 2017; Fan et al., 2015), sheds light on the observation that deliberate rumination efforts to comprehend events and a preference for emotion-based response strategies tend to strengthen with age (Folkman et al., 1987; Shakespeare-Finch & Lurie-Beck, 2014). While deliberate rumination is a crucial aspect of the process leading to post-traumatic growth, it’s important to note that it can also contribute to higher PTSD symptoms during the attempt to understand events (Pineles et al., 2017). Despite some studies indicating that younger firefighters are more susceptible to psychological distress (Goh et al., 2021), this vulnerability may be contingent on job conditions. Generally, younger individuals are more inclined to seek help in crisis situations (Jeong & Kim, 2021), increasing their likelihood of benefiting from various support mechanisms provided by firefighting agencies. This insight suggests that being younger could be an asset in coping flexibly with threatening situations (Amiri et al., 2023). While older age is typically associated with longer work experience, the non-significant impact of work experience in this study implies that individual characteristics may play a more substantial role in maintaining stability than job-related factors.

In the Time 1 LPA study conducted by Shin et al. (2023), age did not emerge as a significant factor, whereas work-related aspects such as years of service and shift pattern were identified as significant. However, in our current study, the lack of significance for years of service and shift pattern can be attributed to the intricacies of model estimation during the LTA analysis, preventing an exhaustive exploration of influencing factors. Additionally, the dynamic nature of shift pattern, contingent on job responsibilities, complicates establishing a direct link with transition features. In our investigation, the shift pattern was examined at the time of the survey, and the 20-month interval between Time 1 and Time 2 posed challenges in understanding how alterations in the shift pattern might impact transition patterns. However, when considering the results of the Time 1 study collectively, it appears that shift work creates an environment that renders firefighters more susceptible to experiencing PTSD symptoms. Moreover, age seems play a role in shaping the long-term maintenance, promotion, or persistence of a psychologically healthy state or PTSD symptoms. To delve deeper into these processes and gain a clearer understanding, further in-depth research, incorporating a larger sample size for comparing groups with unchanged and changed shift pattern or enabling more complex model estimation, is warranted.

5. Clinical implications

This study provides important clinical insights for firefighters who are frequently exposed to traumatic events. First, the findings offer valuable information for determining tailored intervention strategies for firefighters. It emphasizes the need to differentiate the direction of interventions based on whether the perceived impact of the traumatic event is positive or negative, even if individuals exhibit similar levels of PTSD symptoms. If the individual perceives the impact as positive, they may be more amenable to change. Conversely, if the impact is perceived negatively, it becomes essential to identify factors that may exacerbate distress over time post-trauma (Adams et al., 2019), or the collapse of self-enhancing illusions (Maercker & Zoellner, 2004), and consider strategies that promote a positive shift.

Secondly, the study underscores the importance of considering the firefighter’s age, in addition to making adjustments to work patterns (Shin et al., 2023), in efforts to alleviate the distress associated with traumatic events. The influence of age over length of service in impacting group classifications suggests a greater need to focus on personal characteristics than on traits associated with tenure. Features common in older individuals, such as reduced help-seeking behaviour (Jeong & Kim, 2021) and rigid thinking (Amiri et al., 2023), may elevate the risk after a traumatic event. Therefore, interventions that account for these general traits may facilitate the shift from distress to growth or resilience.

6. Limitations

The limitations of this study and suggestions for future research are as follows. First, during the data collection for longitudinal analysis, the sample size comprised 483 individuals at Time 1 and 346 individuals at Time 2. Unfortunately, this limited sample size hindered our ability to analyze the transition factors in LTA, which aimed to explore 20 different transition patterns across 4 groups at Time 1 and 5 groups at Time 2. Additionally, the ‘PTSD group’ at Time 2 had only 13 individuals (3.5%). The complexity of the data also introduced methodological constraints, making it impractical to analyze latent transitions and the effects of independent variables within a unified model, potentially risking family-wise type I errors. Despite the convention in LPA studies which typically recommend a minimum group ratio of 4% to 5%, we chose to proceed with the analysis. This decision highlights our commitment to accurately reporting specific experiences, regardless of the group sizes involved. We also considered that the lifetime prevalence rate of PTSD is 1.5% (Korean National Center for Mental Health, 2022). This situation diverges from the commonly proposed 3-step approach (Ryoo et al., 2018), and challenges such as a small sample size may complicate predictions in complex latent transition models. These challenges can be mitigated by increasing the sample size; therefore, future studies should ensure a sufficient number of participants, such as firefighter panels, are available for analysis.

