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Journal of Behavioral Addictions logoLink to Journal of Behavioral Addictions
. 2025 Jan 20;14(1):166–177. doi: 10.1556/2006.2024.00074

Sexual trauma and compulsive sexual behavior in young men and women: A network analysis involving two samples

Arielle AJ Scoglio 1, Yen-Ling Chen 2, Kuan-Ju Huang 3, Nicholas C Borgogna 4,5, Marc N Potenza 6,7,8, Gretchen R Blycker 9, Shane W Kraus 10,*
PMCID: PMC11974438  PMID: 39841161

Abstract

Background and aims

Sexual trauma is associated with multiple negative health and social conditions, including compulsive sexual behavior. The present study examined network structures involving sexual trauma history, psychological distress (defined as depression and/or anxiety symptoms), substance use, transactional sex, and compulsive sexual behavior. Prior network analysis work in this area is limited.

Methods

We drew upon two samples of young adults (Sample 1: n = 1,884, 69.3% women; Sample 2: n = 2,337, 69.7% women) recruited from universities in the United States in 2020–2022.

Results

Findings support relationships between sexual trauma and compulsive sexual behavior. Relationships between elements of compulsive sexual behavior, timing of trauma, psychological distress, substance use, engagement in transactional sex, and gender differences were identified. Significant edge strength differences between men and women were observed; distress was a more central node for men, trauma was a more central node for women (Sample 1). When examining elements of CSB, significant gender differences in edge strength were also observed (e.g. connections between dissatisfaction and relapse for men, dissatisfaction and negative consequences for women).

Discussion and conclusions

Specific aspects of CSB closely related to sexual trauma history (e.g., dissatisfaction) and co-occurring psychopathology or clinical concerns (e.g., depression, anxiety, substance use, and engagement in transactional sex) and warrant further attention and study.

Keywords: mental health symptoms, addictive behaviors, impulsive behaviors, transactional sex, sexual assault, sexual abuse

Introduction

Sexual violence is an unfortunately common traumatic experience (Basile, Clayton, Rostad, & Leemis, 2020), with over half of women and almost a third of men in the United States reporting having experienced contact sexual violence during their lifetimes. Experiencing sexual trauma has been associated with negative health conditions including posttraumatic stress, depression, anxiety, substance use disorders, cardiovascular disease, interpersonal difficulties, and compulsive sexual behavior (CSB) (Basile et al., 2020; Choudhary, Smith, & Bossarte, 2012; Irish, Kobayashi, & Delahanty, 2010; Santaularia et al., 2014; Slavin, Scoglio, Blycker, Potenza, & Kraus, 2020; Smith & Breiding, 2011).

CSB may be defined as repetitive sexual behavior resulting from a persistent failure to control intense and recurrent sexual impulses or urges and is accompanied by psychological distress or functional impairment (Reed et al., 2022). Currently, there remains debate regarding what criteria should define CSB (Gola et al., 2020; Reed et al., 2022), with a related disorder defined in the eleventh revision of the International Classification of Diseases (ICD-11) (World Health Organization, 2019, 2021). CSB disorder (CSBD) may involve a failure to control sexual urges, repetitive sexual activities becoming central to an individual's life, multiple unsuccessful efforts to reduce repetitive sexual behavior, engagement in repetitive sexual behavior with little or no associated satisfaction and experiencing negative consequences as a result of the behaviors, such as neglecting one's health, personal care, or responsibilities (WHO, 2019, 2021). Generally, CSB has been associated with other additional health issues (Coleman, 1992; Coleman, Raymond, & McBean, 2003; Kowalewska, Gola, Kraus, & Lew-Starowicz, 2020), and individuals with CSB report more depressive and anxiety symptoms compared to those without CSB (Briken et al., 2022; Odlaug et al., 2013).

Relationships between sexual trauma and CSB

A history of sexual trauma, particularly during childhood, has been associated with CSB development (Reis, Park, Dionne, Kim, & Scanavino, 2023; Slavin et al., 2020). Psychological distress (e.g., depression symptoms), as well as feelings of shame and guilt, may mediate relationships between childhood sexual trauma and CSB (Fontanesi et al., 2021; Reis et al., 2023). While psychological distress may be one mechanism by which exposure to sexual trauma is associated with CSB, transactional sex (i.e., exchanging sex for money or drugs) may also be related. Sexual trauma histories in women have been associated with subsequent transactional sex (Decker et al., 2016; Giorgio et al., 2016). The relationships across sexual trauma, CSB, and other risky behaviors such as transactional sex and substance use problems remain a critical focus in research and clinical practice. Exposure to childhood sexual trauma may alter an individual's perceived norms related to sexual behaviors and generate negative feelings and beliefs (e.g., traumatic sexualization, stigmatization, betrayal, powerlessness), which can lead to subsequent engagement in risky sexual behaviors including transactional sex (Ahrens, McCarty, Simoni, Dworsky, & Courtney, 2013). Poorer psychosocial wellbeing (e.g., increased avoidant coping strategies, lower self-esteem, lower self-efficacy, relationship difficulties, poorer psychosocial functioning) may also explain the mechanism linking childhood sexual trauma and risky behaviors (Quina, Morokoff, Harlow, & Zurbriggen, 2004). Individuals with histories of transactional sex show higher impulsivity and compulsivity which are likely related to elevated rates of CSB seen in this population (Blum et al., 2018). In addition, impulsivity is common in individuals engaging in risky behaviors, including high-risk sexual activities (Charnigo et al., 2013) and substance use (De Wit, 2009). Childhood sexual trauma, participating in transactional sex, and social contexts where survivors experienced victim blaming have all been identified as factors related to revictimization (i.e., when an individual who experienced childhood sexual trauma experiences sexual violence again in adolescence or adulthood) (Messman-Moore, Walsh, & DiLillo, 2010; Relyea & Ullman, 2017), which is in turn associated with negative health conditions and more severe outcomes (Charak, Eshelman, & Messman-Moore, 2019; Cloitre et al., 2009). Please note we choose to use the term survivor in this paper to describe those who have experienced sexual trauma. People who have experienced sexual trauma may or may not identify with specific labels related to their trauma history, but the term survivor is meant to empower. Transactional sex is also closely linked to problematic substance use (Blum et al., 2018; Menza, Lipira, Bhattarai, Cali-De Leon, & Orellana, 2020; Oldenburg et al., 2015). There may be gender differences in CSB development and presentation, but research thus far has yielded mixed or unclear results (Kürbitz & Briken, 2021; Slavin et al., 2020).

