Gender specific associations between potentially traumatic life events and mental health

ABSTRACT

Background: Research consistently shows that gender influences both the likelihood of encountering potentially traumatic events (PTEs) and their psychological consequences. Exposure to PTEs, defined as life-threatening or severely harmful situations, can accumulate over time and undermine an individual’s ability to cope with stress. Based on these assumptions, the current study examined the co-occurrence of PTEs separately in women and men and explored their specific associations with mental health.

Methods: A cross-sectional, group-level network analysis was used to empirically cluster PTEs based on their co-occurrence, in a sample of 782 women and 401 men (n = 1183). Psychological distress was measured using the Depression, Anxiety and Stress Scale 21 (DASS 21), event-related distress was assessed with the Impact of Event Scale - Revised (IES-R) and Resilience Scale 11 (RS-11).

Results: Both men and women networks exhibited similar overall connectivity, with four distinct clusters emerging from the network analysis in each sample. In women, a cluster related to assault-related trauma showed a strong connection to higher levels of depression, anxiety, and stress. Additionally, the relationships between trauma exposure and resilience in women revealed both positive and negative associations, highlighting the complexity of their interplay. In men, a cluster related to physical assault was associated with higher levels of anxiety, whereas resilience was positively associated with exposure to accidental trauma.

Discussion: Overall, the study suggests gender-related patterns, with trauma-related clusters significantly linked to mental health outcomes. Future research should aim to replicate these findings in larger samples, explore a broader range of mental health indicators, and examine longitudinal dynamics to better understand gender-specific patterns and inform targeted interventions.

HIGHLIGHTS

• The study examined how traumatic events cluster across genders to enhance the understanding of their cumulative impact on mental health and guide clinical practices.

• Four distinct clusters emerged, with women exhibiting robust associations between assault-related trauma and psychological distress, accompanied by nuanced resilience patterns. In men, a cluster related to physical assault was linked to higher anxiety levels, while resilience was positively associated with accidental trauma.

• Future research should validate these findings in larger samples using longitudinal designs and incorporating factors like coping strategies and externalizing behaviours to refine practical approaches.

1. Introduction

A robust body of research suggests that gender plays an important role in the exposure to traumatic events and their impact on mental health (Baum et al., 2014; Haering et al., 2024; Tang & Freyd, 2012). Women are generally more likely to be subjected to interpersonal trauma, such as sexual assault or domestic violence, which are associated with a heightened risk of developing anxiety disorders, depression, and post-traumatic stress disorder (PTSD; Breslau et al., 1998; Kessler et al., 2013; Tolin & Foa, 2006). In contrast, men more commonly reported to experience non-interpersonal traumatic events, such as serious accidents or natural disasters (Fossion et al., 2015; Haldane & Nickerson, 2016), which can likewise lead to substantial psychological distress, including symptoms of depression, anxiety and PTSD (Hapke et al., 2006; Warner et al., 2013). These differences highlight that gender as well as the traumatic event type are critical factors in understanding the complex interplay between trauma exposure and mental health.

Beyond differences in exposure, women consistently report higher rates of depression, anxiety, stress, and PTSD compared to men (Breslau et al., 1997; Eugene et al., 2025; Nolen-Hoeksema, 2001; Tang & Freyd, 2012). Contributing factors may include gender-related differences in neuroendocrine and autonomic stress reactivity (Carpenter et al., 2017; Hodes et al., 2024; McEwen, 2002), greater engagement in ruminative coping (Shors et al., 2017), and differential exposure to gender-based violence alongside gendered patterns of mental-health service use (Afifi, 2007; Anis-Farahwahida et al., 2024). Together, these influences may modulate stress responses and recovery processes, thereby contributing to the observed gender differences in the prevalence of depression, anxiety, stress and PTSD following potentially traumatic events (PTEs).

For a comprehensive understanding on how various traumatic events affect mental health, the criteria used to define and classify traumatic events becomes essential. According to Criterion A for trauma in DSM-5 (American Psychiatric Association, 2013), PTEs comprise incidents like serious injury, sexual violence or threatened death. In contrast to other serious life events (e.g. divorce, job loss), PTEs involve exposure to life-threatening situations or severe harm, which can evoke intense fear, horror or helplessness (Gradus & Galea, 2023; Ouagazzal & Boudoukha, 2019). Notably, trauma exposure that may lead to psychological disorders can occur in various ways – whether through direct experience, witnessing the event firsthand, learning that a close friend or relative has been affected, or encountering its aftermath in a professional context, such as emergency personnel exposed to distressing details (Lee et al., 2017; Rowe et al., 2022).

