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Article

A Large-Scale Survey of Barriers and Attractors to Mental Healthcare Utilization for Active-Duty Service Members in the U.S. Department of the Air Force

by
J. Mark Eddy
1,2,*,
Richard E. Heyman
1,
Amy M. Smith Slep
1,
Michael F. Lorber
1,
Shu Xu
1,
Kerry N. Makin-Byrd
1 and
Rachel E. Foster
3
1
Family Translational Research Group, New York University, New York, NY 10010, USA
2
Department of Educational Psychology, The University of Texas at Austin, Austin, TX 78721, USA
3
Air Force Medical Command, Department of the Air Force, Falls Church, VA 22042, USA
*
Author to whom correspondence should be addressed.
Psychol. Int. 2025, 7(2), 30; https://doi.org/10.3390/psycholint7020030
Submission received: 14 February 2025 / Revised: 24 March 2025 / Accepted: 26 March 2025 / Published: 1 April 2025
Versions Notes

Abstract

:
Despite expanded mental health services and outreach within the military, most active-duty members who endorse mental health problems do not seek services. Little is known about why this is the case, but cognitions may play a key role. In this study, cognitions relevant to service seeking were compared among three subgroups of active-duty members: those who endorse one or more mental health problems and sought services, those who endorse problems and do not seek services, and those who do not endorse problems. To examine differences and similarities among these groups, a stratified random sample of 162,340 was drawn from all active-duty members of the U.S. Department of the Air Force serving at 91 installations around the world. Each selected member was invited to anonymously complete the Air Force Community Assessment, and 63,227 members (39% of those invited) participated. Of these, one in five reported at least one mental health problem, and one in three reported receiving services within the past two years. Participants reporting problems and who had not sought services perceived more peer and institutional stigma, reported more knowledge and logistic barriers, reported more negative attitudes towards mental health services, and reported fewer attractors to mental health services compared with those who did not report problems. Those who reported problems and sought services reported more problems and more negative attitudes than those reporting problems who had not sought services. Nine of ten who reported problems but had not received services indicated they had no intention to seek them.

1. Introduction

Over the past several decades, a substantial minority of U.S. active-duty military service members (SMs) have experienced significant mental health problems such as Major Depressive Disorder and Post-Traumatic Stress Disorder (e.g., Meadows et al., 2018). Despite expanded mental health resources and ongoing outreach to encourage access to those resources, most SMs who report having mental health problems do not seek mental health services (Hoge et al., 2004; Kim et al., 2010; Quartana et al., 2014; Tsai et al., 2015). Although a majority of SMs endorse positive attitudes towards treatment—such as the belief that mental health treatment is effective—other cognitions about mental health and treatment continue to be significant predictors of mental health services underuse (Cooper et al., 2003; Corrigan, 2004; Greene-Shortridge et al., 2007; Kim et al., 2010; Wright et al., 2009). Furthermore, the underuse of military mental health services is associated with increased difficulties in multiple areas of life, including individual and family suffering, as well as higher health care costs (Britt, 2000; Constantian, 1998; Hoge et al., 2002). Given the deleterious impact of mental health service underuse, a clearer understanding of the barriers and the attractors of mental health service utilization within the military is needed to help more precisely craft effective mental health care promotion efforts (Acosta et al., 2014).
Cognitions play a key role in the prevailing theoretical models of help-seeking, such as the Transtheoretical Model (i.e., the “stages of change” model; Prochaska & DiClemente, 1982), the Theory of Planned Behavior (e.g., Ajzen, 1991), and the Health Beliefs Model (e.g., Becker, 1974). Three types of cognitions within these models—stigma beliefs, treatment attitudes, and perceived barriers to services—are hypothesized to be impactful in decision-making about whether or not to seek help within the military. The first, stigma beliefs, has received the most attention (e.g., Acosta et al., 2014; Heyman et al., 2022). Stigma beliefs (i.e., beliefs about negative self and public perceptions of the label of mental health problems and associated consequences; Corrigan, 2004) are common among SMs (e.g., 33–37% of nondeployed SMs, 26–42% of deployed SMs, 27–66% SMs who screen positive for psychiatric problems; Heyman et al., 2022) and are associated with less treatment-seeking as well as early discontinuation from treatment (Cooper et al., 2003; Leaf et al., 1987; Sirey et al., 2001a, 2001b). Qualitative studies suggest two versions of stigma that may inhibit service seeking: peer stigma (i.e., the belief that seeking mental health treatment will negatively affect others’ perceptions of the service member) and institutional stigma (i.e., the belief that seeking mental health treatment will impact the SM’s career goals or progression; Corrigan, 2004; Hoge et al., 2004; Stecker et al., 2007). Although this clarification may be helpful, little is known about either stigma type or what role they play in decision making. Given this, it is not surprising that there is a clear consensus in the field that further exploration of stigma beliefs is needed. Each of the major literature reviews conducted over the past decade on stigma has concluded that the understanding of how stigma impacts mental health service-seeking and subsequent outcomes is quite limited (e.g., Cerully et al., 2018; Sharp et al., 2015; Michalopoulou et al., 2017).
Regarding the second type of cognition, SMs holding negative treatment attitudes (i.e., beliefs that mental health treatment does not work) are almost 40% less likely to seek mental health care (Kim et al., 2011) than those with positive attitudes. In terms of the third type, SMs who perceive barriers to health care (e.g., difficulty finding, selecting and maintaining services) are significantly less likely to seek help, less satisfied with care, and have fewer positive health outcomes in civilian and military samples (Andrulis, 1998; Britt et al., 2008; Kasper, 1998; Kim et al., 2010; Wright et al., 2009). Beyond these three types, cognitions about logistic barriers (i.e., practical obstacles to participation) and, to a lesser extent, knowledge barriers (e.g., not knowing where or how to receive help) also appear to be important, and particularly within the military workforce. For example, in a survey of more than 7000 U.S. Army and Marine members, over half of SMs reported logistic barriers to receiving services, whereas knowledge barriers regarding various aspects of service delivery and receipt were endorsed by one-fifth of members (Hoge et al., 2004).
While these various cognitions appear to be related to service utilization, the knowledge base regarding them is slim. Furthermore, notably missing from the research base are studies that examine multiple types of cognitions at once. In the only published large-scale study, Kessler et al. (2001) found that a significant proportion of a nationally representative sample of 8098 adults who both needed and wanted services (i.e., endorsing mental health problems and interested in receiving services) listed multiple cognitions as key reasons for not seeking treatment, with 45.4% reporting negative treatment attitudes (e.g., “help probably would not do any good”), 14.2% endorsing peer stigma (e.g., “concerned about what others might think”), and 51.7% endorsing knowledge or logistic barriers to receiving services (e.g., “unsure about where to get help,). Unfortunately, this study did not also consider how these cognitions operated in concert with each other or how they were offset by attractors to services.
In summary, stigma beliefs, treatment attitudes, and perceived barriers to services are all negatively related to help-seeking. Although barriers and nonspecific stigma beliefs have been examined multiple times in military samples, we know little about (a) what types of stigma are most related to help-seeking and (b) the additive and interactive effects of stigma beliefs, treatment attitudes and perceived attractors and barriers with help-seeking. Furthermore, there is little information on how these factors vary across different subgroups of SMs, such as those who have mental health problems but have not received services (e.g., Vogt, 2011).
Although negative cognitions related to help-seeking appear to be important, they do not capture the full story. Various studies have found that people will only seek services if there are positive attractors to or facilitators of seeking care. A recent systematic review of 111 articles on military help-seeking found that positive treatment attitudes, support from family and friends, and support from leadership (ranging from unit commanders to service leadership) all increased the likelihood of treatment utilization (Hom et al., 2017). For example, in one of the few prospective longitudinal studies of facilitators, 529 SMs were assessed regarding a variety of attitudes towards treatment at baseline and then queried about service utilization 8 months later. Positive attitudes towards treatment—as measured by affirmative responses to statements such as “it takes courage to receive treatment for a mental health problem” and “mental health counseling can be useful for those who need it”—was the only attitudinal variable associated with an increased likelihood of treatment-seeking. Despite the promise of harnessing constructs such as these to understand and promote service utilization, only 24 of the 111 studies in the Hom et al. (2017) review focused on facilitators to care.
The secondary analyses reported here—with data drawn from a large-scale survey of SMs in the U.S. Department of the Air Force (DAF)—are intended to begin to address gaps in the military literature by examining cognitive differences among SMs who endorse clinically significant mental health problems and who do or do not seek services versus those who do not endorse such problems. Comparative analyses like these in large, existing datasets are one of the least used but potentially most cost-effective and powerful approaches for gaining a better understanding of the role of cognitions in help-seeking among those most in need of care. The joint study of barriers (e.g., stigma, treatment attitudes, logistical barriers) and attractors (e.g., belief that treatment would help) to SMs has the potential to inform both military and civilian outreach efforts. Understanding not only what cognitions exist but also whether these cognitions are more common among specific subgroups within the SM population is especially useful for the development of targeted prevention and intervention efforts. We hypothesize that SMs with mental health problems and who have not sought services, compared with those with problems who have sought services and those without problems, will endorse higher rates of stigma and more negative treatment attitudes and will perceive greater barriers and fewer attractors to obtaining treatment.

