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A Fireside Chat: Attachment Theory and the Deployed Dyadic Dialogue

Author:

Warren N. Ponder

One Tribe Foundation, US
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Abstract

This study asserts that communication between a committed dyad during a combat separation can positively and negatively affect post-deployment functioning. Recent research has delineated two different forms of communication: interactive and delayed. Communication is conceptualized in the three phases of the deployment cycle. To address theoretical gaps in the literature, I posit that attachment theory is the best orientation to explain dyadic communication during deployment. Using linear mixed model, I applied the Actor Partner Interdependence Model (APIM) for distinguishable dyads to explain behavior in terms of attachment theory (anxiety and avoidance). After the statistical significance of the model was established, five dependent variables were added. The most important finding demonstrates that during deployment, the veteran’s secondary strategy was deactivation (avoidance), whereas the strategy employed by the stateside significant other was hyperactivation (anxiety). During the wartime separation, the dyad engaged in closed ranks communication and later found that shifting to open communication post-deployment was challenging. Additionally, other communication findings are discussed, along with their post-deployment consequences.

How to Cite: Ponder, W. N. (2021). A Fireside Chat: Attachment Theory and the Deployed Dyadic Dialogue. Journal of Veterans Studies, 7(1), 204–216. DOI: http://doi.org/10.21061/jvs.v7i1.236
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  Published on 09 Jun 2021
 Accepted on 20 Mar 2021            Submitted on 04 Feb 2021

Attachment Theory and the Deployed Dyadic Dialogue

The Global War on Terrorism (GWOT) has led to a renewed focus on researching the impacts of combat on veterans and their families. Although experts have mostly concentrated their efforts on posttraumatic stress disorder (PTSD), closely followed by military suicide, the topic of spousal communication during deployment is beginning to gain traction among researchers. A major impetus for this is the protective role that social connectedness, especially for the marital dyad, has in relation to the severity of PTSD (e.g., Campbell & Renshaw, 2018; Carter et al., 2011; Keane et al., 1985; King et al., 1998), depression (Thomas et al., 2015), and suicidality (e.g., Brenner et al., 2009; Goldsmith et al., 2002; Weisenhorn et al., 2017).

According to the Department of Defense (DoD, 2019), 50.7% of the Active Duty and 43.7% of the Selected Reserve were married, with approximately 492,000 more dependents (spouses, children, and adults) than actual Active-Duty and Selected Reserve service personnel combined. Given these estimates, not surprisingly, spouses and significant others have a large impact on the military. For example, those in marital relationships have been shown to have lower suicide rates than those who are divorced (DoD, 2016), have higher service personnel retention rates (Burrell et al., 2003), and even have reduced post-deployment PTSD (Carter et al., 2011; Weisenhorn et al., 2017).

One of the main struggles for military dyads consists not only of the burden of what a deployment may bring (e.g., PTSD, permanent injury, death), but also the distance imposed between them during a separation, which affects their communication in a variety of ways. Communication in a combat zone has greatly evolved from World War II (WWII) to the GWOT. A growing body of quantitative and qualitative studies of communication during the GWOT highlights the need for a more comprehensive theoretical framework to understand communication patterns during deployment and how they impact functioning post-deployment. Given the positive, preventive impact that strengthening the military dyad could have on our military’s post-deployment mental health, communication during deployment should be emphasized as a proactive measure. The dyadic dialogue is how couples communicate during a combat separation and how that communication affects post-deployment relationship satisfaction. The dyadic dialogue can manifest over different modes of communication such as telephone, emails, or letters. It also includes the frequency of communication along with the content therein. A shift from an intervention (e.g., intervening with military members and their partners to manage PTSD and suicidality) in favor of a prevention model (e.g., laying a foundation in the marital dyad to decrease severity or completely prevent such outcomes) that is undergirded by a sound guiding theoretical framework is clearly needed. The purpose of this study was to answer the following research question: how does attachment theory explain dyadic communication during a combat separation and its impact on post-deployment functioning?

Literature Review

Communication During Deployment

Communication with loved ones during war has always been important, but the preferred method has changed with technological advances. For example, the most frequent mode of communication in the early 20th century was the US mail (Schumm et al., 2004), a delayed form of communication. However, with technological advances, dyadic communication has become more interactive. For example, in Bosnia, Somalia, and Haiti, interactive forms of communication became the norm (e.g., email, telephone, and sometimes teleconferencing) (Ponder & Aguirre, 2012; Schumm et al., 2004) and afforded the possibility of more frequent communication depending upon the type of mission the veteran was there to perform. Frequency of communication was found to be important in terms of strengthening the dyad and the mental health of the military member, with more frequent communication yielding higher post-deployment marital satisfaction (Ponder & Aguirre, 2012). Additionally, Carter et al. (2011) found that greater frequency of communication resulted in lower post-deployment PTSD for some of their participants.

