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Posttraumatic stress in U.S. marines: the role of unit cohesion and combat exposure.

The current conflicts in Iraq and Afghanistan have resulted in a number of investigations examining the postdeployment mental health among military personnel serving in these regions. Although the published rates of self-reported mental health problems among service members returning from combat deployments in support of Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF) have ranged from approximately 19% to 44% (Hoge, Auchterlonie, & Milliken, 2006; Lapierre, Schwegler, & LaBauve, 2007), few doubt that a notable prevalence of mental health issues exists in this population. Some evidence suggests that in subsets of returning service members, posttraumatic stress (PTS) symptoms continue to increase in severity across the months and years after return from combat (Grieger et al., 2006; Orcutt, Erickson, & Wolfe, 2004). Researchers have begun to look into the factors that contribute to the development of mental health conditions, both prior to and following war-zone deployment.

An understanding of these factors could be used to inform a range of topics, notably (a) a service member's suitability for deployment, (b) the mitigation of mental health problems while deployed, and (c) the identification of individuals most at risk for mental health issues on return from combat theater. The aim of the current study was to extend the research into this area by identifying variables predictive of PTS and other mental health outcomes in returning OIF military personnel. For the purposes of this study, PTS was conceptualized as the psychological (i.e., reexperiencing a trauma, avoidance of trauma-related stimuli) and physiological (i.e., exaggerated startle response, insomnia) symptoms that can occur after exposure to a traumatic event. With the available data, a diagnosis of posttraumatic stress disorder (PTSD) could not be made with an acceptable degree of accuracy; thus, PTS was used as a proxy for this anxiety-based response.

Level and intensity of combat exposure is one of the most frequently demonstrated predictors of mental health outcomes (particularly PTS) in theater veterans. For example, Miller, Wolf, Martin, Kaloupek, and Keane (2008) reported that an increase in combat exposure showed a linear relationship to the severity of PTSD symptoms in their sample of 315 male combat veterans who served predominantly in the Vietnam War. Additionally, Hoge et al. (2004) described a linear relationship between the number of firefights in which a soldier had been engaged and the prevalence of PTSD in their sample of OIF/OEF service members. An increase in mental health symptoms as a function of increased combat exposure has also been demonslrated by several other authors (Orcutt et al., 2004; Sharkansky et al., 2000; Smith et al., 2008).

Despite the robust relationship between combat exposure and mental health outcomes, some research indicates that certain factors or experiences might be able to buffer the effects of combat exposure, resulting in less mental illness. One factor that has strong theoretical backing, but mixed empirical support, is unit cohesion, or the degree to which soldiers feel committed to and supported by their military units. Theoretically, greater unit cohesion is expected to mitigate the negative effects of traumatic exposure by providing individuals with social support, acceptance, opportunities for reality testing, and the experience of not being alone in one's suffering (Griffith, 2002). These expected outcomes are based on considerable research on the power of group cohesion and group dynamics. For example, group cohesion has been related to group performance in various settings across numerous studies (for meta-analyses, see Evans & Dion, 1991; Gully, Devine, & Whitney, 1995). Cohesion is a significant predictor of satisfaction and enjoyment with a group (Hogg, 1992) and of outcome in psychotherapy groups (e.g., Joyce, Piper, & Ogrodniczuk, 2007). Cohesion has also been found to moderate the effects of stress on performance such that groups that are more cohesive are able to function well even under considerable stress (Bowers, Weaver, & Morgan, 1996).