2. Materials and Method

2.1. Overview

A stratified random sample of 162,340 potential participants was drawn from all DAF SMs at 91 installations worldwide in 2011. This sampling technique divided the population into subgroups (strata)—by installation, by gender, and by pay grade (i.e., rank)—and then random samples were selected within each stratum in proportion to the stratum’s size in the overall DAF population. This strategy ensured representative sampling within installations and across gender and pay grades. All selected individuals were then invited to voluntarily and anonymously participate in a survey, the DAF Community Assessment. Of note, at the time of this survey, the US was at war in both Iraq and Afghanistan. Ultimately, 63,227 (39%) SMs participated.

2.2. Participants

The demographics of the sample were similar to DAF population demographics at the time of the survey (ICF International, 2012) and similar to those of today (e.g., Department of the Air Force, 2025). Most participants were male (80.6%) and married (63.7%). The average participant was 30.6 years of age (SD = 7.1) and had served in the military an average of 9.4 years (SD = 7.1). The majority of participants were enlisted SMs (79.9%), including those with junior enlisted paygrades E1 to E4 (31.6% of the sample), those with junior non-commissioned officer paygrades E5 to E6 (35.7%), and those with senior non-commissioned officer paygrades E7 to E9 (12.1%). The remaining participants were officers, including those with company-grade officer pay grades O1–O3 (11.4%) and those with field-grade or general officer pay grades of O4 or higher (9.2%).

2.3. Procedures

The study protocol, including the DAF Community Assessment, was designed through a partnership between the DAF and our research team. Survey questions comprised established and purpose-built scales measuring constructs at the individual, family, organization, and community levels. The survey and protocol were reviewed and approved by the DAF Survey Office and the DAF Compliance Office. Once approvals were granted, randomly selected SMs within each of the aforementioned strata received an email from the contractor administering the 2011 DAF Community Assessment. This email included an invitation from one of the top leaders in the DAF and a link for participation. Subsequently, each DAF installation, as well as the DAF overall, publicized the survey through myriad workplace and community channels. Participant recruitment materials stressed the usefulness of past DAF Community Assessments for improving SM health (i.e., results allow both base and DAF leaders to know about SM needs) and emphasized specific actions taken in response to past findings. Informed consent was waived due to military regulations involving force-wide surveys. As noted above, participation was anonymous and voluntary. The online questions took 20 to 45 min to complete per participant and could be completed across multiple sessions if desired. Most (74%) participants who began the survey completed it.

3. Measures

3.1. Demographics

To ensure participant anonymity, questions about demographic characteristics were limited to age, gender, marital status, years in the military, and pay grade. Questions were not asked about name, installation, squadron, or other potentially identifying information. Participants could choose not to answer any question in this and all sections of the survey.