Communication and the Deployment Cycle

Studies have pointed to three deployment phases: pre-deployment, deployment, and post-deployment (e.g., Devoe & Ross, 2012; Houston et al., 2013; Lapp et al., 2010; Sahlstein et al., 2009). The length of the deployment cycle may look different for each branch and component of the military. The GWOT was the first war to activate and redeploy non-active duty units consistently. In the pre-deployment phase, the service member trained for the mission they would be deployed to perform. In the active duty component, this could have been a one-month stint at the National Training Center (NTC) in Fort Irwin, CA or Joint Readiness Training Center (JRTC) in Fort Polk, LA. For Reserve personnel, they could have been activated for a couple of months before their rotation at NTC or JRTC. The Army generally had a 12-month tour. Tours were shorter for specialty units, such as a Ranger battalion or Special Forces detachment, which typically lasted for 3 to 7 months. The Marine Corps traditionally had 6 to 7-month tours and sometimes 12-month rotations. The post-deployment phase was the last phase of the deployment cycle, and it began when the service member returned stateside. The GWOT was different than previous wars because there are large numbers of military personnel that have had to serve multiple combat tours, meaning dyads have been gone through the deployment phases multiple times during their military career.

Lapp et al. (2010) sampled 18 spouses (16 female and 2 male) of National Guard or Reservists using a phenomenological qualitative design to assess two stress-related factors: the potential sources of stress and coping strategies for stateside spouses over all deployment phases. Pre-deployment analysis uncovered one stressor; the spouses felt like “their lives were on hold” (Lapp et al., 2010, p. 51). This stressor arose from the lack of certainty or communication about deployment dates and times for the service personnel. This finding was similar to conclusions drawn by Sahlstein et al. (2009). During the deployment phase, five stressors were identified: worrying, waiting, going at it alone, pulling double duty, and loneliness. Worrying, or anxiety, was originally rooted in concerns about safety. However, the underlying motivation later turned into the general concern of what their partner would be like upon returning stateside. The spouses consistently asserted that they felt “captive” to the mode of technology they used to communicate (phone calls, emails). Going at it alone is an inclusive term that refers to tasks that were once shared (e.g., household tasks, finances) but, due to deployment, became solo activities for the stateside spouse. The most frequent statement of pulling double duty was lack of sleep, an experience that mirrored that of being a single parent. Loneliness drew comparisons to being abandoned, and respondents mentioned that social support helped buffer this to a degree. Post-deployment spouses reported stress surrounding re-assimilation back into pre-deployment roles.

Spouses also shared how they coped with pre, during, and post-deployment adjustment (Lapp et al., 2010). During the pre-deployment phase, the spouses highlighted social support as an aid for preparation, which includes setting up communication devices (e.g., webcam). During deployments, the spouses shared four coping strategies: keeping busy, staying connected, managing personal needs, and seeking support. Specifically, spouses reported trying to find support from other spouses whose partners were similarly deployed. During the post-deployment phase, spouses read literature about reunions or went to family events hosted by the unit. The participants noted that re-assimilating into family roles was difficult. Upon return stateside, Faber and colleagues (2008) found that communication was difficult for couples transitioning from a closed communication system during deployment to an open communication system post-deployment. Closed communication was used by the service member while deployed so as not to disclose information related to operational security (OPSEC; US Army, 2014). Open communication, healthy for any dyad, was a difficult transition due to the needs, wants, or desires of the other. The longer the relationship life in terms of years, the easier the transition was to open communication. The soldier-to-civilian transition lasted approximately four weeks.

Zamir et al. (2020) investigated how dyads communicated after a combat separation to Afghanistan and Iraq. At time point one, they conducted an in-home assessment along with self-report assessments. The dyads participated in a 14-week group-based intervention to improve parenting practices. At time point two, the dyads were given the same standardized mental health variables. Using the Actor Partner Interdependence Model (APIM), they were able to show that service members’ psychological distress (PTSD, depression, and anxiety symptoms) predicted lower communication quality, after controlling for baseline responses.

Giff et al. (2020) conducted a retrospective cross-sectional study with a sample recruited from the Utah National Guard post-deployment workshops to investigate how different coping strategies (problem focused, emotion and avoidance coping) predicted relationship satisfaction. Using APIM, the only statistically significant predictor of service member relationship satisfaction was their own avoidance coping strategy. However, partner emotion coping was a statistically significant positive predictor of their own, along with the service member’s, relationship satisfaction. In their second model, service member self-reported PTSD negatively predicted their own and their significant other’s relationship satisfaction. Partner self-reported distress, as measured by the Depression Anxiety Stress Scale, significantly negatively predicted their own as well as the service member’s relationship satisfaction. Similarly, partner avoidance coping significantly negatively predicted satisfaction for both. Finally, partner emotion coping significantly positively predicted higher relationship satisfaction for both.

Riggs et al. (2020), guided by attachment theory, used a sample of 156 military service members to investigate communication and family functioning as related to self-reported psychological distress. Attachment avoidance and attachment anxiety were significantly positively correlated with depression and posttraumatic stress. Next, they found that demand/withdraw communication was positively correlated with depression and posttraumatic stress, whereas positive communication was negatively correlated with depression and posttraumatic stress. Additionally, they found that service members who have high attachment avoidance tended to engage in demand/withdraw communication, which appeared to increase the risk for posttraumatic stress and depression. Their findings underscored the maladaptive communication strategies that were associated with high attachment avoidance in a sample of service members.

