Predictors of Academic Distress Among Military and Nonmilitary College Students

Data from the Center for Collegiate Mental Health (CCMH, 2021) indicate that college students are at risk for psychological concerns (e.g., anxiety, depression, trauma) that can negatively influence their academic performance (Brackney & Karabenick, 1995) and lead to feelings of academic distress. Academic distress refers to concerns students have about “their academic motivation, confidence, concentration, enjoyment, and ability to complete coursework” (Lockard et al., 2012, p. 243). Academic distress has significant implications for college students as it may interfere with their degree completion (Fleming et al., 2018) and employment goals (Beiter et al., 2015). Beiter et al. (2015) found that college students reported academic performance, pressure to succeed, and postgraduation plans as primary concerns. As more students enter college and compete for a limited number of employment opportunities (Uno et al., 2010), it is possible they will feel pressure to excel academically, leading to feelings of academic distress. Because academic distress is a common issue among college students, understanding this construct in a university counseling center (UCC) is important for guiding treatment.

Previous research suggests different subgroups of college students (e.g., military students, students with disabilities) experience higher rates of academic distress (Johnson et al., 2014). For this study, we focused on military students, who are individuals who have served in any branch of the U.S. military (i.e., active duty, veteran, National Guard, or reserves; CCMH, 2009) and are currently enrolled in an institution of higher education. Although we use the term military students in reference to our study, we use the terms scholars have used in their studies when discussing results from previous research (e.g., student veterans, student service members). Military students represent a specific subgroup with experiences that may differ substantially from most college students, such as combat‐related stressors, deployments, and prolonged separation from family and friends (Jakupcak et al., 2010; Maguen et al., 2010). These experiences may inform military students’ academic success and adjustment (Grossbard et al., 2015). Understanding the potential link between military students’ mental health concerns and academic adjustment may improve their academic outcomes. Taking into consideration military background may require an understanding of different constructs that may influence academic distress when compared with nonmilitary students (Johnson et al., 2014). The purpose of this study was to investigate mental health factors that predict academic distress for military and nonmilitary students seeking counseling services.

This study adds to existing literature about what mental health factors contribute to military and nonmilitary college students’ academic distress and whether there are differences in predictors of academic distress between these two populations. By investigating specific mental health concerns, mental health professionals can tailor interventions to meet the needs of military and nonmilitary students. Findings can also be used to develop and implement interventions that promote academic success for both military and nonmilitary students.

College Students’ Academic Distress

Previous research has focused on measuring academic distress, with researchers noting that measures of performance (e.g., grade point average [GPA]) may not be sufficient to capture students’ perceptions of academic functioning (Lockard et al., 2012). Several researchers have utilized academic functioning and adjustment to measure distress (Choi et al., 2010), whereas others have focused on achievement, motivation, and confidence (Locke et al., 2011, 2012). Choi et al. (2010) investigated how engaging in counseling services at a UCC helped clients cope with their presenting concerns and academic functioning. Their findings suggested clients who completed the intake and termination surveys reported higher levels of personal and academic functioning.

Lockard et al. (2012) also examined academic distress over the course of one semester with clinical (e.g., UCC and training clinic clients) and nonclinical (e.g., psychology students) samples utilizing the Counseling Center Assessment of Psychological Symptoms (CCAPS) 62‐ and 34‐item versions. Results indicated there was a significant decrease in the mean score for the Academic Distress subscale of the CCAPS‐34 for the clinical sample over six sessions (Lockard et al., 2012). The change in Academic Distress scores for the clinical sample exceeded the change in the nonclinical sample, suggesting that engaging in counseling can reduce academic distress.

In summary, some research supports the notion that college students who engage in counseling may have reduced levels of academic distress (Choi et al., 2010; Lockard et al., 2012). However, none of these studies examined the intersections of mental health concerns, academic concerns, and military status with military students seeking counseling services. This significant gap in the literature warrants further investigation to inform effective treatment of mental health concerns.

Military and Nonmilitary Students in Counseling

With the passage of the latest GI Bill (Harry W. Colmery Veterans Educational Assistance Act, 2017), it is likely that institutions of higher education will continue to experience increasing numbers of military students on campus (McBain et al., 2012; U.S. Department of Veterans Affairs [VA], 2019). Although more military students may utilize these benefits to pursue higher education, they are at risk for dropping out of college prematurely (Wood, 2012), partly due to difficulties navigating their mental health concerns. Because of the variety of services they offer students (e.g., short‐term individual counseling, group therapy, outreach services), universities and UCCs in particular are in vital positions to help military students address their mental health concerns and improve their academic adjustment.

