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. Author manuscript; available in PMC: 2023 May 1.
Published in final edited form as: J Trauma Dissociation. 2021 Oct 13;23(3):324–338. doi: 10.1080/15299732.2021.1989110

Elucidating the Association between Military Sexual Trauma Types and Different Types of Risky Behaviors

Shannon R Forkus 1, Nicole H Weiss 1, Svetlana Goncharenko 1, Melissa R Schick 1, Lindsey L Monteith 2,3, Ateka A Contractor 4
PMCID: PMC9152949  NIHMSID: NIHMS1751711  PMID: 34641771

Abstract

Introduction.

Military sexual harassment (MSH) and assault (MSA) are associated with serious mental and physical health outcomes among military personnel and veterans. However, less is known about how these experiences relate to risky, impulsive, and health-compromising behaviors.

Objective.

The goal of the current study was to assess MSH and MSA in relation to a wide range of risky behaviors.

Method.

Participants were 512 veterans in the community (M age = 41.36, 71.3% male, 71.3% white) who completed an online survey via Amazon’s Mechanical Turk (MTurk).

Results.

Compared to veterans with no MSH or MSA history and veterans with a MSH history, veterans with a MSA history reported greater past-month risky behavior engagement, both overall and for specific behaviors, including problematic use of alcohol, drugs, gambling, technology, risky sexual behaviors, eating behaviors, illegal behaviors, reckless spending, physically aggressive behaviors, verbally aggressive behaviors, property destruction, reckless driving, non-suicidal self-injury, and suicidal behavior.

Discussion.

Our findings emphasize the importance of differentiating between MSH and MSA when detecting and intervening with veterans at risk for engaging in risky behaviors.

Keywords: Military sexual trauma, risky behaviors, military populations

The Department of Veterans Affairs (VA) refers to experiences of sexual violence that occur during military service as military sexual trauma (MST; Title 38 U.S. Code 1720D). MST is characterized by experiences of sexual harassment (MSH; unwanted/unwelcome advances, requests, and other inappropriate verbal or physical contact of a sexual nature) and/or sexual assault (MSA; sexual activity that occurs without consent or by force, threats, intimidation, or coercion; Title 38 U.S. Code 1720D). According to a recent meta-analysis, MST is highly prevalent, with rates estimated at 31.2% and 13.9% for MSH and MSA, respectively (Wilson, 2018). MST is also associated with numerous deleterious health outcomes (Kimerling et al., 2007; Suris & Lind, 2008), including suicide mortality (Kimerling et al., 2016).

One potential explanation for this may entail risky behaviors (Nock et al., 2018), which can be defined as actions that increase one’s risk for experiencing negative physical, psychological, social, or financial outcomes (Byrnes, Miller, & Schafer, 1999). Risky behaviors have been linked to higher rates of negative psychosocial and health consequences across several domains (Contractor, Weiss, Dranger, Ruggero, & Armour, 2017; Drescher et al., 2003; Harris et al., 2017); these include poor mental and physical health, involvement in the criminal justice system, and premature death (Cawley & Ruhm, 2011; Drescher et al., 2003; Zohrabian & Philipson, 2010). As risky behaviors present a significant clinical and public health concern, additional research is needed to identify experiences that contribute to risky behavior involvement, to inform targeted interventions and prevention efforts.

Risky behaviors have been studied extensively in the context of trauma (for a review, see Ben-Zur and Zeidner, 2009). Theoretically, trauma has been conceptualized as an antecedent to risky behaviors, such that risky behaviors may develop as a strategy to modulate aversive trauma-related symptoms and distress (Weiss, Sullivan, & Tull, 2015) or to restore personal resources and control compromised by trauma (Ben-Zur & Zeidner, 2009). Further, trauma may increase risk-taking by interfering with attentional and information processing capacity, affecting decision-making processes and perceptions of risk (Ben-Zur & Zeidner, 2009). Notably, given the relevance of risky behaviors to trauma, risky behavior engagement (E2) was added as a potential symptom of PTSD (APA, 2013). This criterion captures risky behaviors that emerge following a Criterion A traumatic event (APA, 2103).