Communication During GWOT and Impact on Dyad

The study of communication during the GWOT has been more nuanced and complex, exploring a broader range of topics than previous inquiries with a healthy mix of qualitative and quantitative studies. Most wartime separation literature about couples narrowly focused on improving positive constructs, such as resiliency, and decreasing divorce, suicide, and mental health issues (Campbell & Renshaw, 2018; DeViva et al., 2016; McCubbin, 1979; Sahlstein et al., 2009; Weisenhorn et al., 2017; Wiens & Boss, 2006). However, more recent studies have also examined content, mode, and frequency of communication.

Topic avoidance was identified by Merolla (2010) and Faber et al. (2008), who found that veterans made deliberate attempts to center communication around daily activities and avoided communication about combat and other stressful topics. Faber et al. (2008) uncovered that families often felt that their deployed family members withheld information to keep them from worrying excessively. Durham (2010) qualitatively studied how deployed service personnel communicated with their stateside partners. This study was one of the first to probe veterans’ experiences with new technologies and the impact of using these technologies on the veteran. The four themes identified were: need to control communication, need to limit communication with home, value of peer communication, and involvement in a traumatic event. Durham (2010) asserted that “most themes related directly to the barriers to honest communication in a combat environment” (p. 556).

Merolla (2010) investigated relational maintenance during a military deployment. He identified that communication mode preference (e.g., phone, letters/care packages, email [including digital photos], instant messenger/message board, video messages, webcam), debriefing talk, topic avoidance, affection and intimacy, creating and keeping communication routines, future planning, openness, reassuring safety, positivity, and faith talk contributed positively to relational maintenance (Merolla, 2010).

Ponder and Aguirre (2012) quantitatively examined communication between deployed service members and stateside spouses. In this study, 56% of respondents divulged that their primary mode of communication was a computer (i.e., email, instant messenger, webcam). In comparison, 20% indicated US mail, and 24% reported telephone (Ponder & Aguirre, 2012). The frequency of communication with a stateside spouse was significantly related to marital satisfaction. Dyads that communicated less than once per week had the lowest marital satisfaction, whereas those who communicated every day had the highest marital satisfaction. Of the three modes of communication (i.e., computer, US mail, telephone), US mail had the highest marital satisfaction, and computer-based communication had the lowest marital satisfaction.

Renshaw et al. (2009) used a different National Guard sample of 50 from Utah who returned from Operation Iraqi Freedom to investigate psychological problems and marital satisfaction. Those with higher marital satisfaction had less PTSD and depression. Allen and colleagues (2010) found negative communication (negative affect) decreased marital satisfaction, whereas positive bonding (positive affect) increased marital satisfaction. Finally, Carter et al. (2011) found higher marital satisfaction and greater frequency of communication predicted lower post-deployment PTSD symptoms.

Gaps in the Literature

Though research has identified that communication content, mode, and frequency are important to military service members’ mental health and to their marital relationships, there are still gaps in our understanding. The largest gap in the current literature is the lack of a comprehensive theoretical framework to explain the impact of communication content, mode, and frequency. Attachment theory may successfully explain these different aspects of the impact of communication.

Theoretical Framework: Attachment Theory

While no single theory can explain the patterns and effects of communication on the marital dyad’s post-deployment functioning, I argue that attachment theory is the best fit for this topic because it allows for the ability to describe, explain, and predict emotional expression (Mikulincer & Shaver, 2007). Even though attachment theory is often conceptualized as relating to bonding between a child and his/her primary caregiver, studies have indicated that the attachment styles adopted in early childhood continued to impact relationships throughout the lifespan (Ainsworth, 1969; Pickover, 2002).

Adult Attachment

In adult relationships, attachment figures are not just individuals with whom someone has a casual conversation or relationship but can also be “special individuals to whom a person turns when protection and support are needed” (Mikulincer & Shaver, 2007, p. 17). Mikulincer and Shaver (2007) noted previous research (Ainsworth 1991; Hazan & Shaver 1994; Hazan & Zeifman, 1994) that showed that an attachment figure can provide three roles: a person to turn to for proximity seeking, a safe haven, and a secure base.

During infancy, there is a three-stage sequence of emotional reaction: protest, despair, and detachment (Bowlby, 1960). Vormbrock (1993) asserted this same pattern is present for those in a wartime separation. The stateside spouse experienced anger at the military as an institution, which mimics the protest stage from infancy. Next, the stateside spouse shifted to hyperactivation, which resembles despair, and then last, the spouse shifted to detachment as the service member physically departed for the combat zone.

There are four adult attachment styles: secure, preoccupied, dismissing, and fearful. Each has its own unique template for viewing oneself and others. Those who were securely attached had a positive view of themselves and others. Preoccupied people viewed themselves negatively, but others positively. A positive view of self and a negative view of the other was characteristic of dismissive attachment. In contrast, a negative view of self and a negative view of the other was described as a fearful attachment (Mikulincer & Shaver, 2007).

Adult Attachment and Long-Distance Romantic Relationships

The majority of attachment theory research studying long-distance relationships has been conducted on undergraduate students (e.g., Drouin & Landgraff, 2012; Gilbertson et al., 1998; Guerrero et al., 2009; Jin & Peña, 2010; Pistole et al., 2010; Roberts & Pistole, 2009; Timm & Keily, 2011; Weisskirch, 2012; Weisskirch & Delevi, 2011), yet there is applicability to long-distance relationships that are due to a deployment. Similarly, everyone in the current all-volunteer military can almost certainly count on a combat deployment during their enlistment; thus, military members and their romantic partners know to expect a separation. Research has shown that military couples were resilient if they knew to expect a deployment separation; they could cope with it better than if it was unexpected (Karney & Crown, 2007). The military Brigade Combat Team (BCT) usually has a year or more notice of scheduled deployments and training, which allows for pre-deployment preparation within the dyad.