Recent research has focused on military students’ academic adjustment and success, defining academic functioning in terms of educational self‐efficacy (e.g., confidence in academic performance), academic motivation (e.g., why the student is attending college), and persistence (e.g., likelihood of remaining in school; Whiteman et al., 2013). These aspects of academic functioning align well with the CCAPS‐34's Academic Distress subscale, which assesses students’ motivation, concentration, confidence, ability, and enjoyment with four items (CCMH, 2009).

In a study examining posttraumatic stress among 131 student service members/veterans, Barry et al. (2012) found that although posttraumatic stress was negatively associated with GPA, it was unrelated to educational self‐efficacy and academic motivations for those exposed to combat trauma. Campbell and Riggs (2015) also examined the effects of psychological distress (e.g., generalized anxiety, depression, posttraumatic stress disorder [PTSD]) and social support on the academic adjustment of 117 previously deployed student veterans. Findings suggested generalized anxiety had a negative effect on student veterans’ academic adjustment, meaning symptoms typically associated with generalized anxiety (e.g., worry, concentration difficulties) may impede their ability to study, complete coursework, and attend classes. Research also suggests military students diagnosed with PTSD report more hostility and difficulties in interpersonal relationships (Johnson et al., 2014), experience more alcohol‐related problems (Barry et al., 2014), and report greater alienation on campus when compared with nonmilitary students without a PTSD diagnosis (Elliott et al., 2011). Furthermore, findings from Rudd et al.'s (2011) study with veterans from the Iraq and Afghanistan conflicts suggested that 35% of military students endorsed high levels of anxiety and 25% reported high levels of depression. Findings from these studies suggest that mental health concerns negatively affect military students’ academic outcomes and interpersonal relationships.

In addition to mental health concerns, student veterans in DiRamio et al.'s (2008) qualitative study reported that their most difficult adjustment was the transition from military to college. Participants shared how they needed to relearn study skills, experienced difficulties connecting with nonveteran peers, and worried about the financial costs of college, all of which contributed to academic adjustment concerns. Similarly, military students who have been previously deployed may experience disruptions in their academic careers (Hayden et al., 2014) and may have to withdraw from courses and reenroll when their deployments are completed. This can contribute to financial aid delays and falling behind academically (DiRamio et al., 2008). In a study including over 800,000 military and military‐affiliated students, findings suggested that although military students earned postsecondary degrees (e.g., vocational training to doctorate) at rates similar to civilian peers (51.7%), it took them longer to earn their degree (Cate, 2014). This increase in completion time could be a result of military students’ academic challenges, including withdrawing from courses due to deployments, reenrolling in academic credits after deployments, or transferring colleges, which may lead to feelings of academic distress (Barry et al., 2014; Hayden et al., 2014).

In summary, previous research indicates there are links between specific mental health concerns such as PTSD, anxiety, and substance use and military veterans’ academic outcomes, with lower GPAs and academic adjustment. Research also suggests that military students experience concerns related to transitioning into higher education that negatively affect their academic functioning. However, further research is needed on how these concerns predict military students’ academic distress.

Current Study

Although researchers have examined how academic distress may decrease through counseling and how military students may have unique experiences that may require differential attention to how concerns are addressed in counseling, there has not been a comprehensive examination of mental health predictors of academic distress in military and nonmilitary college students. Additionally, given the unique experiences and potential mental health difficulties specific to military students, it is possible that military status moderates which variables predict academic distress.

The purpose of the current study was to (a) examine the relationship between common mental health concerns and academic distress and (b) determine whether military status acts as a moderator for the relationships between common mental health concerns and academic distress. As predictors of mental health concerns, we used the Alcohol Use, Depression, Eating Concerns, Hostility, Generalized Anxiety, and Social Anxiety subscales of the CCAPS‐34. Based on previous research suggesting negative relationships among mental health concerns and academic adjustment (e.g., Campbell & Riggs, 2015; DiRamio et al., 2008; Grossbard et al., 2014), we hypothesized these predictors would differ across military and nonmilitary students in counseling and that military status would moderate the relationships between these predictors and academic distress.

Method

Procedure

For this study, we used client data from UCCs for the 2014–2015 and 2015–2016 academic years. The data were collected by CCMH (2014–2016), an organization that pools standardized data from UCCs across the United States to assess college students’ mental health concerns. From 2014 to 2016, 156 UCCs contributed to the data set, providing data from 174,120 clients who completed the CCAPS‐34 at their first session. Our study was reviewed by the institutional review board at a large public university and was not considered human subjects research due to the use of secondary data.