Generally, research examining sexual violence focuses more on women survivors, while research examining CSB focuses more on men (Kürbitz & Briken, 2021). Comparisons between men and women in the same sample have been lacking in prior work. One previous network analysis of CSB (not in trauma survivors) found no gender differences (Marchetti, 2023). Both sexual trauma and CSB are stigmatized for men and women (Kennedy & Prock, 2018; Turchik & Edwards, 2012), but sexual trauma may be especially stigmatizing for men (Turchik & Edwards, 2012) and CSB may be especially stigmatizing for women due to traditional gender norms (England & Bearak, 2014) and biases, such as the sexual double standard (Kürbitz & Briken, 2021).

Novel methodological approaches, including network analysis, may provide additional insights into relationships but remain underutilized. Network analysis is a method of studying the relationships between entities in a system by directly modelling and visualizing their intercorrelations. Examining relationships between sexual trauma exposure, psychological distress, substance use, transactional sex and CSB in both men and women using a network perspective may reveal specific elements of CSB that are particularly correlated with trauma exposure and indirect pathways through which potential consequences of trauma (e.g., psychological distress, transactional sex) may be associated with CSB. Specifically, a network analysis approach allows for investigation into the dimensions of CSB that may be clinically important, as well as co-occurring clinical concerns among people who have experienced trauma, which could inform clinical assessments and interventions (McNally, 2016). A main difference between a conventional modeling approach and a network analysis approach is how the relationships between variables are conceptualized and tested. A conventional modeling approach is usually based on the construction of a latent variable that is manifested by observable indicators (Borsboom, 2008). A latent variable is used to explain positive relationships among observable indicators. Researchers can include a latent variable in the conceptual model and test for group differences. However, this approach limits the possibility to study a more complex pattern of interrelationships between variables. For example, some CSB domains may have more distal effects on other mental health concerns, and some may have proximal effects. Mental and sexual health symptoms frequently co-occur with one another, and therefore, they may be better modelled as a “system.” Network analysis provides a useful way to visualize complex interrelationships without the need to hypothesize the existence of latent variables (Cramer, Waldorp, van der Maas, & Borsboom, 2010; Schmittmann et al., 2013).

Present study

We investigated network structures involving sexual trauma histories, psychological distress, substance use, and CSB, and their relevant correlates using network analyses in two college student samples. Based on prior literature, we hypothesized that sexual trauma history would connect with psychological distress, substance use, engagement in transactional sex and CSB and that these connections would differ based on self-reported male or female sex. Second, we hypothesized that cumulative sexual trauma exposure (revictimization) might differentially relate to aspects of CSB compared to sexual trauma that occurred in childhood only. Finally, we sought to explore how networks might differ depending on how CSB is defined and measured, as two distinct measures were used in our two samples. The use of two measures will help to identify replication of initial results, something prior CSB research often does not include.

Method

Participants

In Sample 1, among 1,993 participants who completed all survey items on sexual trauma history, 105 (5.3%) participants had incomplete data and were excluded from analysis; thus, the analytic Sample 1 included 1,888 young adults (67.6% women). In Sample 2, among 2,449 participants who completed all survey items on sexual trauma history, 112 (4.6%) participants had incomplete data and were excluded from analysis; thus, the analytic Sample 2 included 2,337 young adults (69.7% women).

Procedure

Data were collected within two larger studies examining 1) sexual and mental health (Jennings, Chen, Way, Borgogna, & Kraus, 2023) and 2) religiosity and health among US undergraduate students (Borgogna, Way, & Kraus, 2024) across three universities (1 Western, 2 Southern) from March of 2020 to May of 2021 (Sample 1) and October of 2020 to May of 2022 (Sample 2). Undergraduate students signed up to participate in online surveys through Psychology Department subject pools to receive course credit (1 h) and provided informed consent.

Measures

Additional details on all measures are available in the Supplemental Material. Most measures were consistent in both samples, except for CSB measurement, as outlined below. The sexual violence items from the Sexual and Physical Abuse History Questionnaire (Leserman, Drossman, & Li, 1995) assessed participants' self-reported histories of sexual trauma. These items queried about unwanted exposure of sex organs, unwanted touching of sex organs, and forced sex. Participants reported if they experienced any of these incidents at the ages of 13 years or younger (childhood) or 14 years or older (adolescence/young adulthood). This questionnaire has good reliability (Sample 1: α = 0.84; Sample 2: α = 0.86). In this study, we coded history of sexual trauma into a binary item (presence of any sexual victimization vs. absence of any sexual victimization). Those who endorsed sexual trauma occurring in both childhood and adolescence/young adulthood were classified as “revictimized.”

The transactional sex variable was a single binary item asking whether an individual has ever received and/or provided money, drugs, or alcohol for sex (yes/no).

In Sample 1, the Compulsive Sexual Behavior Inventory-13 (CSBI-13) (Miner, Raymond, Coleman, & Swinburne Romine, 2017) measured participant's self-reported CSB, or difficulty controlling one's sexual feelings, urges, or behaviors via 13 items rated on 5-point frequency scales. Cronbach's α in this sample was 0.88, indicating good reliability. The CSBI-13 was designed as a single-dimensional scale. In Sample 2, the Compulsive Sexual Behavior Disorder Scale (CSBD-19) (Böthe et al., 2020) was used to measure participant's level of agreement on symptoms related to compulsive sexual behavior disorder (CSBD). The CSBD-19 is a 19-item, 4-point Likert scale developed using the ICD-11 CSBD diagnostic guidelines. The scale contains five subscales: control (α = 0.70), salience (α = 0.70), relapse (α = 0.71), dissatisfaction (α = 0.86), and negative consequences (α = 0.86). We examined each subscale in analyses.

The DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure – Adult (American Psychiatric Association [APA], 2022) was used to measure psychological distress (depression and anxiety symptoms). In Sample 1, participants rated symptom severity in the past two weeks along 5-point scales. In Sample 2, participants rated these items along 4-point scales. We used the sum score of two depression items and three anxiety items, consistent with prior work using this scale (α = 0.86 in both samples) (Doss & Lowmaster, 2022).