* Unit Cohesion and Stress in Soldiers

These findings are consistent with research on cohesion in military contexts. A meta-analysis of research on cohesion and military stress generally supported the buffering effect of unit cohesion, indicating that high levels of unit cohesion have a positive impact on an individual's ability to cope with military-related stressors (Oliver, Harman, Hoover, Hayes, & Pandhi, 1999). For example, the authors found that group cohesion was positively related to well-being, readiness, and group and individual performances, with effect sizes of .24, .30, .40, and .20, respectively. These results were supported in a quasi-experimental design of Israeli soldiers during training (Rom & Mikulincer, 2003). In groups with greater unit cohesion, soldiers reported higher instrumental and socioemotional functioning during specific missions as well as less anxious and avoidant attachment to their groups. Other studies have demonstrated such a relationship in samples of deployed and nondeployed military personnel (Brailey, Vasterling, Proctor, Constans, & Friedman, 2007; Britt, Dickinson, Moore, Castro, & Adler, 2007; Iversen et al., 2008; Noy, 1978; Solomon & Mikulincer, 1990; Steiner & Neumann, 1978). For example, Iversen et al. (2008) noted in their retrospective cohort study of U.K. military personnel deployed to the Iraq War in 2003 that low social support within the unit was associated with greater risk of PTS symptoms. Brailey et al. (2007) demonstrated that life experiences and unit cohesion independently predicted PTSD symptoms, with unit cohesion serving to attenuate the impact of life experiences on PTSD, in a sample of yet-to-be-deployed U.S. Army soldiers.

However, Fontana, Rosenheck, and Horvath (1997), in their sample of Vietnam veterans, found no main effect between unit cohesion and PTSD. In addition, the authors demonstrated that a high level of reported unit cohesion in combination with high levels of combat exposure was associated with the highest levels of PTSD. Therefore, although results from many studies support the positive relationship between cohesion and functioning, one of the few studies conducted to directly examine whether unit cohesion buffers the effects of combat exposure on PTSD did not find support for the hypothesis. Fontana et al. related their observed pattern to the theory postulated by Milgram and Hobfoll (1986), who suggested that high unit cohesion may heighten the sense of loss and survivor guilt experienced when members of one's unit are wounded or killed, thus increasing the vulnerability to PTSD. Fontana et al. suggested that the immediacy of data collection related to combat experience may be important in that the short-term effects of unit cohesion may be positive, but the longer term effects may be injurious. However, the results might also be influenced by the retrospective nature of their data (Vietnam veterans recalling degree of unit cohesion at least 10 years following their active duty), such that recall of unit cohesion might be shaped by both the positive and negative postwar experiences of these veterans.

* Other Potential Predictors

The association of demographic and military factors with the development of mental health problems after deployment has also been examined. In their sample of Gulf War I veterans, Orcutt et al. (2004) found that women, those of racial/ethnic minority status, and those with less education had a greater probability of reporting PTSD symptomatology. In their sample of OIF and OEF soldiers, Lapierre et al. (2007) reported that being unmarried and being of junior rank were associated with poorer mental health outcomes. Iversen et al. (2008), in a sample of U.K. service members deployed in the Iraq War, reported an association of PTS symptoms with lower rank, unmarried status, having less education, and having a history of childhood adversity. It thus seems that some degree of convergence exists on these types of variables as they relate to mental health outcomes.

* Other Important Outcomes

Although PTS and PTSD are often of primary concern when examining the mental health of combat veterans, other outcomes are also important in understanding the overall functioning of postdeployment service members. Two other areas in which service members returning from war seem to struggle are depression and anger. For instance, Hoge et al. in their seminal 2004 study of returning OIF/OEF soldiers and Marines reported that approximately 15% of their sample reported significant depressive symptomatology. Lapierre et al. (2007) found a higher rate of depressive symptoms for U.S. Army OIF (37%) and OEF (38%) soldiers who deployed for 12 months in 2004-2005 as measured by the Center for Epidemiologic Studies Depression Scale (Radloff, 1977). Understanding the role of combat exposure and any buffering effects of unit cohesion on depression and anger would help to expand the mental health profile of returning veterans.

* Purpose of the Present Project

As noted, the aim of the current study was to extend research into the identification of variables associated with mental health problems in returning military personnel. We endeavored to identify variables associated with the self-reported mental functioning of U.S. Marines returning to the United States from Iraq after a 7-month combat deployment. Given the proximity of the measurement to the sample's combat experience, we predicted that unit cohesion would be negatively related to the indication of PTS, depression, and anger.

We also hypothesized that greater combat exposure would be associated with the report of greater PTS, depression, and anger. Finally, we hypothesized that the effect of combat exposure on PTS, depression, and anger would be moderated by unit cohesion, such that combat exposure would not be significantly related to PTS, depression, and anger in those from more highly cohesive units.

* Method

Participants and Procedures

All participants were drawn from a Marine infantry battalion that in 2007 had just completed a 7-month deployment to the A1 Anbar province in support of OIF. The unit's primary mission in the combat theater was to search and eliminate the threat caused by various violent groups and to restore peace in the area of operation.