Participants

Military status was determined through a “yes” response to “Have you ever served in any branch of the U.S. military?” on the Standardized Data Set (SDS; CCMH, 2017). Of the total sample, 2,836 individuals reported having military experience. The average age for this sample was 27.51 years (SD = 7.42, range = 18–60).

We selected a comparison sample of students without military experience from the rest of the total sample. Each individual from the military sample was matched to an individual from the nonmilitary sample on the basis of age (categorized as under 20, 20–21, 22–24, 25–29, 30–39, and 40 or over), academic status (e.g., freshman, sophomore), gender identity (e.g., woman, man, transgender), and race/ethnicity (e.g., White, African American, Asian American).

All but 10 of the 2,836 military students were successfully matched with a nonmilitary student on age, gender identity, academic status, and race/ethnicity. The 10 military students who were not matched on all four characteristics were matched to nonmilitary students by gender identity and age only. The nonmilitary sample also had 2,836 individuals, and the average age was 27.19 years (SD = 7.25, range = 18–59).

Measures

Demographics. Demographic information was captured by the SDS, which was created by UCCs to gather clients’ self‐reported information on variables such as age, gender, race/ethnicity, academic status, and military status (CCMH, 2009). Table 1 shows demographic information for the matched pairs.

Table 1

Demographic Characteristics of Military and Nonmilitary Students

Note. N = 2,836 matched pairs. Most students were matched by age (not shown), gender identity, race/ethnicity, and academic status; however, 10 were matched on age and gender identity only.

Assessment of psychological symptoms. The CCAPS‐34 (Locke et al., 2011) is a brief, self‐report assessment of college student distress that includes seven subscales: Academic Distress, Alcohol Use, Depression, Eating Concerns, Generalized Anxiety, Hostility, and Social Anxiety. Items are rated using a 5‐point, Likert‐type scale of responses ranging from 0 (not at all like me) to 4 (extremely like me). Scale development, reliability, and validity evidence for the CCAPS‐34 has been examined in the general college population (Locke et al., 2011, 2012). The CCAPS‐34 has demonstrated adequate reliability, with Cronbach's alphas for individual scales ranging from .76 to .89 and convergent validity, with scales correlating most highly with hypothesized referent measures, coefficients ranging from .52 to .78 (Locke et al., 2012). The factorial invariance of the CCAPS‐34 between military and nonmilitary students has also been supported in previous research (Ghosh et al., 2021). In the current samples, Cronbach's alphas for nonmilitary and military samples, respectively, were .81 and .82 for Academic Distress; .83 and .82 for Alcohol Use; .87 and .88 for Depression; .88 and .88 for Eating Concerns; .84 and .84 for Generalized Anxiety; .83 and .86 for Hostility; and .83 and .82 for Social Anxiety.

Data Analysis

Single‐group confirmatory factor analysis (CFA), multigroup CFA, and multigroup structural equation modeling (SEM) were used for this study. Items from the CCAPS‐34 were used as indicators for seven latent factors measuring Academic Distress, Alcohol Use, Depression, Eating Concerns, Generalized Anxiety, Hostility, and Social Anxiety. Items were treated as ordinal‐level data for the current study. The weighted least squares with means and variances adjusted (WLSMV) estimation was used for all models. Mplus (Version 7.4; Muthén & Muthén, 1998–2012) was used for all analyses.

Single‐group models. The CCAPS‐34 items were fit to a correlated‐factors measurement model separately for nonmilitary and military samples to evaluate general model fit for each group. Model parameters were examined to ensure factor loadings were adequate and model parameters were viable within each group.

Factorial invariance. To compare latent means and structural parameters across groups (i.e., regression coefficients between latent variables), it is necessary to establish factorial invariance of factor loadings and item thresholds (Widaman & Reise, 1997). Multigroup CFA was used for factorial invariance tests. First, a configural model with the same pattern of factor loadings across nonmilitary and military samples was estimated and parameters were freely estimated within each group. Next, cross‐group equality constraints were added to factor loadings and item thresholds. A substantial degradation in model fit (e.g., change in comparative fit index [CFI] of less than –.002) would suggest factor loadings and/or item thresholds are not equal across groups (Widaman & Reise, 1997).