3.2. Mental Health Problems

A limited set of mental health problems, namely substance use problems, depression, suicidality, and post-traumatic stress, were chosen for the focus of this survey. These same problems have been the focus of other large-scale surveys of military populations in recent years (e.g., Ayer et al., 2022). Multiple questions were asked about each problem, and scales were created from multiple questions. Each measure was scored so that high scores indicated more problems. Descriptions of each scale, sample items, possible ranges, and internal consistencies are listed in Table 1. More information about these measures is also available in Lorber et al. (2018).
Alcohol problems. Problem drinking was assessed with the 10-item Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993). Total scores range from 0 to 40. Following the World Health Organization’s recommended guidelines, all individuals who scored eight or greater on the AUDIT were classified as above the cut-off for hazardous drinkers (Rumpf et al., 2002); this cut-off has been shown to have 90% or greater specificity and sensitivity (e.g., Chen et al., 2005).
Prescription drug misuse. Participants were provided with a list of controlled prescription drugs (e.g., Codeine, Oxycodone). For each drug checked, participants were asked (a) the frequency of use when s/he did not have a prescription and (b) the frequency of use at a dosage greater than prescribed. Military focus groups indicated that the questions were clear and unambiguous. Prescription drug misuse was scored as present versus absent, a threshold that discriminates clinically significant psychopathology from its absence (Lorber et al., 2018).
Suicidality. Using items from the Center for Disease Control and Prevention Youth Risk Behavior Surveillance System (Brener et al., 2002), respondents were classified as positive for suicidality if they reported, in the last year, (a) thoughts of ending his/her life (sometimes, frequently); (b) seriously considering attempting suicide (rarely, sometimes, frequently) or (c) planning suicide. This operationalization of suicidality discriminates clinically significant psychopathology from its absence (Lorber et al., 2018).
Depressive symptoms. Seven items (Mirowsky & Ross, 1992) from the Center for Epidemiological Studies Depression Scale (Radloff, 1977) were included to assess how many days participants experienced symptoms of depression over the past week (from none to 5–7 days). Scores were averaged across items and ranged from 1 to 4. Scores of 3 or higher, marking the 98th percentile, were defined as problematic.
Post-traumatic stress. The Primary Care Post Traumatic Stress Disorder (PTSD) Screen (4 items; Prins et al., 2003) was used to screen for symptoms of post-traumatic stress symptoms. As suggested by Prins et al. (2003), endorsement of 3 or more symptoms was classified as problematic.
Total mental health problems. A total mental health problems score was calculated by counting the number of problems meeting the problematic thresholds (range = 0 to 5).

3.3. Mental Health Service Utilization or Intention to Use Mental Health Services

Participants were asked whether they had received counseling, mental health care, or other mental health assistance in the last two years (the time elapsed since the previous DAF Community Assessment was conducted) from each of nine sources: Airman and Family Readiness Center, civilian religious leaders, Defense Centers of Excellence Outreach Center, Military and Family Life Counseling Program, Military One Source, military chaplain, on-base mental health clinic (including from the Family Advocacy Program and Alcohol and Drug Abuse Prevention and Treatment), and/or primary care/family practice physician. These sources reflect the diversity of personnel who provide mental health services to SMs. Participants who did not report using any of these services during the past two years were asked the following: “How likely will you seek counseling or mental health care services in the next three months?” Answer choices were 1 = “not at all likely”, 2 = “somewhat likely”, 3 = “very likely”, and 4 = “absolutely certain”. Of note, these utilization questions could be about services received for any mental health problem or issue rather than only those queried in the survey.

3.4. Barriers and Attractors to Mental Health Services Use

As part of the development of the DAF Community Assessment, potential items for help-seeking constructs (i.e., Treatment Attractors, Negative Treatment Attitudes, Peer Stigma, Institutional Stigma, Knowledge Barriers, and Logistic Barriers) were compiled from literature searches and existing surveys used by the Department of Defense at large. Six senior officers, who were all licensed mental health providers serving in DAF leadership roles, served as subject matter experts, provided feedback on the aggregated list, and determined the items of greatest relevancy to SMs. The experts’ judgments provided content validity for the items intending to measure military mental health barriers and attractors. Items were rated on a 1–4 scale, and item averages were used for each scale. Items for the Knowledge Barriers and Logistic Barriers scales were only asked of participants who had not received mental health services in the past two years. Items on the Treatment Attractors scale were worded differently for participants who had and had not received mental health services in the past two years. For those who had not used mental health services, each question was posed hypothetically in the future tense. For those who had used services, each question was worded in the past tense with reference to their use of services.

4. Analytic Approach

To account for both oversampling at small AF installations as well as differential response rates for participants across the sampling variables, post-stratification weights were applied to the data to match the sample to the AF population. For partially completed surveys, missing data were imputed using the Sequential Regression Imputation Method (Raghunathan et al., 2002). Specifically, 200 iterations of multiple imputation were conducted, saving every tenth resulting dataset. The results were then combined to produce 20 full, multiply imputed datasets, and the values that had been imputed for legitimately “not applicable” data points were removed. A “group” variable was then created that sorted each participant into one of four mutually exclusive categories: Unmet Need—participants with at least one current mental health problem who had not received mental health services in the past two years; Met Need—participants with at least one current mental health problem who received services in the past two years; Other Need—participants with no current mental health problems who had received services in the past two years; and No Need—participants with no current mental health problems who had not received services. Subsequently, participants with Unmet Needs were compared with participants in the other categories on mental health problem scores as well as scores on the Treatment Attractors, Negative Treatment Attitudes, Peer Stigma, Institutional Stigma, Knowledge Barriers and Logistic Barriers subscales. Comparisons were conducted with multinomial regression using the generalized logistic model for multinomial responses in the SAS PROC SURVEYLOGISTIC program (SAS Institute, 2015). “Group” was individually regressed on each predictor. Multinomial regression reduces to a series of logistic regression equations comparing participants in one comparison category to the others. Gender and pay grade were controlled for in all analyses.

5. Results

5.1. Current Mental Health Problems

The prevalence of the current mental health problems assessed was 19.1%. Prevalence by problem type was as follows: problem drinking (8.8%), prescription drug misuse (7.9%), post-traumatic stress (6.3%), suicidality (2.8%), and depression (2.4%). While most participants with a mental problem were experiencing only one, 32.2% of these individuals were experiencing two or more problems.

5.2. Use of Mental Health Services

More than one-third of participants (35.3%) reported that they had received mental health services during the last two years. The named sources used were as follows (participants could report using more than one): primary care/family practice physician (52.6%), Airman and Family Readiness Center (38.6%), on-base mental health clinic (37.1%), military chaplain (30.9%), civilian religious leader (25.3%), Military One Source (20.9%), Military and Family Life Counseling Program (13.6%), and Defense Centers of Excellence Outreach Center (3.4%).