Hazardous missions included clearing buildings, patrolling streets, manning security outposts, and convoying throughout the region. The unit sustained scores of physical casualties and multiple fatalities. As the Marines exited the combat theater, measures were administered by mental health staff temporarily embedded in the unit to assist the service members in the psychological transition from combat to garrison and to identify individuals who would benefit from mental health assistance once they returned to their permanent duty station. The service members attended a postdeployment briefing and were invited to complete screening measures afterward. Individuals who did not complete all items for the included measures were removed from the analysis. The final sample consisted of 330 male U.S.

Marines (n = 319) and Navy Corpsmen (n = 11). The Navy Corpsmen are permanent fixtures in the Marine units in both training and deployment activities and were thus included in the current analysis. To facilitate readability, we refer to the sample as Marines throughout the article despite this inclusion. Descriptive statistics for the continuous measures that were used can be found in Table 1. Because women are not permitted to serve in infantry units, the sample consisted entirely of men. The median and modal military grade was E-3. This rank of Lance Corporal is more broadly housed in the category of junior enlisted. Seventynine percent of the sample was unmarried. The retrospective analysis of these data was approved by the institutional review board at the Naval Medical Center San Diego.

Power Analysis

To determine the necessary sample size, we calculated a power analysis for multiple regression. In this analysis, we set the alpha level to .05 and power to .80 and then calculated power for the 11 predictors at small [f.sup.2] = .02), medium ([f.sup.2] = .15), and midway between these ([f.sup.2] = .09). The resulting sample sizes to reach adequate power at each level of overall effect were 847, 122, and 207, respectively. This indicates that the current sample has adequate power to detect a medium or small-to-medium effect, but not a small effect. Given the questionable utility of a small statistical effect and the difficulties in collecting data from large numbers of active duty military personnel just leaving the combat theater, we were content with the power of our analyses given our sample size of 330.


Each service member completed a packet of testing instruments composed of military unit and basic demographic information, as well as a short battery of psychologically related instruments. Amid this battery were the Post-Deployment Psychological Short Screen (PDPS; Bliese, Wright, Adler, & Thomas, 2004), the Posttraumatic Stress Disorder Checklist (PCL; Weathers, Litz, Herman, Huska, & Keane, 1993), a measure of combat exposure, and a measure of unit cohesion.

The PDPS is a screening instrument designed to allow servicemen and servicewomen returning from deployment the opportunity to self-identify mental health issues and receive appropriate treatment. Embedded in the screening instrument are measures of various mental health problems, including depression and anger management issues. The instrument was initially validated on a sample of 590 soldiers returning from combat operations in Iraq. Through a blinded procedure, the measure was validated against a structured clinical interview (Mini-International Neuropsychiatric Interview; Sheehan et al., 1998). Cut scores for the various subscales were established with adequate levels of sensitivity and specificity. The Depression subscale is composed of four items tapping mood, anhedonia, dysregulated appetite, and problems with attention over the past 2 weeks. Respondents answer on a 4-point Likert-type scale with 1 = not at all, 2 =few or several days, 3 = more than half the days, and 4 = nearly every day. An item is considered a positive response if the respondent endorses more than half the days or nearly every day. Given an operational environment that can often place mental resources at a premium, Bliese et al. (2004) acknowledged an effort to limit false positives in setting the cut scores. Consequently, in the original sample, the specificity of the measure was .96, with a sensitivity of .50. Although the measure is typically interpreted in a dichotomous fashion with a total score of two or more positive responses considered the cutoff for a positive screen, given the low number of participants in the current study who exceeded this threshold, the measure was instead used as a continuous indicator of depressive symptoms. In the current study, the coefficient alpha for this subscale was .77.