The DSM-5 Level 2 – Substance Use – Adult (APA, 2022) assessed substance use. In Sample 1, this 11-item questionnaire collects information on the frequency of use of alcohol and a range of other substances in the past weeks, using five-point scales. In Sample 2, four of the 11 items from the above questionnaire were used. A summed score was generated to reflect the degree of polysubstance use. More details on this measurement are available in the Supplemental Material.

Statistical analysis

Analyses were conducted using the statistical computing software, R (Version 4.4.1; R Core Team). Listwise deletion was used, and individuals with missing data on the study variables were excluded. In network analysis, each variable is considered a “node” which is connected to its adjacent node by an “edge.” An edge represents the partial correlation between two nodes. Mixed graphic models were used to estimate the networks (Haslbeck & Waldorp, 2020). We used the least absolute shrinkage and selection operator (LASSO) with Extended Bayesian Information Criterion (EBIC) to control for potentially spurious edges (tuning parameter = 0.5). The “AND” rule was used for estimating the networks. First, we estimated networks and the strength of the associations using a mixed graphical model for each of the two total samples separately, adjusting for gender (man, woman, or gender-diverse). Second, we estimated networks for men and women from each of the two samples separately to compare gender-related differences. Third, for exploratory purposes, we compared networks separately by trauma exposure category (childhood only vs. revictimization in adolescence/early adulthood). For each network, we examined the stability of the network using a correlation stability coefficient (CS-coefficient) of edges and edge-weight accuracy with 1,000 bootstrapped samples (Epskamp, Borsboom, & Fried, 2018; Epskamp & Fried, 2018), using the bootnet package (Epskamp et al., 2018). A CS-coefficient ≥0.25 is considered acceptable, and ≥0.50 is considered good (Epskamp et al., 2018). The edge-weight accuracy assessed how reliable the edges were estimated in the network, with smaller confidence interval indicating higher accuracy (Epskamp et al., 2018). In addition, we calculated network centrality indices to examine the relative influence of each node, including strength and expected influence (Bringmann et al., 2019; Epskamp et al., 2018). We also calculated bridge centrality measures to identify which CSB node functions as a “bridge” in linking CSB symptoms to other variables (Jones, Ma, & McNally, 2021). For each network comparison, we used the NetworkComparisonTest package (van Borkulo et al., 2022). We reported network structure invariance tests and global connectivity tests and examined statistical significance for specific edge differences and node centrality using the bootstrapped difference test with 1,000 bootstrapped samples. A Fruchterman-Reingold algorithm was used to visualize the networks (Epskamp & Fried, 2018).

We specified sexual trauma history and transactional sex as binary variables. Anderson-Darling normality tests indicated that continuous variables such as CSB items, psychological distress, and substance use were not normally distributed. Therefore, these variables were treated as Gaussian variables after non-paranormal transformation (Isvoranu & Epskamp, 2023; Liu, Lafferty, & Wasserman, 2009), consistent with prior work (Epskamp et al., 2018). A link to de-identified data and analytic R syntax is available in the Supplemental Material.

Ethics

The studies were approved by the Institutional Review Boards of University of Nevada, Las Vegas, Texas Tech University, and University of South Alabama.

Results

Table 1 summarizes demographic and study variable information for each sample, including effect sizes for differences between the two samples. Descriptive statistics by self-reported gender are summarized in Supplemental Table 1. Frequencies of reported trauma exposure are summarized in Supplemental Table 2.

Table 1.

Descriptive statistics of demographic and study variables

Sample 1 Sample 2 Difference
M (SD) or n (%) M (SD) or n (%)
Gender ϕ = 0.02
 Man 569 (30.1%) 655 (28.0%)
 Woman 1,277 (67.6%) 1,628 (69.7%)
 Gender-diversea 42 (2.2%) 54 (2.3%)
Age 19.95 (4.03) 19.65 (3.40) d = 0.08*
Race ϕ = 0.14***
 White 704 (37.3%) 1,140 (48.8%)
 Black 204 (10.8%) 199 (8.5%)
 Asian 399 (21.1%) 302 (12.9%)
 Multiracial or other races 572 (30.3%) 687 (29.4%)
 Prefer not to say 9 (0.5%) 9 (0.4%)
Ethnicity ϕ = 0.02
 Hispanic/Latinx 532 (28.2%) 701 (30.0%)
 Not Hispanic/Latinx 1,347 (71.3%) 1,627 (69.6%)
 Prefer not to say 9 (0.5%) 9 (0.4%)
Any trauma history 868 (46.0%) 967 (41.4%) ϕ = 0.05**
Childhood trauma only 107 (5.7%) 141 (6.0%) ϕ = 0.01
Adolescent/Adult trauma only 460 (24.4%) 411 (17.6%) ϕ = 0.08***
Revictimized 301 (15.9%) 415 (17.8%) ϕ = 0.02
CSBI-13 22.84 (8.26)
CSBI-13 (≥35) 194 (10.3%)
CSBD-19
 Control 4.42 (1.89)
 Salience 4.07 (1.66)
 Relapse 4.49 (1.86)
 Dissatisfaction 5.06 (2.61)
 Negative consequences 10.03 (4.16)
CSBD-19 (≥50) 94 (4.0%)
Psychological distressb 6.44 (4.87) 4.92 (3.86)
Substance usec 1.28 (1.44) 1.36 (1.11)
Transactional sex 45 (2.4%) 81 (3.5%) ϕ = 0.02*
Sample size 1,888 2,337
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Note. The “prefer not to say” category was excluded from the calculation of effect size. Chi-square tests and Welch's t tests were used to test group differences. CSBI-13 = Compulsive Sexual Behavior Inventory, CSBD-19 = Compulsive Sexual Behavior Disorder Scale.

a Gender-diverse includes trans men, trans women, gender queer, and participants who described their gender identity with a different label.

b Sample 1 used a five-point scale (0 = not at all, 4 = nearly every day) while Sample 2 used a four-point scale (0 = not at all, 3 = nearly every day).

c Sample 1 included 11 substances while Sample 2 only included four substances (i.e., alcohol, marijuana, vape or e-cigarettes, and painkillers).