Importantly, research indicates that PTEs do not occur as isolated incidents; rather individuals are exposed to a variety of PTEs throughout their lifetime (Breslau et al., 1998). The cumulative burden of trauma can compromise an individual's capacity to effectively adapt and recover from stress, which describes a dynamic approach important for stress sensitization (Hammen, 2015). The stress sensitization hypothesis highlights that prior exposure to traumatic life events can increase a person's vulnerability, making them more reactive to subsequent stressors (Hammen et al., 2000). Repeated or chronic exposure to PTEs may therefore diminish one’s capacity to cope with new stressors. This reduction in adaptability could increase an individual’s vulnerability to a range of mental health disorders, even in the absence of PTSD.

Recognizing that the exposure to different PTEs can cumulatively impact mental health highlights the need for a comprehensive framework that clusters PTEs according to their interrelated nature. So far, studies have used various approaches to categorize traumata, often relying on clinical judgment (e.g. Benfer et al., 2024). However, these categorizations are not always empirically driven and can vary between studies (Tolchin et al., 2023). Empirically derived clusters of PTE types are rare (e.g. Contractor et al., 2020), which may lead to overseeing critical connections between different traumatic experiences and their impact on mental health.

Overall, evaluating the co-occurrence of multiple PTEs and their cumulative effects on mental health better displays the complexity of real-world trauma exposure and its subsequent impact on mental health. Drawing on these considerations, the current study used network analysis to identify clusters of PTEs in a sample of PTE-exposed women and men. Notably, data collection in the current study took place during a COVID-19 lockdown. This enabled the examination of associations between lifetime exposure to PTEs and three outcome domains: (1) acute COVID-19–related distress, (2) symptoms of depression, anxiety, and stress and (3) adaptive capacity indexed by resilience.

2. Methods

2.1. Sample

The sample was drawn from a large cross-sectional study originally conducted to investigate psychological distress during the COVID-19 pandemic among the Austrian population. Data collection took place during the second lockdown in Austria. Participants were Austrian residents exceeding the age of 16 years. Participants were recruited through a multimodal outreach strategy. A QR code linking to the anonymous online questionnaire was shared via social media (primarily Facebook), national newspapers, and targeted outreach to occupational groups. As part of the latter, printed recruitment materials including the QR code were displayed in various workplace settings, such as offices, break rooms, canteens, and lifts, to encourage participation. Gender was self-reported with three response options: women, man and diverse. A small number of participants identified as diverse (n = 6). Due to insufficient size for stable network estimation and community detection, gender-stratified analyses and group comparisons were conducted for women and men only. Of the total sample of 1473 self-identified women and men, 290 participants reported no exposure to PTEs. These individuals were excluded from the analysis. The final sample included 1183 participants (women: n = 782; men: n = 401). The study was conducted in accordance with the Declaration of Helsinki. Electronic informed consent was obtained from all participants.

2.2. Variables and instruments

The occurrence of PTEs was assessed via the Life Events Checklist for DSM, 5th edition (LEC-5; Weathers et al., 2013). It covers 17 different types of potentially traumatic events, such as natural disaster, accidents, physical or sexual assault, combat or sudden death. Item 17 (‘Any other very stressful event or experience’) was excluded from the analysis in this study, due to is ambiguous content. This operationalization aligns with prior work using the LEC-5, where trauma-type clustering was conducted with items 1-16, while item 17 was not included (Contractor et al., 2020). Each item offered six response options: happened to me, witnessed it, learned about it, part of my job, not sure, and doesn’t apply. For analysis, exposure was coded as present when happened to me, witnessed it, or part of my job was endorsed. Learned about it, not sure and doesn’t apply were coded as not exposed. Consequently, each event entered the analyses as a binary indicator of exposure.

Psychological distress was measured using the Depression, Anxiety, and Stress Scale (DASS-21; Nilges & Essau, 2015). The scale assesses severity of the core symptoms of depression, anxiety, and stress. It consists of 21 items, with three subscales. Reliability for each subscale was very good (all McDonald’s omega _women/men > .89).

Event related distress was assessed through the Impact of Event scale – revised (IES-R; Maercker & Schützwohl, 1998), referencing the outbreak of the COVID-19 pandemic as the index event. It consists of 22 items and three subscales, which examine three core symptoms of PTSD, namely intrusion, avoidance and hyperarousal. In the current study, IES-R scores reflect COVID-19-related posttraumatic stress symptoms and are not attributable to specific lifetime PTEs reported on the LEC-5. They are interpreted as event-related distress and do not capture the full range of DSM-5 PTSD symptoms. Scale reliability for all subscales was very good (all McDonald’s omega _women/men > .85).

Adaptive capacity was assessed through the Resilience Scale 11 (RS-11; Schumacher et al., 2005; Wagnild & Young, 1993). The RS-11 consists of 11 items, each rated on a scale from 1 to 7. Higher scores indicate higher trait resilience, indicated by a greater ability to adjust to stress and bounce back from challenging situations. Scale reliability was excellent (McDonald’s omega _women/men > .90).