Second, the results revealed that, apart from age, the influencing factors based on transition features were not significant. This contrasts with the findings of a cross-sectional study at Time 1 (Shin et al., 2023), where shift pattern emerged as a significant factor influencing the relationship between PTSD and PTG. Given this discrepancy, future research could conduct more nuanced investigations, comparing groups with unchanged shift pattern and those with changed shift pattern. A qualitative analysis of experiences of change after traumatic events could also be pursued.

Finally, the ‘Resilience and Insensitivity’ group exhibited the highest proportion and transition probability, underscoring the pivotal role of resilience in job adaptation and well-being. As this study did not delve into a detailed analysis of factors contributing to increased resilience, future research could explore individual characteristics like social support (Sattler et al., 2014) and engagement in physical leisure activities (Lim et al., 2020) to better understand resilience among firefighters.

7. Conclusions

This research marks the inaugural attempt to longitudinally investigate the interplay between PTSD and PTG among firefighters. While the majority of firefighters maintained good mental health through resilience, a notable portion of the sample (22.7%), specifically those in groups characterized by continued or escalated distress in transition features, continued to report stress from traumatic events. This indicates ongoing exposure to risks such as job maladaptation, severe depression due to PTSD, and suicidal thoughts. Therefore, in addition to supporting resilience, there is a critical need for continued attention to and research on job characteristics and individual traits that contribute to elevated levels of PTSD symptoms. Notably, the higher likelihood of distress in older age groups, particularly those aged 40 and above, suggests a tendency to avoid seeking professional help, as indicated in previous research (An & Seo, 2017). This underscores the need for customized intervention strategies specifically designed for older firefighters, distinct from those applicable to their younger counterparts. In line with the suggestions by Jeong and Kim (2021), broadening the scope of counselling could be beneficial, enabling older firefighters to access interventions addressing practical issues such as family, interpersonal relationships, job satisfaction, and efficiency.

Funding Statement

This work was supported by 4·16 Foundation and Community Chest of Korea.