5.3. Use of Mental Health Services and Current Mental Health Problems

Table 2 lists the percentages of SMs within each analytic category. The largest category comprised those without one of the assessed problems and who did not receive services (No Need Category; 55.8%), followed by the category comprising those without one of the assessed problems who did receive services (Other Need; 25.0%). The two other categories, Unmet Need (9.2%) and Met Need (9.9%), were similar in size. Table 3 crosses these categories with their intention to seek services in the future. Nearly all members (89.1%; 8.2% divided by the total of 9.2%) in the Unmet Need category reported no intention of seeking services in the next three months.

5.4. Barriers and Attractors for Participants with Met and Unmet Needs

Group (i.e., the four need categories) was regressed on barriers and attractors, and comparisons were made between participants in the Unmet Need category and those in the other categories. Results are presented in Table 4. Compared with the No Need category, participants in the Unmet Need category (a) perceived more peer and institutional stigma, (b) reported more knowledge barriers and more logistic barriers, and (c) endorsed more negative attitudes about, and fewer attractors toward, mental health services. Similar results were found when contrasting SMs in the Unmet Need category with those not reporting current problems but who were receiving services (i.e., those in the Other Need category). Few areas differentiated the two categories reporting current problems (i.e., Unmet Need and Met Need), with no differences in perceived peer stigma or institutional stigma. Those who sought services, compared with those who had not, reported more mental health problems and more negative attitudes toward mental health services.

5.5. Attractors Within Met Need and Unmet Need Categories

The individual attractor items were ranked by their mean scores for participants in the Unmet Needs and Met Needs categories (see Table 5). The rank orders of item means were nearly identical for those with Unmet and Met Needs (Spearman’s ρ = 0.93, p < 0.001). The highest-rated attractors were one’s own beliefs rather than suggestions from others. SMs in both categories rated the attractors as being between slightly and moderately important. The difference in the importance ascribed to the most and least important attractor was moderate (Cohen’s d = 0.42) for SMs with Unmet Needs and large (Cohen’s d = 0.90) for SMs with Met Needs, implying that engaging in mental health services gives members a sharper delineation of the relative importance of attractors to services.

6. Discussion

We hypothesized that SMs with mental health problems and who have not sought services, compared with those with problems who have sought services and those without problems, will endorse higher rates of stigma and more negative treatment attitudes and will perceive greater barriers and fewer attractors to obtaining treatment. We found the opposite. SMs with problems who had not received services reported fewer mental health problems and lower negative attitudes towards mental health treatment than those who had received services. Further, SMs without the assessed mental health problems reported lower stigmatizing beliefs and barriers to mental health care, thought better of mental health treatment, and could see more gains for attending services than did SMs with problems but who had not received services in the past two years. Differences also were found between SMs with problems who had, and had not, received services in the past two years. Interestingly, however, SMs in these two groups did not differ on perceptions of peer or institutional stigma regarding mental health services.
This pattern of similarities and differences suggests that within this sample, stigma does not seem to have been a relevant factor in using services. Further, other findings here suggest that stigma may not have been relevant for even having the intention to use services, as 90% of SMs experiencing problems but who had not availed themselves of mental health services expressed no intention of using services. Of course, a key issue here, as in past studies, is that any differences related to the two distinguishing factors—the number of mental health problems and the degree of negative treatment attitudes—are difficult to interpret because these variables were collected after the receipt of services. For example, greater numbers of mental health problems for participants who received services might indicate that these individuals sought services in the first place because they had more problems, and despite these services, they continue to have more problems. In turn, higher negative treatment attitudes for those who received services might reflect disenchantment with the services that were received, including an awareness that despite treatment, they continue to struggle with certain problems.
Regardless of the remaining uncertainties regarding these and past findings, the importance of self-generated motivations in seeking treatment is apparent. For both participants in the Unmet Need and Met Need categories, the most important attractors to services are self-generated, and the least attractive are suggestions from others. This is consistent with the prevailing models of help-seeking noted in the introduction. Furthermore, this finding aligns with other findings on the importance of self-beliefs versus perceptions of the beliefs of others in regard to stigma and help-seeking (e.g., Hamilton et al., 2017).
This brings us back to the finding that nine out of ten SMs who reported having mental health problems but who had not received services in the past two years indicated they have no intention to seek services. This suggests that population-wide attempts to improve attitudes toward mental health services amongst SMs are unlikely to affect much change in service seeking, as the broader population of non-problematic SMs do not seem to hold stigmatizing beliefs in the first place, and there is a chasm between barriers/attractors and intentions to receive services (i.e., barriers/attractors are mostly seen the same way for service recipients and non-recipients despite their differences in service-seeking). Rather, focused attempts that target specific subpopulations of SMs seem more promising (e.g., Acosta et al., 2014).
For example, seen through the lens of the Transtheoretical Model (Prochaska & DiClemente, 1982), the unserved-but-problematic population comprises presumed pre-contemplators (for whom the processes of problem recognition are likely paramount) and presumed contemplators (for whom the balance between attractors and barriers becomes pertinent). Different strategies are needed to engage and sustain the attention of these different groups. One evidence-informed and evidence-based example of a multi-pronged approach is Triple P (Sanders, 2012), a prevention program that targets parent–child relationships and child behavior at school and in the community. First, universal messages are broadcast to the general population of parents to increase problem recognition and contemplation. Second, opportunities are provided for self-administered or brief evidence-based interventions to make uptake easy and to translate weak intentions into action. Finally, more intensive treatment options are provided for families who need more assistance. Multimodal packages designed to match the stage of change might be a useful approach for SMs in terms of mental health promotion and the treatment of mental health problems.