2.3. Statistical analysis

All statistical analyses were conducted using R (version 4.3.1; R Core Team, 2023). The analysis plan prespecified gender-stratified network estimation and within-gender modelling of associations between PTE clusters and mental health outcomes.

2.4. Sociodemographic variables and PTE exposure

Chi-square tests were used to compare frequencies of PTE exposure between women and men. Independent t-test were used to compare levels of anxiety, depression and stress levels, as well as symptoms of avoidance, hyperarousal, intrusion and resilience across genders.

2.5. Network analysis

The network analysis was based on the 16 binary LEC-5 items assessing PTEs. Two separate networks were estimated for men and women. Undirected, weighted network models were estimated for each group, using the IsingFit package (vers. 0.3.1; van Borkulo et al., 2016). A similar approach was employed by Contractor et al. (2020) to identify clusters of PTEs, thereby illustrating the utility of network-based methods in trauma research. The eLASSO mode in the IsingFit model employs an L1 regularized logistic regression approach to estimate the pairwise associations between binary variables. The estimates represent the change in the log odds of one event occurring when another event is present, while controlling for all other events in the model. Nodes represent traumatic events; edges reflect the pairwise conditional associations between events. The tuning parameter was set to 0.5, leading to greater sparsity within the network. In addition, network density, indicating how many of the potential connections between nodes are present, was analysed to estimate overall connectivity.

To compare the binary network structures derived from the women and men samples, an Independent Groups Binary Network Comparison Test was conducted using the NetworkComparisonTest package (vers. 2.2.2; Van Borkulo et al., 2025). The test included two components: a network invariance test, which evaluates differences in the overall configuration of the networks, and a global strength invariance test that compares the summed absolute edge weights (i.e. overall connectivity). These procedures allowed for a systematic comparison of the network metrics across genders.

PTE cluster detection was performed using the Louvain algorithm implemented in the igraph package (version 2.1.4; Csardi & Nepusz, 2006). To enhance the robustness of the detected communities, we systematically varied the edge-weight threshold (ranging from 0.1 to 0.5). For each threshold, edges with absolute weights below the threshold were set to zero, and the Louvain algorithm was applied to the resulting thresholded network. Modularity scores were computed to assess the quality of the resulting community structure. Higher modularity reflects a stronger division between clusters, characterized by more within-cluster than between-cluster connections. Networks were visualized with qgraph (vers:1.9.1; Epskamp et al., 2012), where nodes were coloured according to their community memberships and edge thickness corresponded to the strength of the associations.

2.6. Network stability and centrality analysis

A difference test for node strength was performed to determine if the differences in connectivity between nodes were statistically significant. The robustness of the network was evaluated using bootstrapping techniques to assess the stability of edge weights and centrality measures (performed with the bootnet package; vers:1.5; Epskamp, 2015). Nonparametric bootstrapping with 1,000 iterations was performed to re-estimate the network and generate confidence intervals for each edge weight. In addition, a case-dropping bootstrapping procedure was implemented to compute the Correlation-Stability (CS) coefficient, which quantifies the consistency of centrality rankings when a portion of the sample is removed. The strength centrality for each node was computed using the igraph package (vers: 1.2.11; Csardi & Nepusz, 2006). This measure quantifies a node’s direct connectivity by summing the absolute weights of all edges connected to that node.

2.7. PTE type clusters and mental health

To examine the associations between PTE cluster exposure and mental health, we estimated seven multiple regression models separately for women and men – three predicting psychological distress (depression, anxiety, stress), three predicting event-related distress (intrusion, avoidance, hyperarousal), and one predicting adaptive capacity (resilience). In each model, the dependent variable was the sum score of the respective subscale. The independent variables were the average scores of LEC-5 items grouped within each identified PTE cluster. Community detection was stratified by gender, which can yield non-identical cluster definitions. Accordingly, cluster–outcome associations are interpreted within gender, and coefficients are not used for direct cross-gender contrasts. All study variables were normally distributed (−2 < skewness, < 2; −7 < kurtosis < 7, Curran et al., 1996). To control for multiple testing, the Benjamini-Hochberg False Discovery Rate (FDR) correction was applied within each mental health domain (psychological distress, event-related distress, and adaptive capacity), using the fdrtool package (vers. 1.12.18; Klaus et al., 2015).

3. Results

3.1. Participants’ characteristics

The sample included 782 women and 401 men (for detailed sociodemographic description of the sample see Table S1, Supplemental materials). Significant gender differences were evident in psychological distress and event-related distress (Table 1). Women reported significantly higher scores on depression, anxiety and stress, indicating elevated levels emotional distress. Similarly, for event-related distress, women reported higher levels of avoidance, hyper-arousal and intrusion, compared to men. In contrast, men demonstrated higher levels of adaptive capacity, as reflected by greater resilience scores.