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Adams, S. W., Allwood, M. A., & Bowler, R. M. (2019). Posttraumatic stress trajectories in World Trade Center tower survivors: Hyperarousal and emotional numbing predict symptom change. Journal of Traumatic Stress, 32(1), 67–77. 10.1002/jts.22357 [DOI] [PubMed] [Google Scholar]
  2. Ahn, H., Joo, H., & Sim, K. (2013). Validation of the event related rumination inventory in a Korean population. Cognitive Behavior Therapy in Korea, 13(1), 149–172. [Google Scholar]
  3. Åkerstedt, T., & Kecklund, G. (2017). What work schedule characteristics constitute a problem to the individual? A representative study of Swedish shift workers. Applied Ergonomics, 59, 320–325. 10.1016/j.apergo.2016.09.007 [DOI] [PubMed] [Google Scholar]
  4. Amiri, H., Rezapour, M., Nakhaee, N., Nekoei-Moghadam, M., Nasab, S. H. M., Nezhad, M. S., & Jahani, Y. (2023). Latent profiles of posttraumatic growth: 17 years after the Bam earthquake in Iran. Disaster Medicine and Public Health Preparedness, 17, e127. 10.1017/dmp.2022.36 [DOI] [PubMed] [Google Scholar]
  5. An, S. J., & Seo, Y. S. (2017). Professional help seeking in middle-aged men: The moderated mediating effects of mid-life crisis and counseling environment on the links between self-stigma, gender role conflict, attitudes toward counseling, and intentions to seek counseling. The Korean Journal of Counseling and Psychotherapy, 29(3), 629–657. 10.23844/kjcp.2017.08.29.3.629 [DOI] [Google Scholar]
  6. Beck, A. T., Kovacs, M., & Weissman, A. (1979). Assessment of suicidal intention: The Scale for Suicide Ideation. Journal of Consulting and Clinical Psychology, 47(2), 343–352. 10.1037/0022-006X.47.2.343 [DOI] [PubMed] [Google Scholar]
  7. Berninger, A., Webber, M. P., Niles, J. K., Gustave, J., Lee, R., Cohen, H. W., Kelly, K., Corrigan, M., & Prezant, D. J. (2010). Longitudinal study of probable post-traumatic stress disorder in firefighters exposed to the world trade center disaster. American Journal of Industrial Medicine, 53(12), 1177–1185. 10.1002/ajim.20894 [DOI] [PubMed] [Google Scholar]
  8. Bernstein, E. M., & Putnam, F. W. (1986). Development, reliability, and validity of a dissociation scale. The Journal of Nervous and Mental Disease, 174(12), 727–735. 10.1097/00005053-198612000-00004 [DOI] [PubMed] [Google Scholar]
  9. Boals, A., & Schuler, K. L. (2018). Reducing reports of illusory posttraumatic growth: A revised version of the stress-related growth scale (SRGS-R). Psychological Trauma: Theory, Research, Practice, and Policy, 10(2), 190–198. 10.1037/tra0000267 [DOI] [PubMed] [Google Scholar]
  10. Boffa, J. W., Stanley, I. H., Hom, M. A., Norr, A. M., Joiner, T. E., & Schmidt, N. B. (2017). PTSD symptoms and suicidal thoughts and behaviors among firefighters. Journal of Psychiatric Research, 84, 277–283. 10.1016/j.jpsychires.2016.10.014 [DOI] [PubMed] [Google Scholar]
  11. Bonanno, G. A., Westphal, M., & Mancini, A. D. (2011). Resilience to loss and potential trauma. Annual Review of Clinical Psychology, 7(1), 511–535. 10.1146/annurev-clinpsy-032210-104526 [DOI] [PubMed] [Google Scholar]
  12. Bond, F. W., Hayes, S. C., Bear, R. A., Carpenter, K. M., Guenole, N., Orcutt, H. K., Waltz, T., & Zettle, R. D. (2011). Preliminary psychometric properties of the acceptance and action questionnaire-II: A revised measure of psychological inflexibility and experiential avoidance. Behavior Therapy, 42(4), 676–688. 10.1016/j.beth.2011.03.007 [DOI] [PubMed] [Google Scholar]
  13. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In Bollen K. A. & Long J. S. (Eds.), Testing structural equation models (pp. 136–162). Sage. [Google Scholar]
  14. Calhoun, L. G., & Tedeschi, R. G. (1999). Facilitating posttraumatic growth: A clinician’s guide. Routledge. [Google Scholar]