7. Strengths and Limitations

This study has significant strengths. We analyzed a large dataset drawn from a representative sample of SMs. The demographics for the sample paralleled that of the DAF active-duty workforce. Second, mental health problems were measured with widely used scales. Furthermore, responses appear similar to those of past samples on similar topics. For example, approximately one in five SMs reported at least one of the mental health problems assessed, a rate found in other recent surveys (e.g., Ayer et al., 2022; Kessler et al., 2014), taking into consideration that several common problems were not assessed in this survey (e.g., other anxiety disorders, adjustment disorders). In addition, approximately one in three SMs reported receiving mental health services at some point in the past two years, a rate also observed in other studies (e.g., Kehle et al., 2010). Third, multiple barriers to accessing services were assessed. Fourth, attractors to service access were also measured.
There were also limitations. Data were collected at one time point, limiting our ability to identify temporal associations. Second, data were collected during combat operations in Iraq and Afghanistan and may not be reflective of the current beliefs, needs, and service utilization patterns of SMs. Third, although several notable mental health problems that may result in service utilization were assessed, other mental health problems and issues were not. Clearly, because the vast majority of those both receiving services and intending on availing themselves of services report none of these problems currently, a broader menu of problems resulting in service utilization would be useful to assess. From our own work with military populations over the 30 years, and from findings of recent prevalence studies of military populations, it is likely that adjustment problems—including couple problems and/or parent–child problems—and anxiety problems other than post-traumatic stress, are the other major issues that often led to service contacts for this sample (e.g., see https://www.health.mil/News/Articles/2021/08/01/Update-MH-BH-MSMR, accessed on 10 December 2024). Finally, the wide range of training and expertise of military mental health services providers introduces further complexity in understanding findings on stigma and other cognitions. Specificity on type of problem(s), type of service provider(s), and perceived outcome(s) would be helpful in future surveys.

8. Implications

8.1. Theory

The key finding in this paper—that stigma did not appear to play much of a role in mental health service utilization in this sample—challenges the prevailing idea that stigma is the key cognition in SM utilization. This could be the result of the DAF’s social ecological context (Bronfenbrenner, 1979) during the study period: the US was at war, and perhaps during wartime, stigma is overshadowed by other factors (e.g., operations tempo) for certain SMs and groups of SMs. In addition, the finding that self-generated motivations for seeking treatment are of vital importance within the service-accessing SM subpopulation suggests that theoretical models for SMs need to incorporate intrinsic motivation along with other cognitive factors (e.g., barriers, attractors, attitudes) when explaining help-seeking behavior. Perhaps different theories of help-seeking are needed to account for SM subgroups, and that part of this heterogeneity is related to differences in cognitions based on past service use. A useful starting place for such theories seems to be the stages of change within the Transtheoretical Model (Prochaska & DiClemente, 1982).

8.2. Research

Studies that focus on the nuances of stigma in the SM population are very much needed. For example, future survey studies of cognitions relevant to service-seeking are needed to provide a better understanding of their role in SMs’ mental health and treatment. These studies would be well-served by broadening the assessment of mental health problem areas to capture each of the problems most likely to drive SMs to services. Further, the conduct of follow-up qualitative studies with the sizable number of SMs who admit problems but indicate no intention of seeking services for those problems are also needed. More information is needed about these SMs to generate hypotheses that can then be tested in additional quantitative studies.
Most importantly, whereas cross-sectional studies such as this one provide insights into how mental health problems, cognitions, and services are related at a given point in time, prospective longitudinal studies are needed to examine the relation of cognitions to subsequent mental health problems and help-seeking behavior. The conduct of a specific kind of prospective longitudinal study—the randomized controlled trial (RCT) of evidence-informed interventions designed to shift cognitions about service seeking and improve mental health and well-being—is important in order to make progress in the field. Scientifically rigorous RCTs that examine outcomes, mediators and moderators of outcomes are one of the best ways for scientists to examine causality and generate the data needed to improve theory and practice. In short, further attempts to understand the intricacies of stigma are sorely needed, particularly efforts that attempt to disentangle relations between stigma and other relevant cognitions, behaviors related to treatment, and subsequent outcomes.

8.3. Practice

The results of this study suggest that the treatment-seeking behaviors of SMs with mental health problems may be impacted by some cognitions, but neither peer nor institutional stigma appears to be particularly relevant. Treatment experiences are related to perceptions of stigma in this sample but not in the direction that was anticipated. This unexpected finding suggests that reducing stigma per se may be a less powerful strategy than assumed (Acosta et al., 2014, 2019; Cerully et al., 2018; Ursano et al., 2011) for engaging SMs in help-seeking when they need services. Furthermore, this finding suggests that a component of treatment needs to address expectations regarding outcomes and not just at one point in time. Given this, the treatment of mental health problems might better be thought about as a combination of intervention and prevention efforts—formal or not—over the long run rather than as a “one and done” intervention effort. This kind of approach may work with specific, constrained physical health problems (e.g., treatment with antibiotics to resolve an infection) but may not fit complex, socially embedded mental health problems.
Ultimately, finding ways to successfully engage SMs earlier on and throughout their mental health problem trajectories seems warranted, and this means including a multitude of approaches. Multilayered, targeted information campaigns that address a wide range of cognitions that inhibit help-seeking seem warranted, including stigma, treatment unawareness, negative attitudes about treatment, and perceptions of logistical barriers. Of particular importance appear to be barriers related to beliefs in treatment efficacy. Within these campaigns, information needs to be clear, not only when simple messages are broadcast widely but also when more detailed information is available on websites and social media. Furthermore, easing access to care, including embedding service providers within units (e.g., Martinez et al., 2023), seems warranted.
For SMs with unmet needs who are still reluctant to access services, engaging interventions are needed to cultivate the motivation needed to move forward. What works for one individual might not work for another (Paul, 1967), so a range of possibilities are needed. For example, psychoeducation that emphasizes the effectiveness of mental health services for specific kinds of problems might be helpful for some SMs, particularly if such an approach is designed to resonate with individual needs and values. Brief motivational interviewing sessions (Miller & Rollnick, 2023) could be helpful for other SMs.
Mapping such strategies to a theoretical approach, such as the “stages of change” from the Transtheoretical Model (Prochaska & DiClemente, 1982), seems particularly promising. For example, pre-contemplators could be targeted via social media public awareness campaigns that are designed to increase problem recognition. Strategies for contemplators could publicize attractors as well as how to overcome barriers and do so through a combination of public awareness campaigns, improved websites, and brief interventions. In contrast, for those who are already ready to move forward with services, easing access to services could be all the “intervention” that is needed. The key point is that one size does not fit all. Following the example of Triple P (Sanders, 2012), addressing the mental health needs of SMs might best be addressed through a combination of (a) evidence-informed, targeted mental health promotion campaigns with (b) interventions that address the particular needs of particular SMs at particular points in time, from “light touch” evidence-based prevention and health promotion efforts (e.g., apps, telehealth, in person) to a range of evidence-based mental health services.