Conceptually, despite advanced notice of a deployment, when a service member draws closer to deployment, the attachment system becomes activated (Riggs & Riggs, 2011). The attachment system may have been perpetually activated during deployment due to the life-threatening environment for both the military member and the stateside partner. Frequent and routine communication was rated as important by military members and their partners (Lapp et al., 2010; Merolla, 2010). Frequent and routine communication allows for deactivation of the attachment system, as it shifts the dyad from physical proximity to emotional proximity during deployment.

Methods

Data Collection

A questionnaire was developed that collected the following data from both the service member and their partner: length of relationship, ethnicity, number of deployments, location of deployment, branch of the military, mode of communication, and frequency of communication, among other data. Additionally, there were eight different standardized assessments used in this study. This study required a full board review by the University of Texas at Arlington’s Institutional Review Board.

Instrumentation

The Experiences in Close Relationships (ECR) is a 36-question instrument that assesses adult attachment using two factors: anxiety and avoidance (Brennan et al., 1998). The ECR is a 7-point Likert instrument with responses ranging from 1 (disagree strongly) to 7 (agree strongly) on each statement. Brennan et al. (1998) reported the coefficients from their initial study were strong for the avoidance factor (α = .94) and anxiety factor (α = .91). Additionally, test-retest reliability statistics usually range from .50 to .75 (Mikulincer & Shaver, 2007). Greater mean scores on the anxiety and avoidance factors indicate a greater presence of each construct. The ECR Cronbach’s alpha in this sample was .940.

Spanier (1976) developed the Dyadic Adjustment Scale (DAS), a 32-question instrument that assesses general satisfaction with the relationship; higher scores indicate better relationship satisfaction. Also, the DAS has high internal consistency with an alpha of .96 along with each subscale: dyadic satisfaction (.94), dyadic cohesion (.81), dyadic consensus (.90), and affectional expression (.73) (Spanier, 1976). The DAS Cronbach’s alpha in this sample was .920.

The Relational Maintenance Behavior Measure (RMBM) has seven factors: positivity, assurances, relationship talk, self-disclosure, understanding, networks, and tasks. The RMBM has good reliability and factorial validity. For men, the reliability coefficients ranged from .83 to .95 and for women, from .86 to .95 (Stafford, 2010). The RMBM Cronbach’s alpha in this sample was .979.

Vogt et al. (2012) created the Combat Experiences (CE), which is a 17-question Likert scale 1 (never) to 6 (daily or almost daily) instrument that measures combat exposure and is summed to obtain an aggregated score that ranges from 17 to 102; higher scores represent greater combat exposure. Within the validation sample of the CE., the internal consistency reliability statistic was .91 (Vogt et al., 2012). The CE Cronbach’s alpha in this sample was .950.

The PCL-5 is a 20-item scale, with Likert scale responses ranging from 0 (not at all) to 4 (extremely) and aggregated scores ranging from 0 to 80. The PCL-5 showed good discriminant (rs = .31 to .60) and convergent (rs = .74 to .85) validity in addition to high internal consistency (α = .94) and test-retest reliability (r = .82; Blevins et al., 2015). The PCL-5 Cronbach’s alpha in this sample was .978.

The PHQ-9 assesses for depression and includes nine questions that range from 0 (not at all) to 3 (nearly every day), which are summed to obtain an overall score ranging from 0 to 27; higher scores indicate more severe depression (Kroenke et al., 2001). The PHQ-9 has internal consistency reliability of .89 (Kroenke et al., 2001). The PHQ-9 Cronbach’s alpha in this sample was .917.

The IES-R measures three symptom clusters that mimic PTSD: intrusions, avoidance, and hyperarousal (Weiss & Marmar, 1997). Aggregated scores range from 0 to 80, with higher scores representing a greater presence of the construct. Furthermore, the internal consistency of the three IES-R subscales is high: intrusion (.87 to .92), avoidance (.84 to .86), and hyperarousal (.79 to .90) (Weiss & Marmar, 1997). The IES-R Cronbach’s alpha in this sample was .978.

The SFI is a 36-question instrument that assesses a person’s perception about their current family functioning across five subscales: health/competence, conflict, cohesion, expressiveness, and leadership (Beavers et al., 1990). For the five subscales, test re-test reliability alphas were: family health/competence (.84 to .87), conflict (.50 to .59), cohesion (.50 to .70), expressiveness (.79 to .89), and directive leadership (.41 to .49) from 30 to 90 days (Beavers et al., 1990). Since there is no aggregated SFI score, only the family health/competence subscale was used. Therefore, in this manuscript, SFI used in reference to the family health/competence subscale. The Cronbach’s alpha in this sample was .931.