A growing number of studies have begun to link MST to specific risky behaviors, including substance misuse (Fillo et al., 2018; Goldberg et al., 2019), legal problems (Backhaus et al., 2016), disordered eating (Blais et al., 2017), and suicidal behaviors (Bryan et al., 2015; Monteith et al., 2019). Yet a recent review of this literature indicated that findings regarding some risky behaviors (e.g., substance use) were mixed; further, other risky behaviors remain underexamined (e.g., high-risk sexual behavior) or completely neglected (e.g., gambling; Forkus et al., 2020). These behaviors may also have severe repercussions for veterans and their loved ones and thus are also critical to be aware of. Thus, research is needed to further clarify and advance our understanding of the MST-risky behavior relation, particularly as it relates to behaviors that have received less research and clinical attention.

Further, although MST can comprise a traumatic experience that may increase the likelihood of engaging in risky behaviors, not all MST types necessarily reflect Criterion A trauma. Previous research has also emphasized the importance of distinguishing between MSH and MSA, given important differences in MST sequelae observed for survivors of MSH vs. MSA (Blais et al., 2019; Monteith et al., 2016; Monteith et al., 2019). In particular, although both MSH and MSA are associated with adverse outcomes, MSA is associated with more severe clinical outcomes (Blais et al., 2019; Monteith et al., 2016). Additionally, MSH is more prevalent than MSA (Wilson, 2018). Thus, distinguishing between MSH and MSA is important for identifying unique determinants and consequences of these experiences to inform targeted prevention/intervention efforts. Nonetheless, research on MST and risky behaviors has rarely differentiated between MST types or directly compared risk for these behaviors by MST type (Forkus et al., 2020).

In light of these considerations, the current study sought to examine whether different MST types were associated with risky behaviors, both overall as well as in regard to specific risky behaviors. Consistent with previous work, we hypothesized that individuals with a history of MSA (vs. no MST [i.e., neither MSH or MSA]) and vs. MSH only) and those with a history of MSH (vs. no MST), would report greater levels of risky behavior engagement, both overall and for specific types of risky behaviors.

Methods

Procedure/Participants

An online survey was developed on the Qualtrics data collection platform and posted on Amazon’s Mechanical Turk (MTurk). MTurk is a crowdsourcing platform that allows researchers to post human intelligence tasks (HITS), such as an online survey, to be completed by participants registered to the site. MTurk has been shown to generate data that is reliable and closely resembles the general population in terms of demographics (Shapiro, Chandler, & Mueller, 2013). For the current study, individuals were eligible to participate if they: 1) were at least 18 years old, 2) were living in North America, 3) had working knowledge of the English language (i.e., indicated ability to fluently read, write, and speak in English), and 4) self-identified as a veteran of the U.S. military. Individuals who met all eligibility criteria were able to provide informed consent and complete the online survey. Participants were compensated for participation. Procedures were approved by the Institutional Review Board at the University of Rhode Island.

Consistent with recommendations for ensuring the quality of MTurk data (Hauser et al., 2018; Peer et al., 2014), participants were only able to participate if they had a >95% approval rating and correctly responded to validity checks embedded throughout the survey. Four validity checks were included to assess for attentive responding/comprehension (Meade & Craig, 2012), and an additional two questions were included to verify military status. Specifically, in addition to self-identifying as a U.S. military veteran, participants had to correctly respond to two questions that assessed their military knowledge that have been used in previous research to differentiate between military and non-military MTurk participants: “What is the acronym for the locations where final physicals are taken prior to shipping off for basic training?” and “What is the acronym for the generic term the military uses for various job fields?” (Lynn & Morgan, 2016).

A total of 2,644 individuals attempted to complete the HIT. We excluded 997 respondents who failed to meet eligibility criteria (remaining n = 1,647). We then removed 899 who failed to pass the attention validity checks and 134 who failed to correctly respond to the military knowledge questions. Lastly, 79 individuals were removed for answering the survey more than once. Thus, the sample comprised 535 individuals. An additional 23 participants were missing more than 30% of item-level data for primary measures and were removed from the analytic sample for the current aims. As a result, the final analytic sample contained 512 participants.