  15. Calhoun, L. G., & Tedeschi, R. G. (2006). The foundations of posttraumatic growth: An expanded framework. In Calhoun L. G. & Tedeschi R. G. (Eds.), Handbook of posttraumatic growth: Research & practice (pp. 3–23). Erlbaum. [Google Scholar]
  16. Calhoun, L. G., & Tedeschi, R. G. (2012). Posttraumatic growth in clinical practice. Routledge. [Google Scholar]
  17. Cann, A., Calhoun, L. G., Tedeschi, R. G., Triplett, K. N., Vishnevsky, T., & Lindstrom, C. M. (2011). Assessing posttraumatic cognitive processes: The Event Related Rumination Inventory. Anxiety, Stress & Coping, 24(2), 137–156. 10.1080/10615806.2010.529901 [DOI] [PubMed] [Google Scholar]
  18. Cao, C., Wang, L., Wu, J., Li, G., Fang, R., Cao, X., Liu, P., Luo, S., Hall, B. J., & Elhai, J. D. (2018). Patterns of posttraumatic stress disorder symptoms and posttraumatic growth in an epidemiological sample of Chinese earthquake survivors: A latent profile analysis. Frontiers in Psychology, 9, 1–8. 10.3389/fpsyg.2018.00001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Chen, C., & Tang, S. (2021). Profiles of grief, post-traumatic stress, and post-traumatic growth among people bereaved due to COVID-19. European Journal of Psychotraumatology, 12(1), 1947563. 10.1080/20008198.2021.1947563 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Chen, J., & Wu, X. (2017). Post-traumatic stress symptoms and post-traumatic growth among children and adolescents following an earthquake: A latent profile analysis. Child and Adolescent Mental Health, 22(1), 23–29. 10.1111/camh.12175 [DOI] [PubMed] [Google Scholar]
  21. Choi, H. K. (2010). Effects of traumatic events on posttraumatic stress disorder, burnout, physical symptoms, and social and occupational functions in Korean fire fighters [Unpublished doctoral dissertation]. Ajou University. [Google Scholar]
  22. Chon, K. K., Choi, S. C., & Yang, B. C. (2001). Integrated adaptation of CES-D in Korea. Korean Journal of Health Psychology, 6(1), 59–76. [Google Scholar]
  23. Dekel, S., Ein-Dor, T., & Solomon, Z. (2012). Posttraumatic growth and posttraumatic distress: A longitudinal study. Psychological Trauma: Theory, Research, Practice, and Policy, 4(1), 94–101. 10.1037/a0021865 [DOI] [Google Scholar]
  24. Del Ben, K. S., Scotti, J. R., Chen, Y. C., & Fortson, B. L. (2006). Prevalence of posttraumatic stress disorder symptoms in fire fighters. Work & Stress, 20(1), 37–48. 10.1080/02678370600679512 [DOI] [Google Scholar]
  25. Eun, H., Kwon, T., Lee, S., Kim, T., Choi, M., & Cho, S. J. (2005). A study on reliability and validity of Korean version of Impact of Event Scale-Revised. Journal of the Korean Neuropsychiatric Association, 44(3), 303–310. [Google Scholar]
  26. Fan, F., Long, K., Zhou, Y., Zheng, Y., & Liu, X. (2015). Longitudinal trajectories of post-traumatic stress disorder symptoms among adolescents after the Wenchuan earthquake in China. Psychological Medicine, 45(13), 2885–2896. 10.1017/S0033291715000884 [DOI] [PubMed] [Google Scholar]
  27. Fletcher, S., Mitchell, S., Curran, D., Armour, C., & Hanna, D. (2023). Empirically derived patterns of posttraumatic stress and growth: A systematic review. Trauma, Violence, & Abuse, 24(5), 3132–3150. 10.1177/15248380221129580 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Folkman, S., Lazarus, R. S., Pimley, S., & Novacek, J. (1987). Age differences in stress and coping processes. Psychology and Aging, 2(2), 171–184. 10.1037/0882-7974.2.2.171 [DOI] [PubMed] [Google Scholar]
  29. Fonagy, P. (1991). Thinking about thinking: Some clinical and theoretical considerations in the treatment of a borderline patient. International Journal of Psycho-Analysis, 72(4), 639–656. [PubMed] [Google Scholar]