Structural model. If factorial invariance was tenable across nonmilitary and military samples, we used multigroup SEM to examine the influence of Alcohol Use, Depression, Eating Concerns, Generalized Anxiety, Hostility, and Social Anxiety on Academic Distress. In this model, all factor loadings and item thresholds are constrained to be equal across groups. Academic Distress was regressed on Alcohol Use, Depression, Eating Concerns, Generalized Anxiety, Hostility, and Social Anxiety. First, these structural parameters were freely estimated across nonmilitary and military samples. Then, cross‐group equality constraints were added to the structural parameters. A statistically significant degradation in model fit would suggest that the structural parameters are different across military and nonmilitary samples, and that military status moderates the influence of common mental health concerns on academic distress.

Although multiple methods are available for testing moderation (e.g., including interaction terms between group membership and other independent variables in multiple regression; Keith, 2019), in SEM the use of multiple group analysis with equality constraints provides several advantages. First, the use of latent variables is advantageous because they model only the shared variance among items, removing measurement error and resulting in better estimation of relationships among constructs of interest. Second, the use of latent variables allows researchers to explicitly test the equivalence of the factor structure and model parameters across groups. This helps ensure that the independent and dependent variables are being measured in the same manner. Finally, the inclusion of equality constraints on structural parameters (e.g., regressing Academic Distress on all other CCAPS‐34 constructs) examines whether the slopes between the independent and dependent variables are different across groups, which is the primary test of moderation. These constraints can be included in a single test, allowing for an omnibus test of whether there are any differences in structural parameters in the model across groups. Overall, this approach provides a powerful, yet straightforward, method for testing whether structural parameters are similar across groups.

Model Fit

We evaluated models using the WLSMV chi‐square difference test, CFI, Tucker‐Lewis index (TLI), and root‐mean‐square error of approximation (RMSEA). Values for chi‐square are highly sensitive to sample size and were expected to be statistically significant as a standalone fit index given the sample sizes in this study (n = 2,836 for each group). Standard criteria for other fit indices were used to assess model fit (Schermelleh‐Engel et al., 2003). For the CFI and TLI, values from .95 to .97 suggest good fit and values greater than .97 suggest excellent fit. For the RMSEA, values from .05 to .08 suggest good fit and values less than .05 suggest excellent fit.

For model comparisons, we used the change in chi‐square. A direct comparison of chi‐square values between nested models is not possible when using WLSMV estimation. In Mplus, the DIFFTEST option was used to compare nested models (Muthén & Muthén, 1998–2012). The change in chi‐square is very sensitive to sample size and is likely to be statistically significant even with trivial sources of misfit. Other criteria were also used to examine change in model fit, specifically the change in CFI. Some researchers have suggested a change in CFI of less than –.01 suggests noninvariance (Cheung & Rensvold, 2002), whereas others have suggested a change in CFI of less than –.002 is more appropriate (Meade et al., 2008). For the current study, we used a change in CFI of less than –.002 when examining factorial invariance.

Results

Missing Data

There was a small amount of missing data in the samples. Little's (2003) test for data missing completely at random (MCAR) was not statistically significant in the nonmilitary sample, χ2(1883) = 1,860.98, p = .637, but was significant for the military sample, χ2(1857) = 1,995.42, p = .013. These powerful tests suggest that missing data are likely MCAR. Overall, 99.8% of item responses were present across both samples, and 93.2% and 93.4% of cases from the nonmilitary and military samples, respectively, were complete.

Single‐Group Models

Table 2 shows model fit and comparisons for all analyses. A correlated‐factors model of the CCAPS‐34 was estimated separately for the nonmilitary and military samples. All items loaded on only one of the seven CCAPS‐34 factors, and there were no correlations between item residual variances. This model fit well for the nonmilitary sample (Table 2, Model 1), but did not converge for the military sample. The nonconvergence issue was due to one item on the Alcohol Problems subscale. Previous research has suggested several residual correlations between items are necessary for adequate model fit because some items within subscales have very similar wording (Ghosh et al., 2021). Models were estimated using maximum likelihood with robust standard errors, and modification indices (MIs) were examined to determine whether some residual correlations were consistently large (MI > 200) across both groups. Six residual correlations with MI values greater than 200 were present in both groups. All were within‐construct residual correlations (i.e., only between items that loaded on the same factor) and appeared to be on account of similar wording across items. When these six residual covariances were added to the nonmilitary sample, there was a very large improvement in model fit (Table 2, Model 2). When this same model was estimated with the military sample, the model converged normally and had excellent fit (Table 2, Model 3). This model was used for all subsequent analyses.