The PDPS Anger subscale is composed of three items tapping common expressions of anger. Respondents are to consider how each item pertained to them in the past month by answering on a 5-point Likert-type scale, with 1 = not at all, 2 = rarely, 3 = sometimes, 4 = often, and 5 = very often. An item is considered a positive response if the respondent endorses sometimes, often, or very often. As with the Depression subscale, Bliese et al. (2004) focused on limiting false positives, and a specificity of .97 and a sensitivity of .53 were reported in the original validation sample. Similar to the Depression subscale, the measure is usually interpreted in a dichotomous fashion. However, owing to a low number of participants who screened in the positive range, the measure was interpreted on a continuous scale as a proxy of anger problems. The coefficient alpha for this subscale in the current study was .83 The PCL is a well-validated and frequently used measure of PTSD symptoms that maps directly onto Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychiatric Association, 2000) criteria for PTSD (see, e.g., Keen, Kutter, Niles, & Krinsley, 2008; Ruggiero, Del Ben, Scotti, & Rabalais, 2003). Respondents are asked to rate 17 DSM-IV-TR PTSD symptoms on a 5-point Likert-type scale ranging from 1 (not at all) to 5 (extremely). The total score is considered an index of PTS. The PCL coefficient alpha for the current sample was .93.

Measures of combat experience and unit cohesion were taken from the Deployment Risk and Resilience Inventory (DRRI; D. W. King, King, & Vogt, 2003). The DRRI is a collection of measures designed to tap 14 risk and resilience factors associated with military deployment-related stress reactions that can be administered separately or in any combination. The measure of combat experience is composed of 15 items asking about the occurrence of combat-related experiences (e.g., "I went on combat patrols or missions," "My unit engaged in battle in which it suffered casualties," "I was wounded or injured in combat"). Items are scored dichotomously whether or not the event was experienced, and the cumulative score is an index of overall combat exposure. The measure of unit cohesion consists of 12 items inquiring into what degree the service member felt supported by different elements of his unit and the military as a whole (e.g., "My unit is like my family," "Members of my unit understand me"). Respondents answer on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The cumulative score is an index of greater perceived support and cohesion regarding the military in general, leaders, and fellow unit members. Internal consistency reliability coefficients for the combat exposure and unit cohesion measures as reported in the test manual are .85 and .94, respectively. In the current sample, the coefficient alphas for the two subscales were .77 and .93, respectively. Additional psychometric properties of the DRRI measures as well as the process for test construction are outlined in the manual (D. W. King et al., 2003).


Descriptive Statistics

Descriptive statistics for all demographic information and outcome variables, as well as a correlation matrix of all included variables, are reported in Table 1. Using a cut score of 50 on the PCL, as recommended by Weathers et al. (1993), we found that 8.5% of the sample had clinical levels of PTS.

Main Analyses

To evaluate whether unit cohesion moderates the relationship between combat exposure and mental health outcomes, we conducted hierarchical multiple regressions. Prior to conducting the ordinary least squares multiple regressions, we calculated the intraclass correlations (ICCs) among the five companies (i.e., Alpha, Bravo, etc.) to check that our data from the individual soldiers were adequately independent of the company of which they were a part. ICCs were calculated for PTS, depression, anger, combat exposure, and unit cohesion using the unconditional analysis of variance (ANOVA) model in multilevel regression (Snijders & Bosker, 1999) with company as the group-level variable. None of the ICCs exceeded .25 (range = .00 to .24), suggesting that multilevel regression was not necessary for these data analyses (Guo, 2005). In the first block of the regression, military and demographic variables (age, rank, marital status, previous deployments, and military unit [company]) were included. Given the small number of participants who were divorced (n = 7), legally separated (n = 4), or widowed (n = 0), the variable of marital status was reduced to the dichotomy of married or unmarried. Regarding the military unit variable, the nominal variable with five levels was dummy coded with Foxtrot as the reference group, because this company was less likely to participate in combat missions and to be in the direct line of enemy fire. (Note. To protect the anonymity of study participants, we changed the company names to labels that do not reflect the identifiers of the actual units.) In the second block, the variables of unit cohesion and combat exposure were entered. In the third block, the Unit Cohesion Combat Exposure interaction term was entered. The variables of unit cohesion and combat exposure were centered prior to the calculation of the interaction product terms, as suggested by Cohen, Cohen, West, and Aiken (2003).