Measures

Military Sexual Trauma.

MST was assessed with the standard VA MST Screening Items, which was used to assess experiences of MSH (“While you were in the military…Did you ever receive uninvited or unwanted sexual attention, such as touching, cornering, pressure for sexual favors, or verbal remarks?”) and MSA (“While you were in the military…Did someone ever use force or the threat of force to have sex against your will?”). These two items have been used extensively in past research and are psychometrically sound (Mengeling et al., 2019). A three-level grouping variable was created to capture the most severe type of MST experienced (i.e., no MSH or MSA, MSH only, and MSA). Three mutually exclusive groups were coded to represent those who denied experiences of MSA or MSH (i.e., no MST group), endorsed sexual harassment only (i.e., MSH group), and endorsed MSA only or endorsed both MSA and MSH (i.e., MSA group). Only a small number of individuals endorsed MSA only (n = 8), so they were combined with those who endorsed both MSA and MSH. Thus, the grouping reflects the most severe MST type endorsed on the MST screen. This approach is consistent with past research (Blais et al., 2019; Monteith et al., 2021).

Risky Behavior.

The Posttrauma Risky Behaviors Questionnaire (PRBQ; Contractor et al., 2020) is a 16-item self-report measure that assesses the extent of post-trauma risky behaviors in the past month and is intended to assess the PTSD E2 criterion. Participants are provided a list of fourteen behaviors that individuals may engage in following a very stressful or traumatic experience and then indicate how often they have engaged in each behavior in the past 30 days, with response options ranging from 0 (never) to 4 (very frequently). The current study administered the first 14 PRBQ items that assess engagement in specific risky behaviors, including problematic alcohol use (e.g., binge drank, which is having 4 or more drinks a day for women or 5 or more drinks a day for men; used alcohol in dangerous situations, such as driving), drug use (e.g., used drugs such as marijuana, heroin, or cocaine; took prescription drugs such as opioids or stimulants other than prescribed; used drugs in dangerous situations, such as driving), gambling (e.g., difficulty limiting money or time spent on gambling, such as playing cards or dice or betting on sports; inability to stop gambling despite significant financial losses), technology use (e.g., texting while driving; inability to reduce time on cellphones/internet/etc. despite significant problems in school, work, home, or with family/friends), risky sexual behaviors (e.g., unprotected sex with an unfamiliar partner such as a one-night stand, or with a risky partner such as someone who uses intravenous drugs, is HIV-positive, or has multiple partners), eating behaviors (e.g., eating so much that your stomach hurts; vomiting, fasting, exercising far too much, or using laxatives/diuretics to prevent weight gain; restricting your diet to the point of starvation), illegal behaviors (e.g., shoplifting, vandalism, forgery, weapons offense, burglary, robbery, arson, illegal prostitution), reckless spending (e.g., spending sprees involving spending a lot of money on things not needed and not affordable), physically aggressive behaviors (e.g., pushing, punching, kicking, or using a weapon on another person), verbally aggressive behaviors (e.g., yelling, screaming, or swearing at another person; threatening to physically harm someone), property destruction (e.g., deliberately smashing dishes, breaking furniture, or wrecking someone’s cellphone, car, home, or other personal belongings), reckless driving (e.g., road rage; intentionally cutting off or chasing another driver; excessive speeding), non-suicidal self-injury (e.g., cutting, biting, burning, hitting, or scratching oneself, punching walls, banging one’s head, interfering with wound healing, or swallowing dangerous substances), and suicidal behavior (e.g., making suicide preparations or attempts such as attempting to strangle, hang, or poison oneself). A sum score was computed to capture the extent of overall risky behavior engagement; higher scores indicate more engagement in risky behaviors. Further, each item was considered separately to examine specific risky behaviors. Consistent with Contractor et al. (2020) the final two items were not included in the sum score as they are supplemental items that measure functional impairment and onset of the worst trauma. The 14 items of the PRBQ have good construct validity, convergent validity and excellent internal consistency (Contractor et al., 2020; Contractor et al., in press), including high internal reliability in the current study (α = .97).

Covariates.