Participants

To qualify for inclusion in this study, the couple had to currently be in the same committed relationship that they were in during their most recent combat deployment. This study consisted of 44 participants 22 male and 22 female that produced 22 heterosexual dyads. There was 1 female and 21 male veterans. There was 1 male and 21 female partners. The mean age was 33.48 years old (SD = 6.92) ranging from 24 to 50 years old. The average length of relationship was 11.36 years (SD = 5.88), ranging from 4 to 23 years. Participants self-identified as 37 White (84.1%), 6 Latino (13.6%), and 1 Other (2.3%). In terms of deployment, 8 participants were deployed once (36.4%), 6 people twice (27.3%), 3 people 3 times (13.6%), 2 people 4 times (9.1%), and 3 people 5 or more times (13.6%). The location of their most recent deployment was Iraq (n = 13; 59.1%), Afghanistan (n = 5; 22.7%), Other (n = 4; 18.2%). Their respective branch of service was Navy (n = 2; 9.1%), Army (n = 15; 68.2%), Air Force (n = 3; 13.6%), and Marine Corps (n = 2; 9.1%). Rank was E-1 to E-4 (n = 5; 22.7%), E-5 to E-9 (n = 12; 54.5%), and Warrant or commissioned officer (n = 5; 22.7%).

Data Analysis

To determine the impact of veterans’ and spouses’ attachment (anxiety and avoidance) on their own dependent variables as well as their partner’s dependent variables, the APIM with distinguishable dyads regression model was used. The APIM regression approach can be used to determine how outcomes are influenced by both members of the dyad (veteran and spouse). In this model, the actor was the veteran, and the partner was their significant other. In this study, the actor effect was the impact of the veteran’s perception on their spouse’s dependent variables, whereas the partner effect was the impact of the spouse’s perception on the veteran’s dependent variables. For the Statistical Package for the Social Sciences (SPSS), the variable ROLE was created with the dyads distinguishing variable with veterans coded as 1 and spouses coded as –1. For the attachment model, all two-way interactions with ROLE were included as recommended by Kenny et al. (2006). All analyses were conducted with the SPSS version 26.

Results

Descriptive Statistics

Veteran’s responses to the RMBM were normally distributed for each subscale (positivity, understanding, self-disclosure, relationship talks, assurances, tasks, and networks) except networks (interactive). All the partner RMBM subscales were normally distributed, except for relationship talks (delayed and interactive) and assurances (delayed and interactive). From this point, 4 dyads were excluded because over 90% of their data on the RMBM was missing. I imputed means for the remaining veterans and partners who had less than 10% of their data missing. After that, partner relationship talks (delayed and interactive), assurances (delayed and interactive), and networks (interactive) were not normally distributed, so they were not included in further statistical analyses. Additionally, veteran networks (interactive) were still not normally distributed after imputed means. Consequently, the only RMBM data used in further statistical analyses on both the veteran and partner were for the following subscales: positivity (delayed and interactive), understanding (delayed and interactive), disclosure (delayed and interactive), task (delayed and interactive), and network (delayed).

All respondents completed the ECR. The veterans’ mean score on the ECR anxiety factor was 3.26 (SD = 1.26), ranging from 1.28 to 6.33. The veterans’ mean score on the ECR avoidance factor was 2.91 (SD = .97), ranging from 1.11 to 4.78. The partners’ mean score on the ECR anxiety factor was 3.29 (SD = 1.41), ranging from 1 to 5.78. The partners’ mean score on the ECR avoidance factor was 2.25 (SD = .90), ranging from 1 to 4. All respondents completed the CE. The veterans’ mean score on the CE was 41.32 (SD = 18.77), ranging from 17 to 79. The spouses’ mean score (perception) about their service members’ CE was 39.86 (SD = 20.19), ranging from 0 to 74.

All respondents completed the IES-R. The mean score for all respondents on the IES-R was 16.14 (SD = 21.79), ranging from 0 to 77, and were not normally distributed. Subsequently, the common logarithm to the base 10 denoted as LOG was used to transform the data. The mean score for veterans’ LOG(IES-R) was 1.06 (SD = .62), ranging from 0 to 1.88, and the mean score for partners’ LOG(IES-R) was .63 (SD = .59), ranging from 0 to 1.89. Both were normally distributed. Refer to Table 1 for descriptive statistics on the dependent variable assessment instruments: LOG(IES-R), PHQ-9, PCL-5, SFI, and DAS.

Table 1

Descriptive Statistics for Dependent Variables.


PHQ-9 PCL-5 LOG(IES-R) DAS SFI

Veteran 9.09 (6.84) 29.32 (21.94) 1.06 (.62) 102.95 (16.55) 39.59 (12.83)

Partner 6.81 (6.99) 24.32 (24.94) .63 (.59) 112.10 (16.14) 36.64 (13.52)

Note: PHQ-9 = Patient Health Questionnaire-9; PCL-5 = PTSD Checklist-5; LOG(IES-R) = Impact of Events Scale-Revised; DAS = Dyadic Adjustment Scale; SFI = Self-Report Family.

Inventory: Version II. Means are outside of the parentheses and Standard Deviations are within the parentheses.

APIM

Model Main Effects on Dependent Variables

As a first step, the statistical significance of this model was established with the five dependent variables (LOG[IES-R], PHQ-9, PCL-5, SFI, and DAS). Next, the remaining variables from the survey (participant demographic information, delayed/interactive communication measures [i.e., RMBM], as well as method/frequency of communication during the combat deployment), were examined for inclusion via a stepwise regression.