*p < 0.05, **p < 0.01, ***p < 0.001.

Main network findings

Figure 1 displays the network structure of sexual trauma history, CSB, psychological distress, substance use and transactional sex in Samples 1 and 2. The edge-weight accuracy indicated estimates of most edge weights were accurate, except for edges connecting transactional sex and other nodes; summarized in Supplemental Figs 1 and 2. CS-coefficients showed that edges in both networks were stable (Sample 1 = 0.59, Sample 2 = 0.67). In Sample 1, sexual trauma history, psychological distress, substance use, transactional sex, and CSB items were all positively related. CSB was positively associated with sexual trauma history (TRAU), psychological distress (PD), substance use (SU), and transactional sex (TS). Specifically, PD correlated with both TRAU and CSB. SU also correlated with both TRAU and CSB. Similarly, in Sample 2, CSB was positively associated with TRAU, PD, SU, and TS. Among all CSB domains, dissatisfaction (DIS) had the strongest relationship with TRAU and SU, while negative consequences (NEG) had the strongest relationship with PD and TS. Measurement of CSB in Sample 2 includes several different domains (nodes) rather than a single dimension (node) as in Sample 1. The most central node of the network was CSB in Sample 1 and NEG in Sample 2, visible in Supplemental Figs 3 and 4). In Sample 2, DIS and NEG were the most important nodes that linked CSB elements to other nodes, as indicated by bridge centrality index; see Supplemental Table 3. For exploratory purposes, we also estimated network using a CSB total score in Sample 2. Visualization of this network is available in Supplemental Fig. 5.

Fig. 1.

Fig. 1.

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Mixed Graphical Model Network for Sample 1 and Sample 2

Note. Edge thickness represents the strength of association. All associations are positive in the network. For continuous variables, the gray area of the pie charts around the nodes represent explained variance. For binary variables, the dark gray area of the pie represents the accuracy of the intercept model, while the light gray area represents the accuracy contributed by all other variables. Gender was adjusted in the model.

Comparing men and women

In Fig. 2 and Supplemental Table 4, we compared the network structures of male and female participants in Sample 1. The edge-weight accuracy indicated estimates of most edge weights were accurate, except for a) edges connecting transactional sex and other nodes in both male and female networks, and b) edges connecting trauma and other nodes in the male network; shown in Supplemental Figs 6 and 7. CS-coefficients for both male and female networks were below the acceptable cut-off in Sample 1, indicating that the edges in both networks were unstable (male <0.05, female = 0.10). Network invariance testing indicated no gender-related differences in overall network structure (p = 0.34) and global strength (p = 0.19). Comparisons of edge strength and node centrality can be found in Supplemental Tables 4 and 5. Significant gender differences in edge strength were labeled in Fig. 2 but should be interpreted with caution.

Fig. 2.

Fig. 2.

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Mixed Graphical Model Networks Comparing Men and Women in Sample 1

Note. Edge thickness represents the strength of association. All associations are positive in the network. For continuous variables, the gray area of the pie charts around the nodes represent explained variance. For binary variables, the dark gray area of the pie represents the accuracy of the intercept model, while the light gray area represents the accuracy contributed by all other variables. See Fig. 1 for full variable names of nodes. * denotes significant differences in edge weights between networks. CS coefficients indicated that the men and women networks were not stable in Sample 1; results should be interpreted with caution.

In Fig. 3 and Supplemental Table 4, we compared the network structures of male and female participants in Sample 2. The edge-weight accuracy indicated estimates of most edge weights were accurate, except for a) edges connecting transactional sex and other nodes in both male and female networks, and b) edges connecting trauma and other nodes in the male network; available in Supplemental Figs 8 and 9. Correlation stability coefficients showed that edges in both male and female networks in Sample 2 were stable (male = 0.67, female = 0.67). Similar to Sample 1, the network invariance test indicated there were no gender-related differences in overall network structure (p = 0.14) and global strength (p = 0.89). Comparisons of edge strength and node centrality can be found in Supplemental Tables 4 and 6. Significant gender differences in edge strength were labeled in Fig. 3. There was little evidence of gender differences in node centrality in Sample 2; however, control and salience and dissatisfaction and negative consequences had stronger links to each other for women compared to men. For men, relapse and dissatisfaction had a stronger link.

Fig. 3.

Fig. 3.

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Mixed Graphical Model Networks Comparing Men and Women in Sample 2

Note. Edge thickness represents the strength of association. All associations are positive in the network. For continuous variables, the gray area of the pie charts around the nodes represent explained variance. For binary variables, the dark gray area of the pie represents the accuracy of the intercept model, while the light gray area represents the accuracy contributed by all other variables. See Fig. 1 for full variable names of nodes.* denotes significant differences in edge weights between networks.

Childhood trauma and revictimization

We also explored cumulative effects of sexual trauma by creating two networks using different sexual trauma nodes: a) whether an individual experienced sexual trauma only in their childhood (CST; at the age of 13 or younger), and b) whether an individual experienced revictimization (REV; sexual trauma in childhood AND in adolescence/early adulthood). In Fig. 4 (Sample 1), the edge-weight accuracy indicated estimates of most edge weights were accurate, except for a) edges connecting transactional sex and other nodes in both CST and REV networks, and b) edges connecting trauma and other nodes in the CST network; summarized in Supplemental Figs 10 and 11. CS-coefficients showed that edges were stable in the REV network (0.52) but not in the CST network (0.09). Comparisons of edge strength and node centrality can be found in Supplemental Tables 7 and 8.

Fig. 4.

Fig. 4.

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Mixed Graphical Model Networks Comparing Childhood Sexual Trauma and Sexual Revictimization in Sample 1

Note. Edge thickness represents the strength of association. All associations are positive in the network. For continuous variables, the gray area of the pie charts around the nodes represent explained variance. For binary variables, the dark gray area of the pie represents the accuracy of the intercept model, while the light gray area represents the accuracy contributed by all other variables. Gender was adjusted in the model. CST = Childhood sexual trauma. REV = Sexual revictimization. See Fig. 1 for variable names of other nodes. * denotes significant differences in edge weights between networks. CS coefficients indicated that the CST network was not stable in Sample 1; results of the CST network should be interpreted with caution.