  30. Germain, A. (2013). Sleep disturbances as the hallmark of PTSD: Where are we now? American Journal of Psychiatry, 170(4), 372–382. 10.1176/appi.ajp.2012.12040432 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Goh, K. K., Jou, S., Lu, M. L., Yeh, L. C., Kao, Y. F., Liu, C. M., & Kan, B. L. (2021). Younger, more senior, and most vulnerable? Interaction effects of age and job seniority on psychological distress and quality of life among firefighters. Psychological Trauma: Theory, Research, Practice, and Policy, 13(1), 56–65. 10.1037/tra0000662 [DOI] [PubMed] [Google Scholar]
  32. Goodman-Williams, R., Clark, S. L., Campbell, R., & Ullman, S. E. (2022). Longitudinal patterns of posttraumatic stress disorder symptoms among sexual assault survivors: A latent transition analysis. Psychological Trauma: Theory, Research, Practice, and Policy, 16(4), 586–595. Advance online publication. 10.1037/tra0001376 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Greenberg, L. S., & Safran, J. D. (1987). Emotion in psychotherapy. Guilford Press. [Google Scholar]
  34. Hass, J. R. (1994). The elusive nature of acceptance. In Hayes S. C., Jacobson N. S., Follette V. M., & Dougher M. J. (Eds.), Acceptance and change: Content and context in psychotherapy (pp. 37–45). Context Press. [Google Scholar]
  35. Hayes, S. C., Strosahl, K., Wilson, K. G., Bissett, R. T., Pistirello, J., Toarmino, D., Polusny, M. A., Dykstra, T. A., Batten, S. V., Bergan, J., Stewart, S. H., Zvolensky, M. J., Eifert, G. H., Bond, F. W., Forsyth, J. P., Karekla, M., & McCurry, S. M. (2004). Measuring experiential avoidance: A preliminary test of a working model. The Psychological Record, 54(4), 553–578. 10.1007/BF03395492 [DOI] [Google Scholar]
  36. Heo, J., Choi, M., & Jin, H. (2009). Study on the reliability and validity of Korean Translated Acceptance-Action Questionnaire-II. Korean Journal of Counseling And Psychotherapy, 21(4), 861–878. [Google Scholar]
  37. Hill, K. G., White, H. R., Chung, I. J., Hawkins, J. D., & Catalano, R. F. (2000). Early adult outcomes of adolescent binge drinking: Person- and variable-centered analyses of binge drinking trajectories. Alcoholism: Clinical and Experimental Research, 24(6), 892–901. 10.1111/j.1530-0277.2000.tb02071.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Jeong, M., & Kim, D. (2021). Influence of male gender role conflict and tendency to individualism or collectivism of firefighting officials on professional help seeking attitude: For the firefighting officials in jeollanam-do. Locality and Globality: Korean Journal of Social Sciences, 45(1), 169–201. 10.33071/ssricb.45.1.202103.169 [DOI] [Google Scholar]
  39. Jin, Y., Xu, J., & Liu, D. (2014). The relationship between post traumatic stress disorder and post traumatic growth: Gender differences in PTG and PTSD subgroups. Social Psychiatry and Psychiatric Epidemiology, 49(12), 1903–1910. 10.1007/s00127-014-0865-5 [DOI] [PubMed] [Google Scholar]
  40. Jo, S., & Park, J. (2012). The relationship between job stress and alcohol-use disorders among firemen. Journal of Korean Society of Hazard Mitigation, 12(1), 133–140. 10.9798/KOSHAM.2012.12.1.133 [DOI] [Google Scholar]
  41. Joseph, S., Williams, R., & Yule, W. (1993). Changes in outlook following disaster: The preliminary development of a measure to assess positive and negative responses. Journal of Traumatic Stress, 6(2), 271–279. 10.1002/jts.2490060209 [DOI] [Google Scholar]
  42. Kang, S., & Yang, J. (2015). Preliminary validation of Emotional Approach Coping (EAC) scale: A case study of Korean military academy cadets. Korean Journal of Clinical Psychology, 34(2), 455–475. 10.15842/kjcp.2015.34.2.007 [DOI] [Google Scholar]
  43. Kim, S., & Yook, S. (2018). The influence of posttraumatic stress on suicidal ideation in firefighters: Cognitive emotion regulation as a moderator. Fire Science and Engineering, 32(2), 92–101. 10.7731/KIFSE.2018.32.2.092 [DOI] [Google Scholar]