9. Conclusions

We set out to examine differences and similarities in cognitions between key subgroups (i.e., presence of mental health problems × service utilization) within a large, representative sample of active-duty SM in the Department of the Air Force. We found that stigma—hypothesized to be of prime importance in help-seeking behaviors in active-duty SMs—may be less important than previously thought. Instead, self-motivation and a variety of other cognitive factors may be much more important. Prospective, longitudinal studies are needed to examine how cognitions and behaviors do or do not lead to help-seeking behaviors for active-duty SMs experiencing mental health symptoms. These studies need to consider a broader set of mental health problems. Furthermore, randomized controlled trials of promising interventions designed to impact cognitions and behaviors related to help-seeking are needed to test causal hypotheses. Active-duty service members are asked to risk not only their lives but also their physical and mental health and well-being in defense of their country. Promoting and protecting their health—and helping them when they have problems during or after their time in uniform—is the least we can do to say “thank you for your service”.

Author Contributions

Conceptualization, R.E.H., A.M.S.S. and R.E.F.; methodology, K.N.M.-B., M.F.L. and S.X.; formal analyses, K.N.M.-B., M.F.L. and S.X.; writing—original draft preparation, J.M.E., K.N.M.-B., R.E.H., A.M.S.S. and M.F.L.; writing—review and editing, J.M.E., R.E.H. and A.M.S.S.; supervision, R.E.H., A.M.S.S. and R.E.F.; project administration, R.E.H., A.M.S.S. and R.E.F.; funding acquisition, R.E.H. and A.M.S.S. All authors—except M.F.L. (who died before the final draft was completed)—have read and agreed to the published version of the manuscript. M.F.L. did agree to a similar version of this manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a grant from the United States Department of Agriculture’s National Institute of Food and Agriculture (2011-48740-31167).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki. Prior to data collection, the survey and protocol were reviewed and approved by the United States Department of the Air Force Survey Office and the United States Department of the Air Force Compliance Office. The New York University institutional review board ruled that this anonymous archival study was exempt from human subjects review. This decision was made in accordance with human research regulations outlined in the United States Department of Health and Human Services Common Rule (45 CFR § 46.104[d][4]) stipulating that research involving the use of existing data, documents, records, or specimens is exempt if the information is recorded in a manner that does not permit the identification of individuals, directly or through identifiers linked to the subjects.

Informed Consent Statement

Informed consent was waived due to military regulations involving force-wide surveys.

Data Availability Statement

These data were collected by the United States Department of the Air Force. The data that support the findings are available from the United States Air Force Medical Command but restrictions apply to their release (i.e., private information is protected, Human Research Protections Office protections are maintained).

Acknowledgments

The authors would like to thank all United States Department of Air Force personnel who have supported and contributed to this project. We are grateful to all the installation points of contact who facilitated our observations and data collection. Michael Lorber is now deceased. Kerry N. Makin-Byrd is now in private practice in Wellington, New Zealand. Lt Col Rachel Foster (Ret.) is now in the School of Social Work at the University of North Carolina, Chapel Hill.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The views expressed are those of the authors and do not reflect the official guidance or position of the United States Government, the United States Department of Defense, the United States Department of the Air Force, or the United States Space Force.