PTS. Results from the hierarchical multiple regression used to analyze predictors of PTS are provided in Table 2. The first block, including demographic and military variables, was significant, F(8, 321) = 2.69, p < .05, with model [R.sup.2] = .06. The addition of the second block, including variables of unit cohesion and combat exposure, produced a significant [delta][R.sup.2] = .12,p < .001, for a model [R.sup.2] = .18, F(10, 319) = 7.10,p < .001. The inclusion of the Unit Cohesion x Combat Exposure interaction term in the third block produced a significant [delta][R.sup.2] = .01,p < .05, for a model [R.sup.2] = .19, F(ll, 318) = 6.86, p < .001. Although certain variables were significant at the block at which they were entered, we were mostly interested in the final model, after all variables were entered (this is analogous to an ANOVA with main effect, interaction effects, and covariates). Therefore, we report regression weights for all variables at the final model stage and discuss only those that were significant at that stage. In the final model, unit cohesion and combat exposure significantly predicted PTS, such that less unit cohesion and greater combat exposure were related to greater PTS. The Unit Cohesion x Combat Exposure interaction term was also significant in predicting the outcome variable. Follow-up analyses of the interaction effect were conducted (Cohen et al., 2003). We calculated the slopes of the regression lines between combat exposure and PTS when unit cohesion was high (i.e., one standard deviation above the mean) and low (i.e., one standard deviation below the mean). We then compared these slopes to determine the moderating effect of unit cohesion on the relationship between combat exposure and PTS. These analyses revealed that for those reporting low unit cohesion, greater combat exposure was related to greater PTS. In contrast, for those reporting high unit cohesion, greater combat exposure was related to less PTS. This finding suggests that unit cohesion acts as a buffer between combat exposure and PTS.

Depression. Results from the hierarchical multiple regression used to analyze predictors of depression are provided in Table 2. The first block, including demographic and military variables, was not significant, F(8, 321) = 0.54,p = .83, with model [R.sup.2] = .01. The addition of the second block, including variables of unit cohesion and combat exposure, produced a significant [delta][R.sup.2] =. 10, p < .001, for a model [R.sup.2] =.l 1, F(10, 319) = 3.75, p < .001. The inclusion of the Unit Cohesion Combat Exposure interaction term in the third block produced a significant [delta][R.sup.2] = .01, p < .05, for a model [R.sup.2] =. 12, F(11, 318) = 3.83,p <.001. In the final model, unit cohesion and combat exposure significantly predicted depression, such that less unit cohesion and greater combat exposure were related to greater depressive symptoms. The Unit Cohesion Combat Exposure interaction term was also significant in predicting the outcome variable. Follow-up analyses of the interaction effect were calculated in the same way as they were for PTS. An examination of the different slopes of the regression lines when unit cohesion was high and when it was low revealed that, although participants reporting both high and low levels of cohesion showed a significant positive relationship between combat exposure and depression, the relationship was not as strong for those who had greater unit cohesion. This finding suggests that unit cohesion acts as a buffer between combat exposure and depression.

Anger. Finally, we conducted one more hierarchical multiple regression to analyze predictors of anger. (Note. Specific results from the regression analyzing predictors of anger are not provided in a table to save space. However, they are available from the first author). The first block, including demographic and military variables, was significant, F(8, 311)= 2.07, p = .04, with model [R.sup.2] = .05. The addition of the second block, including variables of unit cohesion and combat exposure, was significant, [delta][R.sup.2] = .07, p < .001, with a model [R.sup.2] = .12, F(10, 309) = 4.07,p < .001. The regression weights for unit cohesion and combat exposure were B = .07, SE B = .02, [beta] = .23, p < .001, and B = .16, SE B = .07, [beta] = .15, p < .05, respectively. The Unit Cohesion Combat Exposure interaction term in the third block was not significant, [delta][R.sup.2] =.01,p =. 17, with a model [R.sup.2] = .13, F(11,308) - 3.89, p < .001. In the final model, unit cohesion and combat exposure significantly predicted anger, such that less unit cohesion and greater combat exposure were related to greater anger.

* Discussion

The current data extend the research in identifying adverse mental health reactions for those in combat by demonstrating that unit cohesion and intensity of combat exposure were significantly related to various mental health outcomes. More specifically, regression analyses demonstrated that both unit cohesion and combat exposure predicted PTS, depression, and anger in the expected directions, above and beyond demographic and unit information. It is acknowledged that as a result of the cross-sectional nature of the current study, causal links between the variables of interest cannot be concluded solely from these data. However, in light of the extant literature demonstrating such a link between unit cohesion and combat exposure on mental health outcomes (e.g., Griffith, 2002; Miller et el., 2008; Oliver et el., 1999), the present findings seem to provide support for the study hypotheses.