  44. Kline, T. J. (2005). Psychological testing: A practical approach to design and evaluation. Sage publications. [Google Scholar]
  45. Korean National Center for Mental Health . (2022). National Mental Health Survey 2021 (Report No. 11-1352629-000065-01). Retrieved December 24, 2022, from https://mhs.ncmh.go.kr/research/download/12/report
  46. Korean National Fire Agency . (2018). Conducted a survey on the mental health survey of national firefighters. Retrieved February 28, 2018, from https://www.nfa.go.kr/nfa/news/pressrelease/press/?boardId=bbs_0000000000000010&mode=view&cntId=153&category=&pageIdx=3&searchCondition=all&searchKeyword=%EC%A0%95%EC%8B%A0
  47. Korean National Fire Agency . (2023). Firefighters’ mental health support project receives an increase of KRW 680 million this year. Retrieved January 8, 2023, from http://www.nfa.go.kr:8005/wv/ezpdf/customLayout.jsp?contentId=L0RBVEEvbmZhL25mYS53YXIvdXBsb2FkLy9ib2FyZC8vYmJzXzAwMDAwMDAwMDAwMDAwMTAvMjAyMzAxMDYvMjAyMzAxMDYxNzUxMDcxODE5OC5wZGY%3D
  48. Kwak, M., & Bae, J. (2017). Posttraumatic growth and related factors in firefighters. Journal of Korean Academy of Psychiatric and Mental Health Nursing, 26(2), 124–133. 10.12934/jkpmhn.2017.26.2.124 [DOI] [Google Scholar]
  49. Kwon, J. (2011). Evaluation of fit indexes to determine the number of latent classes in mixture models with categorical outcomes: Monte carlo simulation method [Unpublished doctoral dissertation]. Korea University. [Google Scholar]
  50. Kwon, J. K., & Yang, M. H. (2014). A study on the profile analysis of adolescents’ happiness. The Korean Educational Review, 20(3), 201–227. [Google Scholar]
  51. Laureys, V., & Easton, M. (2020). Resilience of firefighters exposed to potentially traumatic events: A literature review. International Journal of Emergency Services, 9(2), 217–232. 10.1108/IJES-05-2019-0021 [DOI] [Google Scholar]
  52. Lee, S. (2009). Trajectories of post-traumatic stress and its relation to post-traumatic growth among emergency personnel [Unpublished doctoral dissertation]. Ewha Womans University. [Google Scholar]
  53. Lee, S., & Lee, H. (1997). The research on the validation of the trait meta-mood scale: The domain exploration of the emotional intelligence. The Korean Journal of Social and Personality Psychology, 11(1), 95–116. [Google Scholar]
  54. Lim, G. Y., Jang, T. W., Sim, C. S., Ahn, Y. S., & Jeong, K. S. (2020). Comparison of cortisol level by shift cycle in Korean firefighters. International Journal of Environmental Research and Public Health, 17(13), 4760. 10.3390/ijerph17134760 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Maercker, A., & Zoellner, T. (2004). The Janus face of self-perceived growth: Toward a two-component model of posttraumatic growth. Psychological Inquiry, 15(1), 41–48. [Google Scholar]
  56. Martin, C. E., Tran, J. K., & Buser, S. J. (2017). Correlates of suicidality in firefighter/EMS personnel. Journal of Affective Disorders, 208, 177–183. 10.1016/j.jad.2016.08.078 [DOI] [PubMed] [Google Scholar]
  57. Meijer, L., Finkenauer, C., Tierolf, B., Lünnemann, M., & Steketee, M. (2019). Trajectories of traumatic stress reactions in children exposed to intimate partner violence. Child Abuse & Neglect, 93, 170–181. 10.1016/j.chiabu.2019.04.017 [DOI] [PubMed] [Google Scholar]
  58. Michl, L. C., McLaughlin, K. A., Shepherd, K., & Nolen-Hoeksema, S. (2013). Rumination as a mechanism linking stressful life events to symptoms of depression and anxiety: Longitudinal evidence in early adolescents and adults. Journal of Abnormal Psychology, 122(2), 339–352. 10.1037/a0031994 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Morris, M. C., Hellman, N., Abelson, J. L., & Rao, U. (2016). Cortisol, heart rate, and blood pressure as early markers of PTSD risk: A systematic review and meta-analysis. Clinical Psychology Review, 49, 79–91. 10.1016/j.cpr.2016.09.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Park, J. M., Choe, B. M., Kim, M. J., Hahn, H. M., Yoo, S. Y., Kim, H. S., & Joo, Y. H. (1995). Standardization of dissociative experiences scale: Korean version (1). The Korean Journal of Psychopathology, 4(1), 105–125. [Google Scholar]