The model for each dependent variable displayed clear trends. The main effect of attachment anxiety or attachment avoidance, as exhibited by the individual or the spouse, was statistically significant at the .05 level of significance. More specifically, there were four attachment anxiety/avoidance variables (or main effects) for everyone: the veteran’s anxiety, the veteran’s avoidance, the spouse’s anxiety, and the spouse’s avoidance. Only one of these four main effects was statistically significant in each model. Attachment anxiety had a positive relationship with the LOG(IES-R), PHQ-9, and PCL-5, whereas attachment avoidance was related to decreased relationship satisfaction. Spousal avoidance was related to family functioning, and role (veteran/spouse) was related to decreased relationship satisfaction.

The veteran and spouse’s attachment anxiety and attachment avoidance variables have four interactions with ROLE. These interactions are complementary to the main effects. If an anxiety main effect was significant, then only an avoidance interaction with ROLE would be significant. Correspondingly, if an avoidance main effect was significant, then only an anxiety interaction with ROLE would be significant. The interaction of veteran anxiety and ROLE was related to family functioning and relationship satisfaction. The interaction of veteran avoidance and ROLE was related to higher PTSD scores. Spousal anxiety was related to decreased family and relationship functioning. Spousal avoidance and ROLE were related to decreased LOG(IES-R), PHQ-9, and PCL-5 scores. In addition to the attachment main effects, I examined the interaction of each with ROLE (actor or partner) as predictors of the five dependent variables (LOG[IES-R], PHQ-9, PCL-5, SFI, and DAS). Refer to Table 2 for the APIM model main effect on dependent variables.

Table 2

Initial Model Main Effects on Dependent Variables.


DV ROLE ANX(ACTOR) AVOID(ACTOR) ANX(PARTNER) AVOID(PARTNER) ANX(ACTOR) × ROLE AVOID(ACTOR) × ROLE ANX(PARTNER) × ROLE AVOID(PARTNER) × ROLE

LOG(IES-R) 0.61 0.16* 0.09 0.04 –0.16 0.01 0.06 0.04 –0.31**

PHQ-9 4.52 3.01*** 1.11 0.51 –1.26 0.21 0.48 0.50 –2.77*

PCL-5 9.52 9.11*** –1.95 1.92 3.47 –0.42 10.08* 1.03 –12.51**

SFI 8.26*** 1.96 0.90 0.94 3.83* –5.29**** 1.49 2.60* –0.57

DAS –7.24* –2.22 –5.10** –0.48 –2.76 5.98*** –0.28 –5.07*** 0.85

Note: DV = dependent variable, ROLE = veteran or spouse, ANX (Actor) = anxiety of actor, AVOID (Actor) = avoidance of actor, ANX(Partner) = partner anxiety, AVOID (Partner) = partner avoidance), ANX (Actor) × ROLE = anxiety of actor × veteran/spouse, AVOID (Actor) × ROLE = avoidance of actor × veteran/spouse, ANX (Partner) × ROLE = partner anxiety × veteran/spouse, AVOID (Partner) × ROLE = partner avoidance × veteran/spouse. LOG(IES-R) = Impact of Events Scale-Revised; PHQ-9 = Patient Healthcare Questionnaire-9; PCL-5 = PTSD Checklist-5; SFI = Self-Report Family Inventory: Version II; DAS = Dyadic Adjustment Scale. Estimate with associated level of significance * p < .10; ** p < .05, *** p < .01; **** p < .001.

APIM Model on LOG(IES-R)

In the model, the only main effect on LOG(IES-R) that was statistically significant was actor anxiety (estimate = 0.16, t = –1.86, p < .10). Additionally, the interaction of partner avoidance and ROLE was a significant (estimate = –0.31, t = –2.11, p < .05) predictor on LOG(IES-R). None of the additional variables in the survey were statistically significant so they were excluded from the model.

APIM Model on PHQ-9

In the model, the only main effect on PHQ-9 that was statistically significant was actor anxiety (estimate = 3.70, t = 4.92, p < .001). Furthermore, the interaction of partner avoidance and ROLE was a significant predictor on PHQ-9 (estimate = –3.61, t = –2.97, p < .01). Finally, the independent variable RMBM delayed task was a significant predictor on PHQ-9 (estimate = .88, t = 3.46, p < .001).

APIM Model on PCL-5

In the model, two main effects on PCL-5 were statistically significant: actor anxiety (estimate = 12.32, t = 4.73, p < .001) and partner anxiety (estimate = 4.98, t = 1.99, p < .10). There were two statistically significant interactions: actor avoidance and ROLE (estimate = 8.84, t = 2.07, p < .10), along with partner avoidance and ROLE (estimate = –9.97, t = –2.27, p < .05). Last, the independent variable CE was a significant predictor on PCL-5 (estimate = .62, t = 3.70, p < .001).

APIM Model on SFI

In the model, two main effects on SFI were statistically significant: ROLE (estimate = 7.66, t = 3.18, p < .01) and partner avoidance (estimate = 3.35, t = 1.81, p < .10). There were two statistically significant interactions: actor anxiety and ROLE (estimate = –4.65, t = –3.65, p < .001) and partner anxiety x ROLE (estimate = 2.45, t = 1.96, p < .10). Finally, the independent variable RMBM interactive task factor was a significant predictor on SFI (estimate = –.74, t = –2.98, p < .01).