Table 2

Goodness-of-Fit Indicators for Matched Samples (N = 2,836)

Note. WLSMV = weighted least squares with means and variances adjusted; CFI = comparative fit index; TLI = Tucker‐Lewis index; RMSEA = root‐mean‐square error of approximation; CFA = confirmatory factor analysis; SEM = structural equation modeling.

Factorial Invariance

The multigroup CFA for testing factorial invariance across nonmilitary and military samples is shown in Table 2. When cross‐group equality constraints were added to the factor loadings and item thresholds, there was a statistically significant degradation in model fit (Table 2, Model 5); however, the change in CFI (+.004) suggested an improvement in fit. The TLI and RMSEA also suggested improvements in fit with these cross‐group equality constraints. Factor loadings and item thresholds were considered invariant across groups. Comparing factor means and structural relations across nonmilitary and military samples was appropriate.

Multigroup SEM

A multigroup SEM whereby Academic Distress was regressed on Alcohol Use, Depression, Eating Concerns, Generalized Anxiety, Hostility, and Social Anxiety was estimated. This model included the cross‐group equality constraints on factor loadings and item thresholds. First, all paths were freely estimated across groups, and this model fit well (Table 2, Model 6). Next, cross‐group equality constraints were added to the structural paths and there was not a statistically significant degradation in model fit (Table 2, Model 7), suggesting the influences of Alcohol Use, Depression, Eating Concerns, Generalized Anxiety, Hostility, and Social Anxiety on Academic Distress are similar across nonmilitary and military samples.

Statistically significant predictors of Academic Distress were Depression (β = .53, p < .001), Generalized Anxiety (β = .25, p < .001), and Social Anxiety (β = –.09, p < .001). Although Eating Concerns was a statistically significant predictor of Academic Distress, the standardized path was very small (β = .04, p = .028). When these paths from Eating Concerns, Alcohol Use, and Hostility were set to zero across groups, there was not a statistically significant degradation in model fit (Table 2, Model 8). The size of the path coefficients from Depression, Generalized Anxiety, and Social Anxiety on Academic Distress were essentially unchanged when these nonstatistically significant paths were removed.

Discussion

Although researchers have examined the role of academic distress in counseling treatment, there has been limited examination of how mental health concerns predict academic distress in college students and, specifically, military students. The purpose of this study was to examine the relationship between common mental health concerns (e.g., depression, anxiety) and academic distress and to determine whether military status is a moderator for the relationships between these common mental health concerns and academic distress.

The results of this study indicate that the predictors of Academic Distress, as measured by the CCAPS‐34, do not differ across military and nonmilitary students. This finding contrasted with our original hypotheses and previous literature suggesting that predictors of academic distress may differ for military and nonmilitary students. There are several possible reasons for our initial hypotheses not being supported. First, the items on the CCAPS‐34 may not adequately capture military students’ experiences, mental health concerns, and academic distress. For instance, military status is assessed by a “yes” response to the question “Have you ever served in any branch of the U.S. military?” on the SDS. This question does not gather information on military students’ branch of service, length of service, or deployment status. These additional factors could influence military students’ mental health concerns and academic distress. For instance, a military student who engaged in deployment‐related combat might have specific symptoms (e.g., PTSD, difficulty concentrating) that could impact their academic functioning.

Similarly, previous studies have utilized different measures than the CCAPS‐34 to assess military students’ presenting concerns in treatment, including the Student Adaptation to College Questionnaire (Baker & Siryk, 1986). It is possible these measures more accurately assess military‐specific presenting concerns, including PTSD, depression, and physical injury, than the CCAPS‐34. This study is one of the few to use the Academic Distress subscale of the CCAPS‐34 instead of self‐reported GPA and academic adjustment to measure academic distress. It may be that the items on the Academic Distress subscale do not address military students’ academic development. For instance, military students may report no difficulties with motivation, confidence, concentration, enjoyment, and ability to complete coursework but still feel distressed related to their academics. Other factors, such as lack of support and lack of preparation for college, may be related to their academic distress. Although many UCCs rely on the CCAPS‐34, researchers and clinicians should be aware of the limitations of this instrument.