Table 1.

Gender differences in psychological distress, event-related distress and adaptive capacity.

	Women (n = 782)		Men (n = 401)
	M	SD	M	SD	T	p	Cohens d
Psychological Distress
Depression	6.05	5.81	3.47	5.03	−6.81	<.001	0.46
Anxiety	3.15	4.21	1.61	3.37	−10.12	<.001	0.39
Stress	6.91	5.81	3.68	4.84	−7.91	<.001	0.59
Event-Related Distress
Avoidance	12.01	8.81	7.76	8.51	−9.01	<.001	0.49
Hyperarousal	11.77	8.98	6.70	7.65	−8.03	<.001	0.59
Intrusion	10.56	8.54	6.15	7.66	−10.47	<.001	0.54
Adaptive Capacity
Resilience	62.29	11.01	64.20	9.57	3.09	<.001	−0.18

3.2. Differences in types of PTE exposure

Table 2 presents descriptive statistics for PTE exposure. The three most frequently reported PTEs in women were sever human suffering, followed by transport accidents other unwanted/uncomfortable sexual experience. The three most frequently reported PTE in men were transportation accidents, followed by serious accident at work/home/during recreational activity and severe human suffering. PTE exposure frequencies differed significantly between men and women for the majority of events. Of note, events connected to physical assault, forced captivity, life-threatening illness or injury, sever human suffering, and sudden, violent death were not different between men and women.

Table 2.

PTE types reported on the Life Events Checklist for DSM-5.

		Women		Men
No.	LEC-5 endorsement	n	%	n	%	χ²	p	ϕ
LE1	Natural disaster	181	23.1	155	38.7	31.35	<.001	0.16
LE2	Fire or explosion	149	19.1	121	30.2	18.61	<.001	0.13
LE3	Transportation accident	314	40.2	223	55.6	25.55	<.001	0.15
LE4	Serious accident at work/home/during recreational activity	177	22.6	165	41.1	44.20	<.001	0.19
LE5	Exposure to a toxic substance	68	8.7	66	16.5	15.91	<.001	0.12
LE6	Physical assault	193	24.7	106	26.4	0.43	.511	0.19
LE7	Assault with a weapon	80	10.2	74	18.5	15.83	<.001	0.12
LE8	Sexual assault	196	25.1	21	5.2	69.57	<.001	0.24
LE9	Other unwanted/uncomfortable sexual experience	306	39.1	39	9.7	110.94	<.001	0.31
LE10	Combat or exposure to a warzone	30	3.8	80	20.0	81.62	<.001	0.26
LE11	Captivity	12	1.5	10	2.5	1.34	.248	0.03
LE12	Life-threatening illness or injury	273	34.9	139	34.7	0.01	.933	0.02
LE13	Sever human suffering	338	43.2	158	39.4	1.58	.207	0.21
LE14	Sudden, violent death	120	15.3	76	19.0	2.50	.114	0.05
LE15	Sudden, accidental death	98	12.5	82	20.4	12.88	<.001	0.10
LE16	Serious injury/harm/death you caused to someone else	12	1.5	14	3.5	4.72	.030	0.06

3.3. Network estimates

Two separate network analyses were conducted, separately for women and men. Community structure within the networks was examined by systematically varying threshold values between 0.1 and 0.5. In both networks, a threshold of 0.5 yielded a modularity score of 0.5 and provided the most coherent community structure. Both networks exhibited comparable densities. The women network had a density of 0.12, the men network had a density of 0.15, suggesting a somewhat greater proportion of connections among events in the men network.

The network comparison analysis did not reveal significant differences between the networks of women and men. Specifically, the network invariance test yielded a test statistic M = 2.54 (p = .08), indicating that the overall configuration of the networks was comparable between groups. Likewise, the global strength invariance test showed nearly identical overall connectivity (18.82 for women vs. 22.04 for men) with a test statistic S = 3.21 (p = .778). These findings suggest that both the structure and the global strength of the networks were statistically equivalent across genders.

In the women's PTE network, the strongest association was observed between LE10 and LE11 – reflecting combat and captivity – with an edge weight of 2.54, indicating that a one-unit increase in one event substantially increases the log-odds of the other. Strong associations were also found between LE6 and LE8 (physical assault and sexual assault; edge weight = 1.26) and between LE7 and LE10 (assault with a weapon and combat or exposure to a warzone; edge weight = 1.15). In the men's network, the strongest link was found between LE8 and LE9 (sexual assault and other unwanted/uncomfortable sexual experiences, edge weight = 1.90), followed by LE8 and LE11 (sexual assault and combat, edge weight = 1.70), and a notable association between LE11 and LE16 (combat severe injury, edge weight = 1.34). For a complete overview of the estimated edge weights, see Tables S2 and S3 in the Supplemental Materials.