In Fig. 5 (Sample 2), the edge-weight accuracy indicated estimates of most edge weights were accurate, except for a) edges connecting transactional sex and other nodes in both CST and REV networks, and b) edges connecting trauma and other nodes in the CST network as shown in Supplemental Figs 12 and 13. CS-coefficients showed good stability in both networks (CST = 0.75, REV = 0.67). We found significant differences between childhood and revictimization networks in network structure (p = 0.04) and global strength (p = 0.003), the sum of the absolute value of network edge weights were 3.05 (childhood network), and 5.35 (revictimization network). Among all CSB domains, DIS was associated with trauma history only in the sexual revictimization network (REV; p = 0.01). The relationship between PD and trauma history was stronger in the sexual revictimization network (REV) than in the childhood only network (CST; p = 0.047). Comparisons of edge strength and node centrality can be found in Supplemental Tables 7 and 9. History of trauma in the revictimization network (REV) was a more central node in the network than in the childhood network (CST) in terms of strength (p = 0.005) and expected influence (p = 0.005).

Fig. 5.

Fig. 5.

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Mixed Graphical Model Networks Comparing Childhood Sexual Trauma and Sexual Revictimization in Sample 2

Note. Edge thickness represents the strength of association. All associations are positive in the network. For continuous variables, the gray area of the pie charts around the nodes represent explained variance. For binary variables, the dark gray area of the pie represents the accuracy of the intercept model, while the light gray area represents the accuracy contributed by all other variables. Gender was adjusted in the model. CST = Childhood sexual trauma. REV = Sexual revictimization. See Fig. 1 for full variable names of other nodes. * denotes significant differences in edge weights between networks.

Discussion

Our findings build on prior studies by identifying connections between elements of CSB, age at sexual trauma, psychological distress, substance use, and engagement in transactional sex. Specifically, our findings suggest that the dissatisfaction dimension of CSB is modestly connected with sexual trauma history and problematic substance use. On the other hand, the negative consequences dimension of CSB modestly correlated with psychological distress, sexual trauma, and engagement in transactional sex. Given debates regarding the roles of specific components of CSB, we believe there is clinical utility in exploring multiple measurements of CSB. Although CSB was measured in distinct ways in the two samples, similar strengths of connections were observed between CSB and sexual trauma history, CSB and substance use, and CSB and transactional sex in both sample networks. The connections between sexual trauma and psychological distress with CSB were stronger and more directly observed in the Sample 1 network, where CSB was measured as a single dimension. In Sample 2, we observed gender differences in connections between CSB domains (e.g. dissatisfaction and negative consequences having stronger links in women, dissatisfaction and relapse having stronger links in men). Future research could further explore potential gender-related differences by examining these trending relationships between CSB and other health-related concerns (e.g., trauma history, substance use, psychological distress) particularly among clinical populations. The replication of the present results among clinical and community samples with elevated rates of CSB, sexual trauma, substance use disorders and psychological distress is recommended.

Importantly, we found that both CSB and psychological distress were related to sexual trauma history, which is consistent with literature suggesting sexual trauma history negatively impacts an individual's psychological and sexual health (Chen et al., 2010). Findings from the current series of network analyses indicate that there are specific CSB nodes connecting sexual trauma history and psychological distress. In assessing and treating trauma symptoms, a clinician may consider how past sexual trauma history has shaped a person's core beliefs leading to feeling dissatisfaction when engaging in sexual activity. Sexual dissatisfaction may impact romantic relationships and possibly maintain trauma-related psychological distress. Similarly, the inter-correlations among sexual trauma history and CSB may provide insights into how addictions may present in individuals who have experienced sexual trauma and supports further screening for these experiences among people seeking help. Future research could further examine mechanisms underlying the observed pathway involving sexual trauma history, CSB symptoms, and substance use. It is possible that an individual feeling dissatisfied with sex, related to their trauma history, may engage in addictive substances or behaviors to cope with distress.

We found that trauma history was connected to CSB only in the networks examining revictimized participants. This is consistent with literature suggesting that multiply victimized individuals may experience more severe negative health and social consequences (Finkelhor, Ormrod, & Turner, 2007; Walsh, DiLillo, & Scalora, 2011). Guilt and shame are common responses to sexual trauma (Chen et al., 2024; Herman, 2012). If such trauma occurs early in the life course (e.g. childhood, adolescence) and multiple times, it may impact the development and maintenance of sexual schemas (Slavin et al., 2020), resulting in an internal message that engaging in sexual activity is shameful. This shame may in turn relate to CSB, as well as psychological distress, as observed in our samples.

Limitations and future directions

Our findings, although from two diverse samples of young adults, may have limited generalizability. First, our measure of sexual trauma defines the experience broadly, with items including experiences of rape, sexual assault, and someone exposing themselves to the participant in a non-consensual context, as a child and as an adolescent/young adult. Future research could consider using a narrower definition of sexual trauma (e.g., rape) or comparing different types, such as including image-based sexual abuse (Pedersen, Bakken, Stefansen, & von Soest, 2023). Similarly, our measurement of substance use as a sum score captured polysubstance use. It was not intended to assess for substance use disorders. The current sample was not a clinical sample and reported low to varying degrees of substance use, as might be expected. Future work should examine the relationship between specific substances, including substance use disorders, and sexual trauma and CSB within clinical samples. Third, there was a small proportion of individuals in our samples who reported engaging in transactional sex. The associations between transactional sex and other variables are less precise and should be interpreted with caution in this convenience sample. The relationships between transactional sex, sexual trauma and substance use could represent an important future research direction and should be further explored in large community and clinical samples. Similarly, only a small proportion of men reported sexual trauma; the associations between trauma and other mental and sexual health concerns for men also warrant further research. Findings from the childhood-only trauma network should be similarly interpreted with caution because of observed instability in the network, and this area too represents an important future research direction. However, the current study provides important preliminary evidence of examining the cumulative negative effects of multiple victimization experiences on sexual and mental health. Fourth, our analyses focused on men and women, and future work should examine the experiences of gender identities outside of the traditional binary. Moreover, a scoring discrepancy between Samples 1 and 2 occurred with regards to the DSM-5 cross-cutting symptom measures, and further replication should ensure that all samples include the same 5-point scoring scale when assessing psychological distress. Finally, data were collected during the COVID-19 pandemic with potential unknown influences on perceptions of trauma and sexual behavior. We were not able to include pandemic-related measures in our analyses. Investigations outside of the COVID-19 pandemic and with larger samples that include trans- or non-binary-identifying persons are indicated. Our study relies on cross-sectional data that offer a snapshot of experiences of young adults with histories of sexual trauma; therefore, future longitudinal research is warranted to establish precise temporality.