Covariates included age, gender, number of deployments, combat involvement (e.g., one question that asked participants to indicate “yes” or “no” to whether they were involved in combat operations), and total number of lifetime traumas (Life Events Checklist for DSM-5; LEC-5; Weathers et al., 2013). The LEC-5 is a 17-item self-report measure of lifetime trauma. Participants indicated their exposure to each event on a 6-point scale: happened to me, witnessed it, learned about it, part of my job, not sure, and does not apply. Trauma exposure consistent with the DSM-5 Criterion A involved endorsement of any of the first four response options (American Psychiatric Association, 2013). A sum score was used to compute total number of lifetime traumas. These covariates were included given their associations with risky behavior in prior research (Ben-Zur & Zeidner, 2009; Byrnes, Miller, & Schafer, 1999; Martin et al., 2004). Further, we included lifetime traumatic experiences as a covariate to control for the effects of other trauma that may contribute to post-trauma risky behaviors.

Data Analyses

Preliminary analyses were conducted to examine assumptions of normality, homogeneity of error variances for each dependent variable, linearity between each pair of dependent variables, and multicollinearity. All assumptions of normality (skewness < 2 and kurtosis < 1), homogeneity of error variances for each dependent variable, linearity between each pair of dependent variables, and multicollinearity (<.90 correlations between dependent variables) were met.

We conducted an analysis of covariance (ANCOVA) and a multivariate analysis of covariance (MANCOVA), with three levels (no MST, MSH, and MSA), with dependent variables as the overall PRBQ score (ANCOVA) or the different types of PRBQ risky behaviors (MANCOVA). As described above, based on prior research, covariates included age, gender, total number of lifetime traumas, and combat involvement. We used partial eta square estimates (ηp2) for effect size (values of .01, .06, and .14 represent small, medium, and large effect sizes, respectively; Richardson, 2011). Post-hoc tests using Tukey’s test were utilized to examine pairwise differences across the groups and computed Cohen’s d effect size estimates (wherein 0.2, 0.5, 0.8 represents a small, medium, and large effects, respectively; Cohen, 1988). To account for the number of outcomes and limit Type 1 error, the Benjamini-Hochberg adjustment was used (Benjamini & Hochberg, 1995).

Results

The majority of participants were men (71.5%), had served in the Army (64.0%), had deployed (76.7%), and had been involved in combat operations (68.5%). Almost a quarter of the sample reported experiencing MST (n = 111, 22.8%), with 45 (9.2%) reporting MSH and 66 (13.6%) reporting MSA as their most severe type of MST experienced (see Table 1).

Table 1.

Sample Characteristics

Full Sample (n = 512)
Variables Mean (SD)
Age 41.36 (11.41)
n (%)
Gender
  Female 143 (27.9%)
  Male 366 (71.5%)
  Female to Male Transgender 2 (0.4%)
Race
  White 365 (71.3%)
  Black 110 (21.5%)
  Asian 25 (4.9%)
  American Indian/Alaska Native 21 (4.1%)
  Native Hawaiian/Other Pacific Islander 6 (1.2%)
  Not Listed 5 (1.0%)
Ethnicity
  Hispanic or Latino/a 115 (23.0%)
  Not Hispanic or Latino/a 386 (77.0%)
Employment Status
  Employed Full-time 433 (84.6%)
  Employed Part-time 47 (9.2%)
  Not in Labor Force (student, homemaker) 15 (2.9%)
  Unemployed 12 (2.3%)
Branch of Service
  Army 328 (64.0%)
  Navy 47 (9.2%)
  Air Force 101 (19.6%)
  Marines 32 (6.3%)
  Coast Guard 6 (1.2%)
Number of Deployments
  None 115 (23.3%)
  One 136 (27.6%)
  Two 110 (22.3%)
  Three or more 151 (26.8%)
Involved in Combat Operations
  Yes 244 (68.5%)
   No 112 (31.5%)
MST
  None (i.e., no MSH or MSA) 376 (77.2%)
  MSH 66 (13.6%)
  MSAa 45 (9.2%)
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Note. MST = Military sexual trauma; MSH = Military sexual harassment; MSA = Military sexual assault.

a

Includes those who indicated they experienced MSA (irrespective of whether they also experienced MSH).