The network analyses of both genders revealed a broadly similar clustering of PTEs (see Figure 1), though with clear differences in the composition of Clusters 2 and 3. Details on the items included in each cluster can be found in Table 3. Clusters were interpreted as follows: Cluster 1 reflected experiences related to natural disasters and was labelled ‘Accidental Trauma.’ Cluster 2 reflected ‘Assault-related trauma’ in women and ‘Assault and perpetration-related trauma’ in men. Cluster 3 encompassed ‘Combat-related trauma’ in women and ‘Physical assault Trauma’ in men. Cluster 4 was labelled ‘Life-Threatening Trauma’ and was structurally similar across both genders and comprised events related to serious illness, suffering, or death. Notably, in the women network three items in the network did not cluster with any other events, namely item 1, 5 and 16.

Figure 1.

Specific PTE networks for men and women. Each node represents an item, and each link represents a relation between each pair of items (stronger lines indicate stronger relations).

Note: Node labels (LE1-LE16) correspond to LEC-5 items, as listed in Table 2.

Table 3.

PTE Cluster for women and men network.

PTE Cluster
		Women (n = 782)			Men (n = 401)
Cluster No.	Label	M	SD	LEC-5 items	M	SD	LEC-5 items
1	Accidental Trauma	0.59	0.74	2,3	1.86	1.55	1,2,3,4,10
2	Women: Assault-related Trauma	0.89	1.03	6,8,9	0.61	0.92	8,9,11,16
	Men: Assault and Perpetration related Trauma
3	Women: Combat Related Trauma	0.16	0.46	7,10,11	0.21	0.61	5,6,7
	Men: Physical assault
4	Life-threatening Trauma	1.29	1.37	4, 12,13,14,15	1.13	1.25	12,13,14,15

3.4. Centrality

To assess centrality, a difference test for node strength was performed (see Figure S2a and S2b). In the women network, LE7 (Assault with a weapon) had the highest strength value (4.94), indicating significantly more connection than most other nodes. LE10 (Combat or exposure to a warzone) and LE9 (Other unwanted/uncomfortable sexual experience) also showed high strength values. The difference test confirmed that these three nodes were similarly dominant in the women sample. In contrast, the men network exhibited a different pattern. LE2 (Fire or Explosion) emerged as the most influential node with the highest strength value (5.41), followed by LE15 (Sudden, accidental death).

3.5. Network stability

Nonparametric bootstrapping with 1,000 iterations was conducted for both networks to assess the robustness of the estimated edge weights (see Figure S1a and S1b). In the women network, half of the edges (50.8%) exhibited narrow CIs (less than 0.5 units wide), suggesting precise and stable estimates. Conversely, about one-third of the edges in the women network (33.3%) had wide intervals (exceeding 0.8 units), suggesting lower stability in those specific edge estimates. In the men network, one third of the edges (32.5%) exhibited narrow CIs (less than 0.5 units wide) and nearly half of the edges (47.5%) had wide intervals (greater than 0.8 units).

To evaluate the robustness of node centrality estimates, case-dropping bootstrapping was performed and the correlation stability (CS) coefficient for strength centrality was assessed. The women and men networks yielded a CS coefficient of 0.2 and 0.1, indicating that dropping more than approximately 20% of the sample (women) and 10% of the sample (men) could substantially alter the relative ranking of node strengths.

3.6. Connection of PTEs to mental health

Zero-order correlations among predictors were moderate (maximum r = 0.41), suggesting limited shared variance (see Table S4a and Table S4b). In women, regression analyses showed that assault-related trauma was the only cluster that was consistently linked to higher psychological distress, with positive associations observed for depression, anxiety, and stress (see Table 4). Additionally, life-threatening trauma was related to higher levels of depression. A small effect size was evident only for the association with depression (f² = 0.02), while all other effects were below the threshold for small effects (f² < 0.02). Regarding adaptive capacity, assault-related trauma was negatively associated with resilience, while life-threatening trauma was positively associated. A small effect size was again observed only for assault-related trauma (f² = 0.02). In men, physical assault trauma was positively related to anxiety (f² = 0.02) (see Table 5). Regarding adaptive capacity, associations were found for accidental trauma and resilience. Event-related distress showed no significant associations in either men or women.

Table 4.

Regression analyses predicting mental health via PTE Clusters for women.