Potentially more important than these main effects of unit cohesion and combat exposure on mental health outcomes was the moderating effect of unit cohesion on the combat exposuremental health relationship. For both PTS and depression, greater unit cohesion was related to a weaker relationship between combat exposure and mental health outcome. This was not true for anger, where unit cohesion did not moderate the relationship between combat exposure and mental health. These results are in contrast with research showing that the highest amount of PTSD was associated with high unit cohesion and high combat exposure (Fontana et el., 1997). There are several possibilities that might account for this difference. Fontana et el. (1997) have already provided one possible suggestion, that unit cohesion has a positive effect in the short term but a negative effect in the long term. Of course, this is plausible; however, before this more complicated explanation is accepted, other more parsimonious explanations should be ruled out. For example, the discrepancy of results might be due to the amount of time since participants' exposure to combat and their corresponding ability to recall the cohesion in their unit, with greater time spans being potentially related to less reliability. The discrepancy might also be accounted for by differences in the participants (e.g., Marines returning from Iraq vs. Vietnam veterans being surveyed after substantial reentry into civilian life).

Because unit cohesion can be considered a modifiable variable, efforts to augment this construct may aid in preventing immediate adverse mental health outcomes in service members deployed to a combat zone. Military leadership could then work further to foster unit cohesion in an effort to protect against the later development of mental health issues in their units. Unit cohesion may potentially be conceptualized as one factor contributing to the larger construct of resilience. However, as noted by Fontana et el. (1997), the long-term impact of high unit cohesion in those with heightened levels of combat exposure may be deleterious. Longitudinal data would be necessary to track any changes in either reported unit cohesion or mental health outcomes. The change across time in any moderating effects of unit cohesion and combat exposure would also require longitudinal data unavailable in the current data set. Regarding combat intensity, clearly, one cannot realistically control for levels of combat exposure while conducting operations in a war zone. However, including a combat exposure screen in any postdeployment psychological screening battery (such as the one used in the DRRI) as a mechanism to assist in the identification of service members at greater risk for the later development of mental health problems may be warranted.

Although significant correlations were found between some of the demographic and military variables and PTS (as measured by the PCL; see Table 1), the strength of these associations did not prevail in the regression analyses once unit cohesion and combat exposure variables were included. This is in contrast to some previous reports (Lapierre et el., 2007; Orcutt et el., 2004) that found associations between lower rank and being unmarried and PTS symptoms. These differences may be secondary to our inclusion of trait cohesion and combat exposure variables; indeed, lower rank was predictive of PTS until the variables of unit cohesion and combat exposure were included in the models. As in other studies, age was not predictive of PTS symptomatology, nor was the number of previous deployments. The latter suggests that multiple deployments in and of themselves do not increase the risk of developing PTS, at least for those individuals able to participate in multiple deployments. This finding corresponds to those of Smith et el. (2008), which further suggests that specific combat experiences, rather than deployments themselves, are related to adverse mental health outcomes.

Limitations and Suggestions for Future Research

The current study has several limitations. First, the extant literature has isolated variables related to PTSD in samples of deployed service members that were not available for analysis in the current models, such as reported childhood adversity (Cabrera, Hoge, Bliese, Castro, & Messer, 2007; Iversen et el., 2008) and perceptions of threat (Iversen et el., 2008; L. A. King, King, Bolton, Knight, & Vogt, 2008; Vogt & Tanner, 2007). Given that the current data were gathered primarily for clinical purposes and analyzed retrospectively, measures to tap such areas were not included in the original data collection. Although the current study was unable to account for the perception of threat, the impact of this variable may be somewhat attenuated relative to L. A. King et al.'s (2008) findings owing to the nature of the respective samples. L. A. King et el. found that the perception of threat was significant in predicting outcome after controlling for combat exposure in a sample of Gulf War I veterans who, as a group, reported a much lower level of reported combat exposure (DRRI Combat Experience subscale, M = 3.99, SD = 3.24) relative to the present sample of OIF Marines (M = 8.79, SD = 2.96). This difference is likely a result of the dissimilarities in the conflict and missions in which each of these samples was involved. Nevertheless, we contend that any future prospective analyses should include instruments to measure the area of perceived threat (and childhood adversity) in order to examine any main or interaction effects in OIF or OEF service members.