References

  1. Acosta, J. D., Ashwood, J. S., Schell, T. L., & Cerully, J. L. (2019). With small power, comes great responsibility: Lessons learned from an evaluation of veteran and military mental health public awareness campaigns. Community Mental Health Journal, 55(8), 1322–1325. [Google Scholar] [PubMed]
  2. Acosta, J. D., Becker, A., Cerully, J. L., Fisher, M. P., Martin, L. T., Vardavas, R., Slaughter, M. E., & Schell, T. L. (2014). Mental health stigma in the military. RAND. [Google Scholar]
  3. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. [Google Scholar] [CrossRef]
  4. Andrulis, D. P. (1998). Access to care is the centerpiece in the elimination of socioeconomic disparities in health. Annals of Internal Medicine, 129(5), 412–416. [Google Scholar] [CrossRef]
  5. Ayer, L., Ramchand, R., Karimi, G., & Wong, E. C. (2022). Co-occurring alcohol and mental health problems in the military: Prevalence, disparities, and service utilization. Psychology of Addictive Behaviors, 36(4), 419–427. [Google Scholar] [CrossRef]
  6. Becker, M. H. (1974). The Health Belief Model and personal health behavior. Health Education Monographs, 2(4), 409–419. [Google Scholar] [CrossRef]
  7. Brener, N. D., Kann, L., McManus, T., Kinchen, S. A., Sundberg, E. C., & Ross, J. G. (2002). Reliability of the 1999 youth risk behavior survey questionnaire. Journal of Adolescent Health, 31(4), 336–342. [Google Scholar] [CrossRef]
  8. Britt, T. W. (2000). The stigma of psychological problems in a work environment: Evidence from the screening of SMs returning from Bosnia. Journal of Applied Social Psychology, 30(8), 1599–1618. [Google Scholar] [CrossRef]
  9. Britt, T. W., Greene–Shortridge, T. M., Brink, S., Nguyen, Q. B., Rath, J., Cox, A. L., Hoge, C. W., & Castro, C. A. (2008). Perceived stigma and barriers to care for psychological treatment: Implications for reactions to stressors in different contexts. Journal of Social and Clinical Psychology, 27(4), 317–335. [Google Scholar] [CrossRef]
  10. Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Harvard University Press. [Google Scholar]
  11. Cerully, J. L., Acosta, J. D., & Sloan, J. (2018). Mental health stigma and its effects on treatment-related outcomes: A narrative review. Military Medicine, 183(11–12), 427–437. [Google Scholar] [CrossRef]
  12. Chen, C., Chen, W. J., & Cheng, A. T. (2005). New approach to the validity of alcohol use disorders identification test: Stratum-specific likelihood ratios analysis. Alcoholism: Clinical and Experimental Research, 29(4), 602–608. [Google Scholar] [CrossRef]
  13. Constantian, A. R. (1998). Mental health status and visit rates of active duty members and their families: Findings from the 1994–1995 Department of Defense health beneficiary survey. Military Medicine, 163(7), 471–476. [Google Scholar] [CrossRef]
  14. Cooper, A. E., Corrigan, P. W., & Watson, A. C. (2003). Mental illness stigma and care seeking. Journal of Nervous and Mental Disease, 191(5), 339–341. [Google Scholar] [CrossRef] [PubMed]
  15. Corrigan, P. (2004). How stigma interferes with mental health care. American Psychologist, 59(7), 614–625. [Google Scholar] [CrossRef]
  16. Department of the Air Force. (2025). Military demographics. Available online: https://www.afpc.af.mil/Portals/70/documents/DEMOGRAPHICS/MilDemographics%20Q1%202025.pdf (accessed on 21 March 2025).
  17. Greene-Shortridge, T. M., Britt, T. W., & Castro, T. A. (2007). The stigma of mental health problems in the military. Military Medicine, 172(2), 157–161. [Google Scholar] [CrossRef] [PubMed]
  18. Hamilton, J. A., Coleman, J. A., & Davis, W. J. (2017). Leadership perspectives of stigma-related barriers to mental health care in the military. Military Behavioral Health, 5(1), 81–90. [Google Scholar] [CrossRef]
  19. Heyman, R. E., Smith Slep, A. M., Parsons, A. M., Ellerbeck, E. L., & McMillan, K. K. (2022). Systematic review of the military career impact of mental health evaluation and treatment. Military Medicine, 187(5/6), e598–e618. [Google Scholar] [CrossRef] [PubMed]
  20. Hoge, C. W., Castro, C., Messer, S. C., McGurk, D., Cotting, D. I., & Koffman, R. L. (2004). Combat duty in Iraq and Afghanistan, mental health problems and barriers to care. The New England Journal of Medicine, 351(1), 13–22. [Google Scholar] [CrossRef]
  21. Hoge, C. W., Lesikar, S. E., Guevara, R., Lange, J., Brundage, J. F., Engel, C. C., Messer, S. C., & Orman, D. T. (2002). Mental disorders among U.S. military personnel in the 1990s: Association with high levels of health care utilization and early military attrition. American Journal of Psychiatry, 159(9), 1576–1583. [Google Scholar] [CrossRef]
  22. Hom, M. A., Stanley, I. H., Schneider, M. E., & Joiner, T. E., Jr. (2017). A systematic review of help-seeking and mental health service utilization among military service members. Clinical Psychology Review, 53, 59–78. [Google Scholar] [CrossRef]
  23. ICF International. (2012). 2011 demographics: Profile of the military community. ICF International. [Google Scholar]
  24. Kasper, J. D. (1998). Asking about access: Challenges for surveys in a changing healthcare environment. Health Services Research, 33(3 Pt 2), 714–766. [Google Scholar]
  25. Kehle, S. M., Polusny, M. A., Murdoch, M., Erbes, C. R., Arbisi, P. A., Thuras, P., & Mei, L. A. (2010). Early mental health treatment-seeking among U.S. National Guard soldiers deployed to Iraq. Journal of Traumatic Stress, 23(1), 33–40. [Google Scholar] [CrossRef]
  26. Kessler, R. C., Berglund, P. A., Bruce, M. L., Koch, J. R., Laska, E. M., Leaf, P. J., Manderscheid, R. W., Rosenheck, R. A., Walters, E. E., & Wang, P. S. (2001). The prevalence and correlates of untreated serious mental illness. Health Services Research, 36(6 Pt 1), 987–1007. [Google Scholar] [PubMed]
  27. Kessler, R. C., Heeringa, S. G., Stein, M. B., Colpe, L. J., Fullerton, C. S., Hwang, I., Naifeh, J. A., Nock, M. K., Petukhova, M., Sampson, N. A., Schoenbaum, M., Zaslavsky, A. M., Ursano, R. J., & Army STARRS Collaborators. (2014). Thirty-day prevalence of DSM-IV mental disorders among nondeployed soldiers in the US Army: Results from the Army study to assess risk and resilience in service members. JAMA Psychiatry, 71(5), 504–513. [Google Scholar]
  28. Kim, P. Y., Britt, T. W., Klocko, R. P., Riviere, L. A., & Adler, A. B. (2011). Stigma, negative attitudes about treatment, and utilization of mental health care among soldiers. Military Psychology, 23(1), 65–81. [Google Scholar] [CrossRef]
  29. Kim, P. Y., Thomas, J. L., Wilk, J. E., Castro, C. A., & Hoge, C. W. (2010). Stigma, barriers to care, and use of mental health services among active duty and National Guard soldiers after combat. Psychiatric Services, 61(6), 582–588. [Google Scholar] [CrossRef] [PubMed]
  30. Leaf, P. J., Bruce, M. L., Tischler, G. L., & Holzer, C. E. (1987). The relationship between demographic factors and attitudes toward mental health services. Journal of Community Psychology, 15(2), 275–284. [Google Scholar] [CrossRef] [PubMed]
  31. Lorber, M. F., Xu, S., Heyman, R. E., Slep, A. M. S., & Beauchaine, T. P. (2018). Patterns of psychological health problems and family maltreatment among United States Air Force members. Journal of Clinical Psychology, 74(7), 1258–1271. [Google Scholar] [CrossRef]