Psychological distress
Predictors		β	SE	t	P
Depression (DASS-21)
Cluster 1	Accidental Trauma	−0.008	0.04	−0.22	.972
Cluster 2	Assault-related Trauma	0.16	0.04	4.07	<.001
Cluster 3	Combat Related Trauma	−0.004	0.03	−0.11	.972
Cluster 4	Life-threatening Trauma	−0.01	0.04	−2.49	.038
Anxiety (DASS-21)
Cluster 1	Accidental Trauma	0.02	0.04	−0.49	.972
Cluster 2	Assault-related Trauma	0.10	0.04	2.62	.032
Cluster 3	Combat Related Trauma	0.006	0.04	0.16	.972
Cluster 4	Life-threatening Trauma	0.001	0.04	0.04	.972
Stress (DASS-21)
Cluster 1	Accidental Trauma	0.006	0.04	0.15	.972
Cluster 2	Assault-related Trauma	0.15	0.04	3.79	<.001
Cluster 3	Combat Related Trauma	−0.007	0.04	−0.18	.972
Cluster 4	Life-threatening Trauma	−0.07	0.04	−1.81	.171
Adaptive capacity
Resilience
Cluster 1	Accidental Trauma	0.006	0.04	0.17	.867
Cluster 2	Assault-related Trauma	−0.11	0.04	−2.65	.034
Cluster 3	Combat Related Trauma	−0.007	0.04	−0.18	.867
Cluster 4	Life-threatening Trauma	0.13	0.04	3.13	.007

Table 5.

Regression analyses predicting mental health via PTE clusters for men.

Psychological distress
Predictors		β	SE	t	P
Anxiety (DASS-21)
Cluster 1	Accidental Trauma	0.03	0.06	−0.58	.677
Cluster 2	Assault and Perpetration related Trauma	0.01	0.06	−0.23	.820
Cluster 3	Physical assault Trauma	0.02	0.05	3.12	.002
Cluster 4	Life-threatening Trauma	0.08	0.06	1.33	.338
Adaptive capacity
Resilience
Cluster 1	Accidental Trauma	0.07	0.04	1.80	.029
Cluster 2	Assault and Perpetration related Trauma	−0.10	0.04	−2.38	.963
Cluster 3	Physical assault Trauma	−0.08	0.03	−2.12	.467
Cluster 4	Life-threatening Trauma	0.05	0.04	1.21	.974

4. Discussion

The study aimed to investigate clusters of PTEs and explore gender-specific associations between these clusters and psychological distress, event-related distress and adaptive capacity. Through methods of network analysis, the research sought to clarify whether trauma exposure operates differently across genders in terms of mental health and adaptive functioning.

4.1. Differences in gender-based networks

Nuanced patterns that emerged from the network analyses, reveal both commonalities and distinct differences in how traumatic experiences co-occur in women and men populations. The estimated networks exhibited a similar density across genders with both networks displaying a comparable number of connections. The node with the highest number of connections was (LE7) (assault with a weapon) in the women network and LE2 (Fire or explosion) in the men network, indicating that both types of events were highly interrelated with other events. Despite the same threshold being applied to both networks, differences emerged in the composition of clusters and the strength of specific associations, pointing toward gender-specific co-occurrence patterns of traumatic events.

In the men network, events related to accidental trauma (LE2 & LE3) showed the strongest association, indicating a strong tendency for these events to co-occur. Conversely, in the women network, the most pronounced association occurred between exposure to combat and captivity (LE10 & LE11), which emphasizes the strong link between war-related events. However, caution is warranted when interpreting these findings due to the small number of women reporting such events, which may affect the reliability of the estimates (Epskamp et al., 2018; Fried & Cramer, 2017). Robust associations between events relating to sexual assault (LE8 & LE9) were evident in both networks.

Cluster analysis revealed four clusters for each gender, with two clusters (Clusters 2 and 3) differing in their composition between women and men. Cluster 2 in the women network reflected assault-related trauma, comprising events related to sexual and physical violence. In contrast, Cluster 2 in the men network included assault and perpetration-related trauma, grouping sexual violence with captivity and causing harm to others. In women, Cluster 3 captured combat-related trauma (e.g. war exposure and captivity), while in men, it comprised physical assault-related trauma (e.g. physical assault, assault with a weapon, and exposure to toxins).

This structure differs from previous research results. Contractor et al. (2020) used a network analysis and reported the finding of three distinctive clusters, compromising Accidental trauma, Victimization trauma and Predominant death trauma. Bae et al. (2008) reported six distinctive clusters: Physical assault/others, Accident/ Injury, Natural disasters/Witnessing death, Sexual abuse, Criminal assault, and Man-made disaster. The clusters described are also evident in the current study. Importantly, no gender specific networks were examined in the studies of Bae et al (2008) and Contractor et al. (2020). The specific cluster composition for both genders observed in the present study might explain the variations noted.