Group Comparisons

The results of the ANCOVA, with three levels (no MST, MSH, and MSA) and the overall PRBQ score as the dependent variable, provided support for group differences in total number of risky behaviors based on MST type, adjusting for covariates. ANCOVA results indicated a statistically significant difference between the groups, F (2, 334) = 26.94, p < .001; ηp2= .14. Post-hoc tests using Tukey’s test indicated that veterans who had experienced MSA reported a statistically greater mean number of total risky behaviors than those who experienced MSH only (p <.001) or no MST (p <.001). There were no statistically significant difference in total risky behavior engagement between those who reported MSH and no MST (p > .05).

Next, we conducted a MANCOVA with three levels (no MST, MSH, and MSA) and the fourteen different types of PRBQ risky behaviors as the dependent variables. Results provided support for mean differences in each risky behavior type based on MST type, adjusting for relevant covariates, with a statistically significant difference between the groups; F (28, 598) = 2.86, p < .001, Wilks Lambda = .78; ηp2= .12. Post-hoc tests using Tukey’s test indicated that the MSA group reported higher scores across all risky behavior types compared to the MSH only and no MST groups. There were no statistically significant differences in risky behavior engagement found between those who reported MSH and no MST (p > .05; see Table 3).

Discussion

Although some studies have examined if MST is associated with specific types of risky behaviors (e.g., substance use), to date, no studies have comprehensively examined if MST is associated with a broad range of risky behaviors (Forkus et al., 2020). In addition, prior research in this area has rarely differentiated the specific type of MST, despite research suggesting that MSA survivors tend to report worse health outcomes than survivors of MSH (Blais et al., 2019; Monteith et al., 2016). Our findings add to this literature by further supporting the importance of differentiating between MST types, as well as examining a broad range of risky behaviors.

Compared to veterans who reported MSH only or no history of MST, veterans with a history of MSA reported greater past-month risky behavior engagement, both overall and for all of the specific risky behaviors examined. This included problematic alcohol use, drug use, gambling, technology use, risky sexual behaviors, eating behaviors, illegal behaviors, reckless spending, physically and verbally aggressive behaviors, property destruction, reckless driving, non-suicidal self-injury, and suicidal behavior. Moreover, these associations were significant when accounting for age, gender, combat involvement, and number of lifetime traumatic experiences. These findings provide further support for established links between MST and substance (i.e., alcohol and drug) misuse (Fillo et al., 2018; Yalch et al., 2018), disordered eating (Blais et al., 2017), risky sexual behavior (Strauss et al., 2011), non-suicidal self-injury, and suicidal behavior (Bryan et al., 2015; Monteith et al., 2019). Extending such findings, we differentiated between MST types, finding that risky behaviors were associated with a history of MSA (vs. MSH and no MST); however, we found no statistically significant difference between those with a history of MSH relative to those who reported no MST. We also found significant relations between MSA and several unexplored risky behaviors, such as gambling, problematic technology use, reckless spending, physically and verbally aggressive behaviors, property destruction, and reckless driving.

Thus, establishing links between MSA and these traditionally underexamined behaviors is essential, as these behaviors are associated with negative consequences across financial, legal, safety, and health domains (e.g., Blows, Ameratunga, Ivers, Lo, & Norton, 2005; Contractor, Frankfurt, Weiss, & Elhai, 2017; Shaffer, & Korn, 2002; Sumitra & Miller, 2005). Further, given that risky behaviors commonly co-occur, these behaviors may be intensified in the context of other behaviors (e.g., substance use; Pulay et al., 2008), potentially leading to worse outcomes (e.g., harm to self or others). Assessing for the presence of these behaviors is necessary for comprehensively understanding post-trauma behaviors that may be maintaining or exacerbating mental and physical health, as well as psychosocial outcomes. For instance, using risky behaviors to modulate and/or avoid MST sequelae may reinforce maladaptive patterns of coping that can lead to poorer long-term outcomes. Moreover, due to the lack of research in this area, these behaviors may be overlooked in clinical settings, potentially resulting in the continuation of these harmful behaviors and interfering with treatment efficacy and overall well-being.