Next, and as noted earlier, the cross-sectional nature of these data resulted in limitations related to the potential development of the constructs over time. This is especially true in the relationships between unit cohesion, combat exposure, and PTS. Longitudinal data would be ideal to explore this relationship, although cross-sectional data gathered several years aider deployment could be adequate. Given the nature of the sample as a Marine infantry unit, only men were included; additional data are necessary to explore whether these results extend to women. Comparisons based on race/ethnicity were not available with the current data. Future investigations may wish to examine the role of this demographic variable in the relationship between unit cohesion, combat exposure, and mental health outcomes. Baseline data gathered prior to this sample's current deployment were also not available; thus, we were unable to absolutely determine whether the current mental health problems developed as a result of this deployment or whether they were present before deployment. It is also acknowledged that the addition of the interaction terms in the PTS and depression models accounted for only a modest increase in the dependent variable's [R.sup.2]. However, as outlined by Cohen et al. (2003) and Champoux and Peter (1987), small effect sizes for interaction terms are not uncommon in the social sciences and can still be interpreted as meaningful.

Finally, as a consequence of the original clinical use of these data, the instruments used have limitations. For instance, they are all self-report and many are relatively short screening measures. Additionally, although used with relative frequency in military settings, the measures of unit cohesion, depression, and anger used in the current study are in need of further validation in the peer-reviewed literature. No doubt future studies would be improved by including more extensive and robust measures designed to evaluate the constructs under evaluation here.

Summary and Implications for Professional Counselors

Despite these limitations, the study was able to survey a unique sample of U.S. servicemen immediately departing the combat theater. The current data demonstrate that efforts to augment levels of unit cohesion and monitor levels of combat exposure may be beneficial in reducing adverse mental health outcomes in deployed service members. It is somewhat encouraging that the potentially malleable variable of unit cohesion better predicted PTS symptoms than did static variables of rank, age, and marital status. Continuing to outline the impact of dynamic variables that are subject to change, such as unit cohesion and perception of threat, holds, perhaps, the most meaningful line of study, because these variables can potentially be altered in a salutary way.

The most salient way that professional counselors working with active duty service members can apply data from the current study may come with preventive interventions. Any efforts at an individual or organizational level to improve unit cohesion prior to a combat deployment may serve to buffer service members from the deleterious effects of combat trauma. This could conceivably come with psychoeducation during individual therapy or exercises to improve unit cohesion conducted in group therapy sessions (assuming, of course, that group members are in the same unit and anticipate deploying together). Organizationally, education to unit commanders on the positive effects of unit cohesion and the encouragement of continued efforts to augment this construct may be an appropriate task for professional counselors.

Additionally, counselors working with those returning from deployment may wish to investigate clients' perceptions of the cohesion within their unit. By addressing this topic, counselors might be able to access a source of support and coping for the servicemen they serve. Additionally, they may be able to ameliorate some of the negative perceptions of cohesion and help servicemen overcome potentially troubling issues related to perceptions of poor cohesion (e.g., lack of loyalty, support, or mast within the unit) that may be exacerbating other mental health problems. Determining whether these treatment efforts are effective in general and with those who have already developed PTSD versus those who have not would be an important topic for future research.

Finally, given the main effect data of combat exposure on the various mental health outcomes, the inclusion of a combat exposure screen in postdeployment psychological screening batteries may be justified. Such a measure may not carry the same stigma as the highly face valid measures of psychological symptoms generally used in screening batteries. As such, it may offer increased sensitivity in those at risk of developing PTS or depression who may be reluctant to endorse overt mental health symptoms.