Accidental trauma and Life-threatening trauma clusters comprised similar items in both networks. These results suggest that the occurrence of these events increases the likelihood of experiencing additional events within the same cluster. Interestingly, events linked to Accidental trauma and Life-threating trauma were among the most frequently reported in both men and women. The high frequency of events related to Accidental and Life-threatening trauma in both genders likely reinforces their interconnections, making them more salient in the networks. This prevalence can lead to a robust pattern of co-occurrence, thereby allowing these events to emerge as distinct clusters in the analysis.

The composition of assault-related clusters may reflect gendered differences in trauma circumstances. The different composition might result from differences in the circumstances of trauma exposure between women and men. Research has shown that women are more likely to be victims of both sexual and physical violence (Sardinha et al., 2022) and more often report domestic violence than men, which frequently involves a combination of sexual and physical violence (Myhill, 2015). The pattern of sexual violence and captivity among men may reflect victimization and instances of self-defending circumstances under coercive circumstances. Kivlahan et al. (2024) reported that men in captivity can be exposed to various forms of sexual violence.

Importantly, although our findings showed that assault-related trauma was more strongly associated with mental health outcomes in women, this may partly reflect gender differences in trauma exposure frequency. For instance, sexual assault was markedly more prevalent among women in our sample (see Table 2), which can amplify its statistical association with distress. However, prior research suggests that the conditional risk of PTSD after sexual violence may not differ significantly between men and women (Ainamani et al., 2020), highlighting the importance of distinguishing between exposure frequency and conditional risk when interpreting gender differences.

In the women network, the items connected to Cluster 3 relate to combat-related trauma comprise experiences such as combat or exposure to a war zone, captivity, and assault with a weapon. In contrast, the men network Cluster 3 primarily includes items connected to physical assault, without combat exposure. This divergence may reflect gender differences in both the nature and context of trauma exposure. Women in conflict zones are increasingly exposed to war-related trauma through indirect pathways, including displacement, captivity, and gender-based violence (Kelly et al., 2024; Wirtz et al., 2014). Women may also report a broader spectrum of traumatic experiences in such contexts, possibly due to higher vulnerability to multiple overlapping forms of violence (e.g. sexual violence during captivity). In contrast, men’s reported experiences may more narrowly reflect direct physical assault in non-combat settings. Differences in exposure contexts may shape the composition of trauma clusters differently across genders.

Stability estimates across both networks further advise prudence in interpreting the results. Although certain associations exhibit considerable stability, bootstrap tests revealed that many associations had high variability, as indicated by wider confidence intervals. The relatively low correlation stability coefficient suggests that central nodes might differ across other network estimations. This limited stability may be due to low endorsement rates for specific traumatic events, such as combat, which can result in less reliable edge weights and centrality measures. This underscores the need for replicating the analysis in larger and more diverse samples to enhance the robustness of the findings.

4.2. PTE and mental health

Gender specific findings were also evident in the analysis of relations between PTE clusters and mental health. In women, assault-related trauma was consistently associated with heightened psychological distress, including higher levels of depression, anxiety, and stress. This suggests that experiencing interpersonal violence and related events are particularly detrimental to women’s mental well-being. This observation aligns with findings by Schmaus et al. (2008), who reported that women may be more vulnerable to the effects of repeated stress exposure. Additionally, research has shown that women who encounter traumatic events are at a higher risk of developing depression and anxiety compared to men (Ghafoori et al., 2013; Rieder et al., 2022). However, in men, negative associations between physical assault and anxiety were evident, indicating that greater exposure to assault-related trauma was linked to higher levels of anxiety symptoms, showing that internalizing symptoms after trauma exposure can also be evident in men. It is important to note, however, that while men also seem to experience internalizing disorders after trauma (Izutsu et al., 2004), they also exhibit externalizing behaviours – such as substance abuse or antisocial behaviour (Hill & Needham, 2013) – which were not assessed in the current study.

No significant associations were found between psychological distress and accidental trauma, which may be attributed to the nature of the trauma involved. Research indicates that assault-related trauma is more likely to result in adverse mental health outcomes as compared to trauma linked to natural disasters or accidents (Sezgin & Punamäki, 2021). Moreover, survivors of accidents often experience relief, and robust social support systems have been shown to mitigate long-term mental health impairments (Kaniasty & Norris, 2008). These observations underscore the influence of additional factors, such as social support and coping mechanisms on the mental health impact of trauma. In contrast to the current study, which focused solely on the number of traumatic events, future research should also consider the specific circumstances surrounding each trauma.

In women, assault-related trauma was negatively associated with resilience, suggesting these experiences not only increase psychological distress but also undermine stress management. One possible explanation is that these trauma types often involve interpersonal violence, which can severely disrupt a person’s sense of safety, trust, and self-worth, which are central to building resilience (Gopal & Nunlall, 2017). In contrast, life-threatening trauma was linked to greater resilience and lower levels of depression, highlighting a complex relationship between trauma and adaptation. Some evidence suggests that overcoming difficult events may foster strength (Helgeson et al., 2006), though more research is needed.