Healthcare providers can use results from this study to incorporate more comprehensive screening for risky behaviors among veterans who report a history of MSA in treatment settings. To our knowledge, comprehensive screening for risky behaviors is not routine in clinical settings. Incorporating these practices could help identify harmful risky behaviors that may otherwise be overlooked. Although providers may currently assess for a wide range of behaviors in trauma/PTSD assessment (e.g., substance use, suicide risk), many risky behaviors associated with adverse outcomes (e.g., problematic technology use; Contractor, et al., 2017) may be missed. Thus, comprehensive assessment of a broad range of risky behaviors could help with identifying these behaviors to reduce their occurrence and associated negative outcomes. Moreover, assessing for these behaviors can help to inform intervention targets and treatment approaches, depending on the function of such behaviors. For instance, for risky behaviors used to modulate emotions, it may be beneficial to utilize Dialectical Behavioral Therapy skills to facilitate adaptive coping, distress tolerance skills, and emotion regulation. Alternately, Acceptance and Commitment Therapy could be used to determine whether risky behaviors are consistent with one’s personal values, and to promote value-consistent action (Hayes, Strosahl, & Wilson, 2009). These approaches may help to reduce impulsive or values-inconsistent behaviors, potentially mitigating the negative consequences associated with risky behavior engagement in the context of MST. Further, as a result of the potential emotion regulating function of risky behaviors, they may undermine treatment progress by maintaining ineffective and harmful ways of responding to distress. Thus, comprehensive screening for risky behaviors could help increase patient and provider awareness, informing targeted interventions to replace these behaviors with more effective ways of regulating distress.

Several limitations should be considered when interpreting these findings. First, we assessed each unique risky behavior type with a single item, which allowed us to assess whether various underexamined behaviors were associated with MST. Single-item questions have been shown to have clinical utility and to reduce participant burden (Davey, Barratt, Butow, & Deeks, 2007). Nonetheless, future research is needed to replicate and extend findings using more comprehensive measures of each risky behavior type. Second, findings only examined risky behavior engagement. Future research is needed to better understand the function of these behaviors in the context of MSA (e.g., coping with symptoms and distress). Third, definitions and measurements of MSH include experiences of unwanted sexual contact that could also meet definitions for military sexual assault. Specifically, experiences of unwanted sexual contact that do not meet legal definitions of sexual assault (e.g., unsolicited back rubs) are categorized as sexual harassment in the military. This may pose some measurement challenges, as respondents may choose to define similar experiences of unwanted sexual contact differently. However, an individual’s perception and categorization of their experience as sexual assault vs. harassment may have important implications for MST sequelae. Future research should examine these specific types of MSH and MSA in relation to health outcomes to further examine their differential influence. Fourth, the cross-sectional design limits the ability to make causal inferences about examined relations. Future research is needed to examine the nature and direction of these associations using prospective and longitudinal designs. Fifth, the exclusive reliance on self-report measures may introduce bias. For instance, respondents may feel compelled to respond to questions in socially desirable ways, such as denying risky behaviors that are socially stigmatized (e.g., risky sexual behaviors). Future research could include control questions to assess for response bias, as well as include behavioral measures to assess for risk-taking tendencies more broadly. Sixth, the use of an online recruitment platform required individuals to self-select to participate; therefore, there may be characteristics that systematically differ between those who did (vs. did not) participate. Lastly, the percentage of participants excluded for failing to respond to validity checks is higher than what has been found in other studies (van Stolk‐Cooke et al., 2018), which may be due to the greater number of validity checks, as well as more sophisticated attempts of respondents trying to surpass validity checks, in the present study. Findings should be replicated using diverse recruitment strategies.

Despite these limitations, results of the current study extend our understanding of the association between MST and risky behaviors. Specifically, findings advance our current understanding of the MST-risky behaviors relation by providing novel support for the link between MSA and a wide range of previously unexplored risky behaviors. Further, our findings emphasize the importance of differentiating between MST types in detecting and intervening with military veterans at risk for engaging in risky behaviors. Considering the potentially serious consequences associated with risky behaviors, these findings have important implications for research and practice related to MSA. Findings highlight the importance of assessing for a wide range of risky behaviors to facilitate prevention and intervention and reduce the negative consequences associated with risky behaviors among survivors of MSA.