Conclusions

Our investigation of network structures involving sexual trauma history, psychological distress, substance use, and CSB revealed important connections between these constructs and differences between men and women with sexual trauma histories. Specific dimensions of CSB closely related to sexual trauma history (e.g., dissatisfaction) and co-occurring psychopathology and clinical concerns (e.g., depression, anxiety, substance use, and engagement in transactional sex) should be explicitly targeted in clinical settings for individuals presenting with CSB or seeking treatment for trauma-related symptoms.

Supplementary material

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Acknowledgments

The authors would like to acknowledge the study participants who shared their personal experiences.

Footnotes

Funding sources: S.W.K was supported by Kindbridge Research Institute (#GR15685).

Authors' contribution: AAJS: conceptualization, methodology, supervision, writing- original draft, writing- review & editing; YLC: formal analysis, methodology, visualization, writing- original draft, writing- review & editing; KJH: formal analysis, methodology, visualization, writing- review & editing; NCB: methodology, writing- review & editing; MNP: writing- review & editing; GRB: conceptualization, writing- review & editing; SWK: conceptualization, data curation, investigation, methodology, supervision, writing- review & editing.

Conflict of interest: MNP serves as an Associate Editor of the Journal of Behavioral Addictions. The other authors have no conflicts to disclose.

Contributor Information

Arielle A.J. Scoglio, Email: ascoglio@bentley.edu.

Yen-Ling Chen, Email: ylchen@unlv.nevada.edu.

Kuan-Ju Huang, Email: kuanjuhuang2@gmail.com.

Nicholas C. Borgogna, Email: borgogna@uab.edu.

Shane W. Kraus, Email: Shane.kraus@unlv.edu.