APIM Model on DAS

In the model, there were three main effects on DAS that were statistically significant: ROLE (estimate = –9.48, t = –2.36, p < .01), actor avoidance, (estimate = –4.47, t = –2.40, p < .05), and partner avoidance (estimate = –4.02, t = –2.12, p < .05). There were two statistically significant interactions: actor anxiety and ROLE (estimate = .5.95, t = 1.27, p < .001) and partner anxiety x ROLE (estimate = –4.46, t = –3.56, p < .001). Finally, two independent variables were significant: interactive partner communication (estimate = –4.99, t = –4.00, p < .001) and RMBM interactive task (estimate = .90, t = .29, p < .01).

Discussion

The purpose of this study is to investigate how attachment theory explains dyadic communication during a combat separation and its impact on post-deployment functioning. Unfortunately, attachment theory has had limited use as a theoretical framework to conceptualize a dyadic wartime separation (Ponder & Carbajal, 2020; Riggs & Riggs, 2011; Vormbrock, 1993). Recent research (Ponder & Carbajal, 2020) using attachment theory as a guiding framework has found a significant relationship between veteran ECR avoidance and email frequency (r = –.44, p ≤ .05), call frequency (r = –.51, p ≤ .05), and Skype frequency (r = –.46, p ≤ .05), whereas partners also had a statistically significant relationship with ECR anxiety and social networking frequency (r = .46, p ≤ .05), as well as text frequency (r = .46, p ≤ .05). Thus, new and emerging research (Ponder & Carbajal, 2020) has empirically established that veterans’ preferred secondary strategy is deactivation (avoidance), whereas stateside spouses’ preferred secondary strategy is hyperactivation (anxiety). This section will address closed ranks versus open ranks styles of communication, awareness/accommodation, caregiving/attachment systems, proposed solutions, directions of future research, and limitations.

Closed Ranks vs. Open Ranks

Since WWII and beyond, experts have attempted to assess the content of communication during wartime family/dyadic separation. Recently, researchers have used different terminology to describe the same construct: openness vs. closedness (Sahlstein et al., 2009), topic avoidance (Merolla, 2010), and several closely related concepts, such as the need to control or limit communication with home (Durham, 2010). For this manuscript, I use the terms closed ranks and open ranks because the nomenclature simultaneously casts both a narrow and a wide umbrella over the content of communication. Closed ranks communication is an attempt to limit the content of communication, whereas with open ranks individuals have the latitude to discuss virtually any topic.

Hill (1949) was one of the first researchers to use the family system rather than the individual as the unit of measurement. Familial (dyadic) adjustment to the wartime separation can exclude (closed ranks) or include (open ranks) the deployed service member. Previous research (Boulding, 1950; Hill, 1949) showed that families who used closed ranks coped better than open ranks during the wartime separation. Though both closed and open ranks had adjustment problems, closed ranks had more severe issues upon reunion (post-combat). Vormbrock, (1993) concluded that “war wives who did not close the husband’s ranks and were in constant turmoil during marital separation fitted the anxious-ambivalent profile” (p. 138).

The RMBM used in this study has seven factors: positivity, understanding, self-disclosure, relationship talks, assurances, tasks, and networks. Interestingly, the RMBM (interactive) task-factor is significantly related to better family functioning (estimate = –.74, SE = .25, t = –2.98, p < .01) and higher relationship satisfaction (estimate = .90, SE = .29, t = 3.07, p < .01). Again, lower scores on the SFI are ideal, whereas higher scores indicate unhealthier family functioning. The RMBM task-factor has four statements: shares in the joint responsibilities that face us, performs his/her household responsibilities; helps with the tasks that need to be done; and does not shirk his/her duties (Stafford, 2010).

To my knowledge, this is the first quantitative finding using delayed and interactive forms of communication to investigate the content of communication during a wartime separation in the GWOT era from an attachment theoretical framework. Communicating in an interactive manner (telephone, instant messaging, and instant messaging with video) while practicing closed ranks communication (RMBM) improves post-deployment family functioning and relationship satisfaction. My interpretation of this finding is that communicating interactively about day-to-day activities, instead of communicating about negatively related topics, such as rent being late or being behind on a car payment is beneficial. When the dyad talks about these day-to-day activities, consciously or unconsciously, they are avoiding the topic of death. Recall that since the veteran is in a warzone, their attachment system is constantly activated (Riggs & Riggs, 2011). Consequently, when the stateside spouse is communicating in real-time (interactive), for that brief moment, their attachment system is dormant.

Awareness & Accommodation

In addition to studying the content of communication, another important finding via the interactions is that the individual’s coping style (anxiety or avoidance) during deployment continues to be their preferred method of coping upon return stateside. When the stateside spouse conforms to the veteran’s preferred secondary strategy (avoidance) post-deployment, the military member has positive outcomes (e.g., decreases in self-reported stress and depression, slight increases in PTSD symptoms). However, when the veteran continues to use their secondary strategy (avoidance) and does not conform to the stateside spouse’s preferred strategy (anxiety), then the stateside spouse has maladaptive outcomes (increases in self-reported stress and depression; increase in perceived level of PTSD in their veteran). The same trend is present for the anxiety interactions. When veterans conform to their spouse’s preferred secondary strategy (anxiety), relationship satisfaction and family functioning improve. However, when the spouse uses hyperactivation and does not conform to the veteran’s preferred secondary strategy (avoidance), there are maladaptive outcomes (decreased relationship satisfaction and family functioning).