  61. Park, K. B., & Shin, M. S. (1990). College goal and suicidal ideation among high school students. Korean Journal of Clinical Psychology, 9(1), 20–32. [Google Scholar]
  62. Park, S. M., & Chung, N. W. (2019). Development and validation of self-rated mentalization questionnaire. The Korean Journal of Counseling and Psychotherapy, 31(3), 929–965. 10.23844/kjcp.2019.08.31.3.929 [DOI] [Google Scholar]
  63. Pineles, S. L., Hall, K. A., & Rasmusson, A. M. (2017). Gender and PTSD: Different pathways to a similar phenotype. Current Opinion in Psychology, 14, 44–48. 10.1016/j.copsyc.2016.11.002 [DOI] [PubMed] [Google Scholar]
  64. Pineles, S. L., Rasmusson, A. M., Yehuda, R., Lasko, N. B., Macklin, M. L., Pitman, R. K., & Orr, S. P. (2013). Predicting emotional responses to potentially traumatic events from pre-exposure waking cortisol levels: A longitudinal study of police and firefighters. Anxiety, Stress & Coping, 26(3), 241–253. 10.1080/10615806.2012.672976 [DOI] [PubMed] [Google Scholar]
  65. Radloff, L. S. (1977). The CES-D scale: A self report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. 10.1177/014662167700100306 [DOI] [Google Scholar]
  66. Richardson, G. E. (2002). The metatheory of resilience and resiliency. Journal of Clinical Psychology, 58(3), 307–321. 10.1002/jclp.10020 [DOI] [PubMed] [Google Scholar]
  67. Ryoo, J. H., Wang, C., Swearer, S. M., Hull, M., & Shi, D. (2018). Longitudinal model building using latent transition analysis: An example using school bullying data. Frontiers in Psychology, 9, 675. 10.3389/fpsyg.2018.00675 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Salovey, P., Mayer, J. D., Goldman, S. L., Turvey, C., & Palfai, T. P. (1995). Emotional attention, clarity, and repair: Exploring emotional intelligence using the trait meta-mood scale. In Pennebaker J. W. (Ed.), Emotion, disclosure and health (pp. 125–154). American Psychological Association. [Google Scholar]
  69. Sattler, D. N., Boyd, B., & Kirsch, J. (2014). Trauma-exposed firefighters: Relationships among posttraumatic growth, posttraumatic stress, resource availability, coping and critical incident stress debriefing experience. Stress and Health, 30(5), 356–365. 10.1002/smi.2608 [DOI] [PubMed] [Google Scholar]
  70. Seo, Y., & Kim, Y. (2018). The relationship between interpersonal trauma and post-traumatic growth: The mediating effect of mentalization and emotion-based coping. The Korean Journal of Counseling and Psychotherapy, 30(3), 775–798. 10.23844/kjcp.2018.08.30.3.775 [DOI] [Google Scholar]
  71. Shakespeare-Finch, J., & Lurie-Beck, J. (2014). A meta-analytic clarification of the relationship between posttraumatic growth and symptoms of posttraumatic distress disorder. Journal of Anxiety Disorders, 28(2), 223–229. 10.1016/j.janxdis.2013.10.005 [DOI] [PubMed] [Google Scholar]
  72. Shin, Y., & Kim, Y. (2019). The effects of intrusive rumination, deliberate rumination, emotional intelligence, and active coping on post-traumatic growth. Korea Journal of Counseling, 20(3), 53–76. 10.15703/kjc.20.3.201906.53 [DOI] [Google Scholar]