References

  1. Ahrens, K. R., McCarty, C., Simoni, J., Dworsky, A., & Courtney, M. E. (2013). Psychosocial pathways to sexually transmitted infection risk among youth transitioning out of foster care: Evidence from a longitudinal cohort study. Journal of Adolescent Health, 53(4), 478–485. 10.1016/j.jadohealth.2013.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. American Psychiatric Association (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). 10.1176/appi.books.9780890425787. [DOI] [Google Scholar]
  3. Basile, K. C., Clayton, H. B., Rostad, W. L., & Leemis, R. W. (2020). Sexual violence victimization of youth and health risk behaviors. American Journal of Preventive Medicine, 58(4), 570–579. 10.1016/j.amepre.2019.11.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Blum, A. W., Lust, K., Christenson, G., Odlaug, B. L., Redden, S. A., & Grant, J. E. (2018). Transactional sexual activity among university students: Prevalence and clinical correlates. International Journal of Sexual Health, 30(3), 271–280. 10.1080/19317611.2018.1491922. [DOI] [Google Scholar]
  5. Borgogna, N. C., Way, B. M., & Kraus, S. W. (2024). Multicultural considerations for the psychometrics of the brief pornography screen. Cyberpsychology, Behavior, and Social Networking, 27(5), 318–327. 10.1089/cyber.2023.0493. [DOI] [PubMed] [Google Scholar]
  6. Borsboom, D. (2008). Latent variable theory. Measurement (Mahwah, N.J.), 6(1–2), 25–53. 10.1080/15366360802035497. [DOI] [Google Scholar]
  7. Böthe, B., Potenza, M. N., Griffiths, M. D., Kraus, S. W., Klein, V., Fuss, J., & Demetrovics, Z. (2020). The development of the Compulsive Sexual Behavior Disorder Scale (CSBD-19): An ICD-11 based screening measure across three languages. Journal of Behavioral Addictions, 9(2), 247–258. 10.1556/2006.2020.00034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Briken, P., Wiessner, C., Štulhofer, A., Klein, V., Fuß, J., Reed, G. M., & Dekker, A. (2022). Who feels affected by “out of control” sexual behavior? Prevalence and correlates of indicators for ICD-11 compulsive sexual behavior disorder in the German health and sexuality survey (GeSiD). Journal of Behavioral Addictions, 11(3), 900–911. 10.1556/2006.2022.00060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., … Snippe, E. (2019). What do centrality measures measure in psychological networks? Journal of Abnormal Psychology, 128(8), 892–903. 10.1037/abn0000446. [DOI] [PubMed] [Google Scholar]
  10. Charak, R., Eshelman, L. R., & Messman-Moore, T. L. (2019). Latent classes of childhood maltreatment, adult sexual assault, and revictimization in men: Differences in masculinity, anger, and substance use. Psychology of Men & Masculinities, 20(4), 503–514. 10.1037/men0000185. [DOI] [Google Scholar]
  11. Chen, Y. L., Huang, K. J., Scoglio, A. A., Borgogna, N. C., Potenza, M. N., Blycker, G. R., & Kraus, S. W. (2024). A network comparison of sexual dysfunction, psychological factors, and body dissociation between individuals with and without sexual trauma histories. Journal of Trauma & Dissociation, 25(1), 62–82. 10.1080/15299732.2023.2231915. [DOI] [PubMed] [Google Scholar]
  12. Chen, L. P. B. S., Murad, M. H. M. D., Paras, M. L. B. S., Colbenson, K. M. B. S., Sattler, A. L. B. S., Goranson, E. N. B. S., … Zirakzadeh, A. M. D. (2010). Sexual abuse and lifetime diagnosis of psychiatric disorders: Systematic review and meta-analysis. Mayo Clinic Proceedings, 85(7), 618–629. 10.4065/mcp.2009.0583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Choudhary, E., Smith, M., & Bossarte, R. M. (2012). Depression, anxiety, and symptom profiles among female and male victims of sexual violence. American Journal of Men's Health, 6(1), 28–36. 10.1177/1557988311414045. [DOI] [PubMed] [Google Scholar]
  14. Cloitre, M., Stolbach, B. C., Herman, J. L., Kolk, B. v. d., Pynoos, R., Wang, J., & Petkova, E. (2009, 2009/10/01). A developmental approach to complex PTSD: Childhood and adult cumulative trauma as predictors of symptom complexity. Journal of Traumatic Stress, 22(5), 399–408. 10.1002/jts.20444. [DOI] [PubMed] [Google Scholar]
  15. Coleman, E. (1992). Is your patient suffering from compulsive sexual behavior? Psychiatric Annals, 22(6), 320–325. 10.3928/0048-5713-19920601-09. [DOI] [Google Scholar]
  16. Coleman, E., Raymond, N., & McBean, A. (2003). Assessment and treatment of compulsive sexual behavior. Minnesota Medicine, 86(7), 42–47. [PubMed] [Google Scholar]
  17. Cramer, A. O. J., Waldorp, L. J., van der Maas, H. L. J., & Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and Brain Sciences, 33(2–3), 137–150. 10.1017/S0140525X09991567. [DOI] [PubMed] [Google Scholar]
  18. De Wit, H. (2009). Impulsivity as a determinant and consequence of drug use: A review of underlying processes. Addiction Biology, 14(1), 22–31. 10.1111/j.1369-1600.2008.00129.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Decker, M. R., Benning, L., Weber, K. M., Sherman, S. G., Adedimeji, A., Wilson, T. E., … Golub, E. T. (2016). Physical and sexual violence predictors: 20 Years of the women's interagency HIV study cohort. American Journal of Preventive Medicine, 51(5), 731–742. 10.1016/j.amepre.2016.07.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Doss, R. A., & Lowmaster, S. E. (2022). Validation of the DSM-5 level 1 cross-cutting symptom measure in a community sample. Psychiatry Research, 318, 114935–114935. 10.1016/j.psychres.2022.114935. [DOI] [PubMed] [Google Scholar]
  21. England, P., & Bearak, J. (2014). The sexual double standard and gender differences in attitudes toward casual sex among U.S. university students. Demographic Research, 30, 1327–1338. http://www.jstor.org/stable/26348237. [Google Scholar]
  22. Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. 10.3758/s13428-017-0862-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617–634. 10.1037/met0000167. [DOI] [PubMed] [Google Scholar]
  24. Finkelhor, D., Ormrod, R. K., & Turner, H. A. (2007). Poly-victimization: A neglected component in child victimization. Child Abuse & Neglect, 31(1), 7–26. 10.1016/j.chiabu.2006.06.008. [DOI] [PubMed] [Google Scholar]
  25. Fontanesi, L., Marchetti, D., Limoncin, E., Rossi, R., Nimbi, F. M., Mollaioli, D., … Ciocca, G. (2021). Hypersexuality and trauma: A mediation and moderation model from psychopathology to problematic sexual behavior. Journal of Affective Disorders, 281, 631–637. 10.1016/j.jad.2020.11.100. [DOI] [PubMed] [Google Scholar]
  26. Giorgio, M., Townsend, L., Zembe, Y., Guttmacher, S., Kapadia, F., Cheyip, M., & Mathews, C. (2016). Social support, sexual violence, and transactional sex among female transnational migrants to South Africa. American Journal of Public Health (1971), 106(6), 1123–1129. 10.2105/AJPH.2016.303107. [DOI] [PubMed] [Google Scholar]
  27. Gola, M., Lewczuk, K., Potenza, M. N., Kingston, D. A., Grubbs, J. B., Stark, R., & Reid, R. C. (2020). What should be included in the criteria for compulsive sexual behavior disorder? Journal of Behavioral Addictions, 11(2), 160–165. 10.1556/2006.2020.00090. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Haslbeck, J. M. B., & Waldorp, L. J. (2020). mgm: Structure estimation for time-varying mixed graphical models in high-dimensional data. Journal of Statistical Software, 93(8), 1–46. 10.18637/jss.v093.i08. [DOI] [Google Scholar]
  29. Herman, J. L. (2012). Shattered shame states and their repair (1st ed., pp. 157–170). Routledge. 10.4324/9780429480140-4. [DOI] [Google Scholar]
  30. Irish, L., Kobayashi, I., & Delahanty, D. L. (2010). Long-term physical health consequences of childhood sexual abuse: A meta-analytic review: Special issue on health consequences of child maltreatment. Journal of Pediatric Psychology, 35(5), 450–461. 10.1093/jpepsy/jsp118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Isvoranu, A.-M., & Epskamp, S. (2023). Which estimation method to choose in network psychometrics? Deriving guidelines for applied researchers. Psychological Methods, 28(4), 925–946. 10.1037/met0000439. [DOI] [PubMed] [Google Scholar]