Caregiving & Attachment Systems

In addition to findings regarding the content of communication and awareness/accommodation, another interesting discovery involving the differing roles of the caregiving and attachment systems emerged. The caregiving and attachment systems are separate but complementary to each other. Mikulincer and Shaver (2007) asserted that “the set goal of the caregiving system is to reduce other people’s suffering, protect them from harm, and foster their growth and development” (p. 326). Caregiving behaviors are activated when another person has to cope with a stressful situation, such as a wartime separation. In a review of home-based and traveling spouses, Vormbrock (1993) asserted the traveling spouse uses the caregiver system, whereas the home-based spouse has their attachment system activated. Aside from the intrapersonal experience within the dyadic relationship, most communication with a stateside spouse is initiated by the deployed service member; therefore, it is reasonable to assume the veteran is in the caregiver role, whereas the stateside spouse experiences the attachment system.

When the veteran is in the care-giving role, a plethora of issues can arise. Ideally, individuals in the care-giving role are attentive and responsive to their significant others’ needs and signals (Mikulincer & Shaver, 2007). However, since the veteran’s secondary strategy is avoidance, there can be a spillover effect. For example, avoidant people generally endorse a neglectful and nonresponsive style of caregiving (Mikulincer & Shaver, 2007). Consequently, this can exacerbate the stateside spouse’s attachment system, which is already constantly activated.

Proposed Solution

One of the many advantages of using attachment theory is that it allows for observing and predicting the emotional responses an individual may have. For example, Whealin et al. (2015) investigated e-mental health preferences for veterans with and without PTSD. They used Anderson’s Behavioral Model as their theoretical framework and hypothesized those with PTSD would be more likely to use e-mental health services. Their findings were the exact opposite; 20–38% of those with PTSD and 36–68% of those without PTSD showed a willingness to use e-mental health modalities. Within attachment theory, these results would be expected. Veterans’ preferred coping mechanism during and post-deployment is avoidance (deactivation). From the vantage point of an attachment perspective, avoidance would be expected.

Pre-deployment, the spouse’s attachment system gets activated due largely to a perceived lack of control or power. At this point, spouses’ anxiety begins from a lack of information (Lapp et al., 2010; Sahlstein et al., 2009). Simple communication through official channels to spouses about deployment logistics may empower them. There is a necessity for OPSEC, but the DoD could provide limited explanations about brigade or battalion timelines.

During the deployment, the spouse becomes anxious about their loved one’s safety, which later transitions to anxiety about reunification. Policy and adherence to OPSEC are vital, so details of any operation cannot be divulged. However, institutional controls may provide benefits. Because spouses exhibit hyperactivation and interactive communications are preferred, ensuring routine times for interactive communication might help. Additionally, veterans report that there are privacy concerns, since interactive communication is generally done in a group setting like a Morale, Welfare, and Recreation location. If a platoon or company could reserve a more private setting so instant messaging with video can be used, this would extinguish the stateside spouse’s anxiety about their loved one’s safety.

Making the transition from coping skills used during deployment to post-deployment can be challenging. While in a combat theatre, where the veteran’s attention and focus need to be on the mission, it is understandable that closed communication is preferred. However, when a unit knows they are leaving for Kuwait for a couple of weeks before returning stateside, there is virtually no threat to safety. During this time, the DoD could educate veterans about transitioning from closed to open emotional expression, which will be used at home. After physically returning stateside, veterans are required to attend briefings on various topics, including family reunification. Briefings are usually done in a very large assembly hall, which more closely resembles a lecture than a didactic process. It is recommended that dyads that want or need more in-depth training have that set up in a 10–15 couple maximum format.

Future Research

Findings from studies in the literature I reviewed show that during the deployment phase, couples attempted to keep the content of their communication on day-to-day events rather than the content of the veteran’s combat missions. When used interactively, the relationship maintenance tasks factor is a significant predictor variable in two of the main effects (SFI, DAS). It is hypothesized that the relationship maintenance factor, daily tasks, is what other researchers referred to as topic avoidance (Sahlstein et al., 2009). Future research should focus on attempting to quantify day-to-day events. This study conceptualizes delayed and interactive forms of communication like the Carter and colleagues’ (2011) brief report. Special attention should be given to email, as this might be interactive if couples are emailing in real time.

Limitations

The results of this study should be interpreted with caution for several reasons. First, the nature of the study is cross-sectional and retrospective, precluding causal inference. Second, I did not collect data on the respondents’ level of education; however, education should not be overlooked in future research. Third, data from a couple of the instruments is not normally distributed. It is hypothesized that this is in part due to the small sample size. Fourth, I did not collect data on whom the veteran turned to for support, and under what circumstances. Durham (2010) suggested that peer to peer support among veterans is highly valued, and research is recommended in this area. Despite the study limitations, I hope that other researchers see the applicability and utility of using attachment theory when studying veteran dyads.

Competing Interests

The author has no competing interests to declare.

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