Table 2.

Do Veterans who have Experienced MSH or MSA Report Engaging in More Risky Behaviors?

No MST
(n = 376)		 MSH
(n = 45)		 MSA
(n = 66)
Outcome M (SE) M (SE) M (SE) Class Comparisons F (2, 458) ηp2
Problematic alcohol use 1.00 (0.08) 0.74 (0.23) 2.02 (0.19) 3>1*(Cohen’s d = 0.97)
2<3*(Cohen’s d = 1.22)		 13.39 .08
Problematic drug use 0.78 (0.07) 0.56 (0.22) 2.16 (0.18) 3>1*(Cohen’s d = 1.27)
2<3*(Cohen’s d = 1.51)		 26.09 .14
Problematic gambling 0.78 (0.08) 0.41 (0.23) 1.87 (0.19) 3>1*(Cohen’s d = 1.10)
2<3*(Cohen’s d = 1.47)		 16.68 .10
Problematic technology use 1.13 (0.08) 0.98 (0.24) 2.07 (0.20) 3>1*(Cohen’s d = 0.92)
2<3*(Cohen’s d = 1.07)		 9.62 .06
Impulsive/risky sexual behaviors 0.79 (0.07) 0.97 (0.21) 1.77 (0.17) 3>1*(Cohen’s d = 1.10)
2<3*(Cohen’s d = 0.89)		 13.10 .08
Problematic eating behavior 0.96 (0.08) 0.79 (0.23) 2.27 (0.19) 3>1*(Cohen’s d = 1.19)
2<3*(Cohen’s d = 1.35)		 20.31 .12
Illegal behaviors 0.67 (0.07) 0.53 (0.22) 1.99 (0.18) 3>1*(Cohen’s d = 1.22)
2<3*(Cohen’s d = 1.39)		 23.77 .13
Reckless spending 0.89 (0.08) 0.79 (0.22) 1.97 (0.19) 3>1*(Cohen’s d = 1.06)
2<3*(Cohen’s d = 1.20)		 14.61 .09
Physically aggressive behavior 0.76 (0.08) 0.62 (0.22) 1.93 (0.18) 3>1*(Cohen’s d = 1.14)
2<3*(Cohen’s d = 1.40)		 18.29 .11
Verbally aggressive behavior 0.91 (0.07) 1.03 (0.21) 1.95 (0.18) 3>1*(Cohen’s d = 1.04)
2<3*(Cohen’s d = 0.93)		 14.15 .08
Property destruction 0.72 (0.07) 0.55 (0.22) 1.88 (0.18) 3>1*(Cohen’s d = 1.12)
2<3*(Cohen’s d = 1.42)		 18.38 .11
Reckless driving 0.84 (0.08) 0.62 (0.23) 2.08 (0.19) 3>1*(Cohen’s d = 1.16)
2<3*(Cohen’s d = 1.42)		 19.76 .11
Non-suicidal self-injury 0.66 (0.07) 0.75 (0.21) 1.86 (0.17) 3>1*(Cohen’s d = 1.21)
2<3*(Cohen’s d = 1.09)		 20.13 .11
Suicidal behaviors 0.73 (0.07) 0.59 (0.22) 1.99 (0.18) 3>1*(Cohen’s d = 1.21)
2<3*(Cohen’s d = 1.37)		 21.82 .12
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Note. All findings were significant after applying the Benjamini-Hochberg adjustment. Findings are adjusting for age, gender, total number of traumas, total number of deployments, and combat involvement. Estimated marginal means are reported to account for the covariates in the model. Cohen’s d calculated using standard deviation. MSH = Military sexual harassment; MSA = Military sexual assault.

Acknowledgments

This material is based upon work supported in part by the Department of Veterans Affairs (VA) and the Rocky Mountain MIRECC for Suicide Prevention. The views expressed are those of the authors and do not necessarily represent the views or policy of the VA or the United States Government. Work on this paper by the second author (NHW) was supported, in part, by National Institute on Drug Abuse grant K23DA039327.

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