In men a positive association between accidental trauma and resilience was apparent. A possible explanation might be that accidental trauma may be perceived as less personally targeted compared to traumas connected to interpersonal violence. Overcoming such experiences may foster a sense of mastery and personal efficacy, contributing to higher adaptive capacity. Notably, neither sample showed associations between trauma exposure and event-related distress, suggesting that while trauma exposure may impact overall well-being, it may not directly trigger distress in response to broader and more ambiguous stressors like the onset of the COVID-19 pandemic, which individuals may have experienced in highly variable ways.

4.3. Strengths and limitations

The study’s strengths are multifaceted. To our knowledge, this is the first study to examine gender specific co-occurrence of PTEs using network analysis based on LEC-5 criteria. It investigates the impact of clinically relevant traumatic events and uses methods based on data to explore how these events group together in men and women separately. The study also adopts an innovative approach by integrating clusters derived from the network into regression models, directly linking network structure to mental health outcomes. Moreover, it provides empirical evidence on PTE exposure in a general population, thereby enhancing the generalizability of the findings. Finally, by aggregating the number of traumatic exposures defined under Criterion A of the DSM-5, the study acknowledges the cumulative burden of multiple traumatic experiences on mental health outcomes. In addition, the approach recognizes that diverse types of trauma exposure – including indirect experiences such as witnessing events – can significantly impact well-being (Knežević & Hinek, 2023).

Despite these strengths, several limitations must be acknowledged. First, reliance on self-report data may result in item misinterpretation, which in turn could bias the results. Second, the cross-sectional design precludes to draw causal conclusions. Third, although replication of these results in future studies is desirable, challenges may arise due to differences in trauma experiences among populations. Cultural and economic factors contribute to variations in trauma exposure. For example, Reeves et al. (2017) showed that among women in the United States, Columbia, and Hong Kong, significant differences were observed in the rates of specific traumatic events, such as exposure to natural disasters or physical assault. Such variations in trauma prevalence can lead to different network configurations. Fourth, most associations found in the present study were statistically significant but showed small effect sizes, reflecting the complexity of trauma-related outcomes. These modest effects suggest limited explanatory power and highlight the need to include additional factors know to moderate trauma effects, such as age of exposure (Croft et al., 2019) and individual coping strategies (Littleton et al., 2007). Importantly, given that PTSD symptoms were indexed with the IES-R anchored to COVID-19 rather than to specific PTEs, the associations are likely attenuated. Fifth, the low base rate for certain types of PTE may compromise the reliability of the corresponding estimates.

Sixth, due to an insufficient number of gender-diverse participants, analyses were limited to binary gender groups, which constrains generalizability. Future research should recruit adequately powered gender-diverse samples, given their elevated risk of trauma exposure (e.g. Cogan et al., 2021). Seventh, as trauma clusters were derived separately by gender, predictor structures are not fully isomorphic. While standardization improves comparability, it does not ensure construct equivalence, and coefficient differences across genders should be interpreted with caution. Eighth, PTSD symptoms were assessed using the IES-R anchored to the COVID-19 outbreak rather than a specific traumatic event, limiting specificity and possibly attenuating associations. Moreover, the IES-R does not capture DSM-5 criteria (e.g. negative alterations in cognition and mood), which may further limit construct validity. Use of DSM-5-aligned measures (e.g. PCL-5) anchored to a target event is recommended for future work. Ninth, unequal sample sizes between men and women may have influenced network estimates. As the Network Comparison Test does not adjust for sample size differences, group comparisons should be interpreted with caution. Finally, cumulative trauma was operationalized as the co-occurrence of different trauma types, without accounting for frequency, chronicity, or subjective severity. While this approach captures breadth of exposure, it may underestimate the cumulative burden of repeated trauma of the same type.

4.4. Conclusion and future research

In summary, the study elucidates the complex interplay of trauma exposure across genders by revealing both shared and divergent patterns in the network structures of PETs. The analysis highlights significant links between trauma exposure and adverse mental health outcomes for women, particularly in relation to assault-related trauma. Furthermore, the findings revealed that the relationship between trauma exposure and resilience is nuanced and manifests differently in men and women. These insights enhance our understanding of trauma networks and underscore the need for tailored interventions that address the unique experiences and vulnerabilities of genders. Future research should validate these findings to improve generalizability, incorporate a broader range of measures for psychological distress, and include longitudinal studies to capture the evolving dynamics of trauma exposure and its long-term impact on mental health. Such studies would provide practical guidance to further enhance clinical interventions and outcomes, specifically for men and women.

Disclosure statement

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

Data availability statement

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.

Supplemental Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/20008066.2025.2564040.