  32. Jennings, T. L., Chen, Y. L., Way, B. M., Borgogna, N. C., & Kraus, S. W. (2023). Associations between online dating platform use and mental and sexual health among a mixed sexuality college student sample. Computers in Human Behavior, 144, 107727. [Google Scholar]
  33. Jones, P. J., Ma, R., & McNally, R. J. (2021). Bridge centrality: A network approach to understanding comorbidity. Multivariate Behavioral Research, 56(2), 353–367. 10.1080/00273171.2019.1614898. [DOI] [PubMed] [Google Scholar]
  34. Kennedy, A. C., & Prock, K. A. (2018). I still feel like I am not normal: A review of the role of stigma and stigmatization among female survivors of child sexual abuse, sexual assault, and intimate partner violence. Trauma, Violence, & Abuse, 19(5), 512–527. 10.1177/1524838016673601. [DOI] [PubMed] [Google Scholar]
  35. Kowalewska, E., Gola, M., Kraus, S. W., & Lew-Starowicz, M. (2020). Spotlight on compulsive sexual behavior disorder: A systematic review of research on women. Neuropsychiatric Disease and Treatment, 16, 2025–2043. 10.2147/NDT.S221540. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Kürbitz, L. I., & Briken, P. (2021). Is compulsive sexual behavior different in women compared to men? Journal of Clinical Medicine, 10(15). 10.3390/jcm10153205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Leserman, J., Drossman, D. A., & Li, Z. (1995). The reliability and validity of a sexual and physical abuse history questionnaire in female patients with gastrointestinal disorders. Behavioral Medicine (Washington, D.C.), 21(3), 141–150. 10.1080/08964289.1995.9933752. [DOI] [PubMed] [Google Scholar]
  38. Liu, H., Lafferty, J., & Wasserman, L. (2009). The nonparanormal: Semiparametric estimation of high dimensional undirected graphs. Journal of Machine Learning Research, 10, 2295–2328. [PMC free article] [PubMed] [Google Scholar]
  39. Marchetti, I. (2023). The structure of compulsive sexual behavior: A network analysis study. Archives of Sexual Behavior, 52(3), 1271–1284. 10.1007/s10508-023-02549-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. McNally, R. J. (2016). Can network analysis transform psychopathology? Behaviour Research and Therapy, 86, 95–104. 10.1016/j.brat.2016.06.006. [DOI] [PubMed] [Google Scholar]
  41. Menza, T. W., Lipira, L., Bhattarai, A., Cali-De Leon, V., & Orellana, E. R. (2020). Prevalence and correlates of transactional sex among women of low socioeconomic status in Portland, OR. BMC Women's Health, 20(1), 219–219. 10.1186/s12905-020-01088-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Messman-Moore, T. L., Walsh, K. L., & DiLillo, D. (2010). Emotion dysregulation and risky sexual behavior in revictimization. Child Abuse & Neglect, 34(12), 967–976. 10.1016/j.chiabu.2010.06.004. [DOI] [PubMed] [Google Scholar]
  43. Miner, M. H., Raymond, N., Coleman, E., & Swinburne Romine, R. (2017). Investigating clinically and scientifically useful cut points on the compulsive sexual behavior inventory. Journal of Sexual Medicine, 14(5), 715–720. 10.1016/j.jsxm.2017.03.255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Odlaug, B. L., Lust, K., Schreiber, L. R. N., Christenson, G., Derbyshire, K., Harvanko, A., … Grant, J. E. (2013). Compulsive sexual behavior in young adults. Annals of Clinical Psychiatry, 25(3), 193–200. [PubMed] [Google Scholar]
  45. Oldenburg, C. E., Perez-Brumer, A. G., Biello, K. B., Landers, S. J., Rosenberger, J. G., Novak, D. S., … Mimiaga, M. J. (2015). Transactional sex among men who have sex with men in Latin America: Economic, sociodemographic, and psychosocial factors. American Journal of Public Health (1971), 105(5), e95–e102. 10.2105/AJPH.2014.302402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Pedersen, W., Bakken, A., Stefansen, K., & von Soest, T. (2023). Sexual victimization in the digital age: A population-based study of physical and image-based sexual abuse among adolescents. Archives of Sexual Behavior, 52(1), 399–410. 10.1007/s10508-021-02200-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Quina, K., Morokoff, P. J., Harlow, L. L., & Zurbriggen, E. L. (2004). Cognitive and attitudinal paths from childhood trauma to adult HIV risk. In Koenig, L. J., Doll, L. S., O’Leary, A., & Pequegnat, W. (Eds.), From child sexual abuse to adult sexual risk: Trauma, revictimization, and intervention (pp. 117–134). American Psychological Association. 10.1037/10785-006. [DOI] [Google Scholar]
  48. R Core Team . R: A language and environment for statistical computing. In R Foundation for Statistical Computing. https://www.R-project.org/. [Google Scholar]
  49. Reed, G. M., First, M. B., Billieux, J., Cloitre, M., Briken, P., Achab, S., … Bryant, R. A. (2022). Emerging experience with selected new categories in the ICD‐11: Complex PTSD, prolonged grief disorder, gaming disorder, and compulsive sexual behaviour disorder. World Psychiatry, 21(2), 189–213. 10.1002/wps.20960. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Reis, S. C., Park, K. E., Dionne, M. M., Kim, H. S., & Scanavino, M. D. T. (2023). Symptoms of depression (not anxiety) mediate the relationship between childhood sexual abuse and compulsive sexual behaviors in men. Revista brasileira de psiquiatria, 45(1), 38–45. 10.47626/1516-4446-2022-2584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Relyea, M., & Ullman, S. E. (2017). Predicting sexual assault revictimization in a longitudinal sample of women survivors: Variation by type of assault. Violence Against Women, 23(12), 1462–1483. 10.1177/1077801216661035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Santaularia, J., Johnson, M., Hart, L., Haskett, L., Welsh, E., & Faseru, B. (2014). Relationships between sexual violence and chronic disease: A cross-sectional study. BMC Public Health, 14(1), 1286–1286. 10.1186/1471-2458-14-1286. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Schmittmann, V. D., Cramer, A. O. J., Waldorp, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31(1), 43–53. 10.1016/j.newideapsych.2011.02.007. [DOI] [Google Scholar]
  54. Slavin, M. N., Scoglio, A. A. J., Blycker, G. R., Potenza, M. N., & Kraus, S. W. (2020). Child sexual abuse and compulsive sexual behavior: A systematic literature review. Current Addiction Reports, 7(1), 76–88. 10.1007/s40429-020-00298-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Smith, S. G., & Breiding, M. J. (2011). Chronic disease and health behaviours linked to experiences of non-consensual sex among women and men. Public Health (London), 125(9), 653–659. 10.1016/j.puhe.2011.06.006. [DOI] [PubMed] [Google Scholar]
  56. Turchik, J. A., & Edwards, K. M. (2012). Myths about male rape: A literature review. Psychology of Men & Masculinity, 13(2), 211–226. 10.1037/a0023207. [DOI] [Google Scholar]
  57. van Borkulo, C. D., van Bork, R., Boschloo, L., Kossakowski, J. J., Tio, P., Schoevers, R. A., … Waldorp, L. J. (2022). Comparing network structures on three aspects: A permutation test. Psychological Methods, 28(6), 1273–1285. 10.1037/met0000476. [DOI] [PubMed] [Google Scholar]
  58. Walsh, K., DiLillo, D., & Scalora, M. J. (2011). The cumulative impact of sexual revictimization on emotion regulation difficulties: An examination of female inmates. Violence Against Women, 17(8), 1103–1118. 10.1177/1077801211414165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. World Health Organization (2019–2021). International classification of diseases, eleventh revision (ICD-11). https://icd.who.int/browse11. Licensed under Creative Commons Attribution-NoDerivatives 3.0 IGO licence (CC BY-ND 3.0 IGO). [Google Scholar]

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