Although measurement concerns with military students’ symptoms may be one possible explanation for our findings, another reason could be the actual sample. Specifically, previous studies have examined military students’ mental health concerns without a comparison group (DiRamio et al., 2008) or used nonmatched samples (Ghosh et al., 2021), which may have influenced overall findings. If unmatched samples are used, it is possible that differences between military and nonmilitary (e.g., traditional‐age college students) students’ mental health symptoms would be due to demographic variables, such as age, gender, and marital status. Cleveland et al. (2015) also argued this point, finding that after matching military students to civilian students who were demographically similar on variables (e.g., age, gender, race/ethnicity), the prevalence of mental health symptoms was similar. This suggests military students may have mental health concerns and needs similar to those of the larger student population. Our findings also suggest that researchers should use similar comparison strategies because traditional‐age, nonmilitary student clients may not represent the best or only comparison group for military students’ mental health concerns.

It is also possible that military and nonmilitary students do not experience academic distress differently. This is supported by the findings of Johnson et al. (2014) and Cleveland et al. (2015). For instance, Johnson et al. found that the means for military and nonmilitary students’ academic distress were the same for both groups. Similarly, Cleveland et al. argued that student service members/veterans have the same mental health concerns as civilian students. It is possible that military students have the same levels of academic distress as nonmilitary students and that there are simply no differences between these groups.

One finding consistent with existing literature was that depression and generalized anxiety were the strongest predictors of academic distress, whereby individuals with higher levels of depression and anxiety also had higher levels of academic distress. Depression and generalized anxiety are common among military students (Rudd et al., 2011) but may present differently than with nonmilitary students. For instance, military students’ depression and anxiety may manifest as hostility, irritability, and aggression (Morland et al., 2012), and research has suggested that comorbid diagnoses of anxiety and depression among veterans are often associated with increased odds of suicide death (Shepardson et al., 2019). Counselors should complete thorough risk assessments when working with military students who report elevated levels of distress.

Implications

• Obtain additional information regarding military service. Counselors working with military students should ask questions about military experiences, including combat tours, previous coping strategies, deployment-related stressors, and social support. It may be beneficial for counselors to ask open-ended questions such as What was your experience like in the military? and Describe your mental health concerns before, during, and after your military service to obtain rich clinical information. Counselors should also ask military students about their transitional experiences, focusing on how their mental health concerns have affected their academic goals. Counselors are encouraged to take a strengths-based approach to counseling military students and not stereotype or assume their primary mental health concern is PTSD. It is crucial that clinicians take a holistic perspective when working with military students, including asking about additional responsibilities such as parental status, prior military service, and reintegration into civilian and academic life. Factors such as age and life stage, including marital status, may influence academics and should not be overlooked in treatment.
• Obtain more clinical data. Clinicians should remember that items on the CCAPS-34 are only one source of data. This information should be supplemented with additional measures and counseling questions to gather a deeper understanding of military students’ mental health, academic distress, and progress in treatment. In particular, academic distress items on the CCAPS-34 may be limited and may not fully capture military students’ academic experiences.
• Utilize military-specific resources. Counselors may benefit from using military-specific resources and toolkits designed to support individuals in higher education. These resources can offer guidance on responding to common concerns among military students, such as transitional challenges, disrupted sleep, hypervigilance, and adjustment issues. Such materials can also help counselors identify factors unique to military students, including nontraditional student status, multiple roles such as parent or caregiver, and adherence to military culture.
• Reduce barriers to treatment. Many military students may not seek counseling services due to barriers such as reluctance to engage in help-seeking behaviors and stigma related to mental health treatment. Counselors should take proactive steps to reduce these barriers by developing partnerships with campus military student resource centers and organizations. Outreach efforts specifically targeting military students may help reduce stigma and encourage help seeking when mental health support is needed.

Limitations

Although this study contributes to the scant research on military and nonmilitary students’ academic distress, it has several limitations. First, although the present study had adequate sample sizes, the samples were homogeneous in terms of racial/ethnic group membership, limiting generalizability to clients who do not identify as White and male. Replication of this study with other more diverse samples is warranted. Replication of this study with more military‐specific information is needed. For instance, future studies should include questions about military students’ time in service, postdischarge adjustment, and educational goals instead of a yes/no endorsement about military background. Second, the CCAPS‐34 is a self‐report measure, and participants could have responded in socially desirable or inconsistent ways.

Conclusion

Our findings suggest there were no differences in predictors of academic distress for military and nonmilitary students in counseling. When identifying the needs of military students, mental health professionals should consider how military students may differ from the general college population (e.g., traditional‐age college students) and acknowledge that differences among military and nonmilitary students may be due to demographic and life‐stage characteristics.