  73. Shin, Y., Nam, J. K., Lee, A., & Kim, Y. (2023). Latent profile analysis of post-traumatic stress and post-traumatic growth among firefighters. European Journal of Psychotraumatology, 14(1), 1–15. 10.1080/20008066.2022.2159048 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Skeffington, P. M., Rees, C. S., Mazzucchelli, T. G., & Kane, R. T. (2016). The primary prevention of PTSD in firefighters: Preliminary results of an RCT with 12-month follow-up. PLoS One, 11(7), 1–22. 10.1371/journal.pone.0155873 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Sopp, M. R., Michael, T., Lass-Hennemann, J., Haim-Nachum, S., & Lommen, M. J. (2021). Longitudinal associations between hair cortisol, PTSD symptoms, and sleep disturbances in a sample of firefighters with duty-related trauma exposure. Psychoneuroendocrinology, 134, 105449. 10.1016/j.psyneuen.2021.105449 [DOI] [PubMed] [Google Scholar]
  76. Ssenyonga, J., Owens, V., & Olema, D. K. (2013). Posttraumatic growth, resilience, and posttraumatic stress disorder (PTSD) among refugees. Procedia – Social and Behavioral Sciences, 82, 144–148. 10.1016/j.sbspro.2013.06.238 [DOI] [Google Scholar]
  77. Stanton, A. L., Kirk, S. B., Cameron, C. L., & Danoff-Burg, S. (2000). Coping through emotional approach: Scale construction and validation. Journal of Personality and Social Psychology, 78(6), 1150–1169. 10.1037/0022-3514.78.6.1150 [DOI] [PubMed] [Google Scholar]
  78. Tedeschi, R. G., & Calhoun, L. G. (2004). Posttraumatic growth: Conceptual foundations and empirical evidence. Psychological Inquiry, 15(1), 1–18. 10.1207/s15327965pli1501_01 [DOI] [Google Scholar]
  79. VanderVeen, J. W., Gulliver, S. B., Morissette, S. B., Kruse, M. I., Kamholz, B. W., Zimering, R. T., Knight, J., & Keane, T. M. (2012). Differences in drinking patterns, occupational stress, and exposure to potentially traumatic events among firefighters: Predictors of smoking relapse. The American Journal on Addictions, 21(6), 550–554. 10.1111/j.1521-0391.2012.00282.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Vishnevsky, T., Cann, A., Calhoun, L. G., Tedeschi, R. G., & Demakis, G. J. (2010). Gender differences in self-reported posttraumatic growth: A meta-analysis. Psychology of Women Quarterly, 34(1), 110–120. 10.1111/j.1471-6402.2009.01546.x [DOI] [Google Scholar]
  81. Weaver, T. L., & Clum, G. A. (1995). Psychological distress associated with interpersonal violence: A meta-analysis. Clinical Psychology Review, 15(2), 115–140. 10.1016/0272-7358(95)00004-9 [DOI] [Google Scholar]
  82. Weiss, D. S., & Marmar, C. R. (1997). The impact of event scale-revised. In Wilson J. P. & Keane T. M. (Eds.), Assessing psychological trauma and PTSD (pp. 399–411). Guilford Press. [Google Scholar]
  83. Zalta, A. K., Gerhart, J., Hall, B. J., Rajan, K. B., Vechiu, C., Canetti, D., & Hobfoll, S. E. (2017). Self-reported posttraumatic growth predicts greater subsequent posttraumatic stress amidst war and terrorism. Anxiety, Stress, & Coping, 30(2), 176–187. 10.1080/10615806.2016.1229467 [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Zhen, R., & Zhou, X. (2022). Latent patterns of posttraumatic stress symptoms, depression, and posttraumatic growth among adolescents during the COVID-19 pandemic. Journal of Traumatic Stress, 35(1), 197–209. 10.1002/jts.22720 [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Zhou, X., Wu, X., & Zhen, R. (2018). Patterns of posttraumatic stress disorder and posttraumatic growth among adolescents after the Wenchuan earthquake in China: A latent profile analysis. Journal of Traumatic Stress, 31(1), 57–63. 10.1002/jts.22246 [DOI] [PubMed] [Google Scholar]
  86. Zhou, Y., Liang, Y., Tong, H., & Liu, Z. (2019). Patterns of posttraumatic stress disorder and posttraumatic growth among women after an earthquake: A latent profile analysis. Asian Journal of Psychiatry, 101834. 10.1016/j.ajp.2019.10.014 [DOI] [PubMed]
  87. Zoellner, T., & Maercker, A. (2006). Posttraumatic growth in clinical psychology — A critical review and introduction of a two component model. Clinical Psychology Review, 26(5), 626–653. 10.1016/j.cpr.2006.01.008 [DOI] [PubMed] [Google Scholar]

Associated Data

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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