Received 12/15/09

Revised 02/08/10

Accepted 03/11/10

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Patrick Armlatead-Jehle, Department of Behavioral Health, Munson Army Health Center, Fort Leavenworth, Kansas; Scott L. Johnston, United States Navy, Naval Medical Center San Diego, San Diego, California; Nathaniel G. Wade, Department of Psychology, Iowa State University; Christofer J. Ecklund, United States Navy, Naval Health Clinic New England, Groton, Connecticut. The views, opinions, and/or findings contained in this article are those of the authors and should not be construed as an official Department of the Army or Navy position, policy, or decision unless so designated by other official documentation. Correspondence concerning this article should be addressed to Patrick Armistead-Jehle, Department of Behavioral Health, Munson Army Health Center, 550 Pope Avenue, Fort Leavenworth, KS 66027 (e-mail:
Correlation Matrix for Predictor and Outcome Variables With Means
and Standard Deviations

Variable                   M      SD        1          2          3

1. Age                    22.0    3.4       --
2. Rank                                    .64 ***     --
3. Marital status                          .38 ***    .39 ***     --
4. Previous deployments    0.66    0.90    .05        .07        .03
5. Unit cohesion          40.9    10.5     .05        .05        .04
6. Combat exposure         8.8     3.0    -.12 *     -.22 ***   -.16 **
7. Posttraumatic stress   29.5    12.1    -.15 **    -.19 ***   -.08
8. Depression              2.10    2.16    .03       -.04       -.02
9. Anger                   2.42    2.51   -.11       -.15 **    -.07

Variable                    4        5          6         7

1. Age
2. Rank
3. Marital status
4. Previous deployments     --
5. Unit cohesion           .02     --
6. Combat exposure        -.03    .04        --
7. Posttraumatic stress    .00   -.22 ***   .31 ***    --
8. Depression             -.01   -.25 ***   .16 **    .62 ***
9. Anger                   .01   -.24 ***   .19 **    .47 ***

Variable                     8      9

1. Age
2. Rank
3. Marital status
4. Previous deployments
5. Unit cohesion
6. Combat exposure
7. Posttraumatic stress
8. Depression              --
9. Anger                  .50 ***   --

Note. Means and standard deviations are provided for all continuous
variables. Correlations between marital status and all other
variables are point biserial.

* p < .05. ** p < .01. *** p < .001.

Results of Hierarchical Multiple Regressions Analyzing Predictors of
Posttraumatic Stress and Depression

                                   Posttraumatic Stress

Variable                [R.sup.2]   [R.sup.2]     B     SE B   [beta]

Block 1                   0.06       .06 **
  Age                                           -0.18   0.24   -.05
  Rank                                          -1.63   0.95   -.12
  Marital status                                 1.17   1.68    .04
  Previous deployments                           0.57   0.70    .04
  Military unit (a)
    Alpha                                        0.81   1.95    .03
    Bravo                                       -2.25   2.08   -.07
    Charlie                                      1.07   2.00    .04
    Delta                                       -3.08   2.04   -.10
Block 2                   0.18       .12 ***
  Unit cohesion                                 -0.24   0.06   -.21 ***
  Combat exposure                                1.30   0.24    .32 ***
Block 3                   0.19       .01 *
  Interaction                                   -0.04   0.02   -.10 *


Variable                [R.sup.2]   [R.sup.2]     B     SE B   [beta]

Block 1                    0.01      .01
  Age                                            0.07   0.05    .10
  Rank                                          -0.19   0.19   -.07
  Marital status                                 0.00   0.33    .00
  Previous deployments                           0.00   0.14    .00
  Military unit (a)
    Alpha                                        0.22   0.38    .04
    Bravo                                        0.06   0.40    .01
    Charlie                                     -0.26   0.39   -.05
    Delta                                       -0.41   0.40   -.07
Block 2                    0.11      .10 ***
  Unit cohesion                                 -0.05   0.01   -.25 ***
  Combat exposure                                0.15   0.05    .19 **
Block 3                    0.12      .01 *
  Interaction                                   -0.01   0.00   -.11 *

Note. Regression weights and standard errors are given for the
variables at the final model, after all effects had been entered.
Interaction = Unit Cohesion x Combat Exposure interaction term.

(a) Foxtrot is the reference group.

* p < .05. ** p < .01. *** p < .001.
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Title Annotation:Research
Author:Armistead-Jehle, Patrick; Johnston, Scott L.; Wade, Nathaniel G.; Ecklund, Christofer J.
Publication:Journal of Counseling and Development
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2011
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