  32. Martinez, R. N., Galloway, K., & Thompson, C. (2023). The potential of an embedded mental health services program toward increasing health care-seeking behaviors among U.S. Air Force aircrew: A mixed-methods study. Military Medicine, 188(Suppl. S6), 262–270. [Google Scholar] [CrossRef]
  33. Meadows, S. O., Engel, C. C., Collins, R. L., Beckman, R., Cefalu, M., Hawes-Dawson, J., Doyle, M., Kress, A. M., Sontag-Padilla, L., Ramchand, R., & Williams, K. M. (2018). 2015 Department of defense health related behaviors survey (HRBS) of active duty service members: Final report (RR-1695-OSD). RAND. [Google Scholar]
  34. Michalopoulou, L. E., Welsh, J. A., Perkins, D. F., & Ormsby, L. (2017). Stigma and mental health service utilization in military personnel: A review of the literature. Military Behavioral Health, 5(1), 12–25. [Google Scholar] [CrossRef]
  35. Miller, W. R., & Rollnick, S. (2023). Motivational interviewing: Helping people change and grow (4th ed.). Guilford Press. [Google Scholar]
  36. Mirowsky, J., & Ross, C. E. (1992). Age and depression. Journal of Health and Social Behavior, 33(3), 187–205. [Google Scholar] [CrossRef]
  37. Paul, G. L. (1967). Strategy of outcome research in psychotherapy. Journal of Consulting Psychology, 31(2), 109–118. [Google Scholar] [CrossRef] [PubMed]
  38. Prins, A., Quimette, P., Kimerling, R., Cameron, R. P., Hugelshofer, D. S., Shaw-Hegwer, J., Thrailkill, A., Gusman, F. D., & Sheikh, J. I. (2003). The primary care PTSD screen (PC-PTSD): Development and operating characteristics. Primary Care Psychiatry, 9(1), 9–14. [Google Scholar] [CrossRef]
  39. Prochaska, J. O., & DiClemente, C. C. (1982). Transtheoretical therapy: Toward a more integrative model of change. Psychotherapy: Theory, Research & Practice, 19(3), 276–288. [Google Scholar] [CrossRef]
  40. Quartana, P. J., Wilk, J. E., Thomas, J. L., Bray, R. M., Rae Olmsted, K. L., Brown, J. M., & Hoge, C. W. (2014). Trends in mental health services utilization and stigma in US soldiers from 2002 to 2011. American Journal of Public Health, 104(9), 1671. [Google Scholar] [CrossRef]
  41. Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. [Google Scholar] [CrossRef]
  42. Raghunathan, T. E., Solenberger, P. W., & Van Hoewyk, J. (2002). IVEware: Imputation and variance estimation software user guide. University of Michigan. [Google Scholar]
  43. Rumpf, H., Hapke, U., Meyer, C., & John, U. (2002). Screening for alcohol use disorders and at-risk drinking in the general population: Psychometric performance of three questionnaires. Alcohol and Alcoholism, 37(3), 261–268. [Google Scholar] [CrossRef]
  44. Sanders, M. R. (2012). Development, evaluation, and multinational dissemination of the Triple P-Positive Parenting Program. Annual Review of Clinical Psychology, 8, 345–379. [Google Scholar] [CrossRef]
  45. SAS Institute. (2015). Base SAS 9.4 procedures guide. SAS Institute. [Google Scholar]
  46. Saunders, J. B., Aasland, O. G., Babor, T. F., de la Fuente, J. R., & Grant, M. (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction, 88(6), 791–804. [Google Scholar] [CrossRef] [PubMed]
  47. Sharp, M. L., Fear, N. T., Rona, R. J., Wessely, S., Greenberg, N., Jones, N., & Goodwin, L. (2015). Stigma as a barrier to seeking health care among military personnel with mental health problems. Epidemiological Reviews, 37(1), 144–162. [Google Scholar] [CrossRef]
  48. Sirey, J. A., Bruce, M. L., Alexopoulos, G. S., Perlick, D. A., Friedman, S. J., & Meyers, B. S. (2001a). Stigma as a barrier to recovery: Perceived stigma and patient-rated severity of illness as predictors of antidepressant drug adherence. Psychiatric Services, 52(12), 1615–1620. [Google Scholar] [CrossRef]
  49. Sirey, J. A., Bruce, M. L., Alexopoulos, G. S., Perlick, D. A., Raue, P., Friedman, S. J., & Meyers, B. S. (2001b). Perceived stigma as a predictor of treatment discontinuation in young and older outpatients with depression. American Journal of Psychiatry, 158(3), 479–481. [Google Scholar] [CrossRef] [PubMed]
  50. Stecker, T., Fortney, J., Hamilton, F., & Ajzen, I. (2007). An assessment of beliefs about mental health care among veterans who served in Iraq. Psychiatric Services, 58(10), 1358–1361. [Google Scholar] [CrossRef] [PubMed]
  51. Tsai, J., Harpaz-Rotem, H., Armour, C., Southwick, S. M., Krystal, J. H., & Pietrzak, R. H. (2015). Dimensional structure of DSM-5 posttraumatic stress disorder symptoms: Results from the national health and resilience in Veterans study. Journal of Clinical Psychiatry, 76(5), 546–553. [Google Scholar] [CrossRef] [PubMed]
  52. Ursano, R. J., Fullerton, C. S., & Brown, M. C. (2011). Stigma and barriers to care. Uniformed Services University of the Health Sciences. Available online: https://www.cstsonline.org/assets/media/documents/CSTS_report_stigma_exec%20summary%202012.pdf (accessed on 13 May 2021).
  53. Vogt, D. (2011). Mental health-related beliefs as a barrier to service use for military personnel and veterans: A review. Psychiatric Services, 62(2), 135–142. [Google Scholar] [CrossRef]
  54. Wright, K. M., Cabrera, O. A., Bliese, P. D., Adler, A. B., Hoge, C. W., & Castro, C. A. (2009). Stigma and barriers to care in soldiers post combat. Psychological Services, 6(2), 108–116. [Google Scholar] [CrossRef]
Table 1. Barriers and Attractors Scales.
Table 1. Barriers and Attractors Scales.
ScaleDescription/Sample ItemItemsPossible Rangeα
Attractors to Mental Health Treatment Potential reasons for mental health service seeking.
“I believe that mental health care would help me”.		91–5 a
1–4 b		0.91 a
0.96 b
Negative Treatment AttitudesBelief that mental health services are not helpful.
“Mental health care professionals are not useful as a way to deal with life’s problems”.		61–40.80
Peer
Stigma		Belief that Air Force co-workers would have less confidence in the participant if s/he sought mental health services.
“Co-workers would criticize or make fun of me”.		41–40.85
Institutional
Stigma		Belief that mental health services are not confidential and would negatively impact a person’s Air Force career.
“Seeking mental health care would hurt my career”.		51–40.79
Knowledge Barriers to ServicesConcerns that the participant would not know how to seek help.
“I would not know where to get help”.		21–40.79 b
Logistic Barriers
to Services		Day-to-day difficulties that would impact seeking and maintaining services.
“It would be difficult for me to get time off of work”.		61–40.85 b
Note. a = for those who used mental health services. b = for those who did not use mental health services.
Table 2. Percentage within each Analytic Group.
Table 2. Percentage within each Analytic Group.
Received Mental Health Services at Some Point During Past Two YearsAt Least One Reported Mental Health Problem Now
NoYes
No (64.7%)Group 1: No Need 55.8%Group 2: Unmet Need 9.2%
Yes (35.3%)Group 3: Other Need 25.0%Group 4: Met Need 9.9%
Table 3. Intent to Seek Services in Next Three Months within Analytic Groups.
Table 3. Intent to Seek Services in Next Three Months within Analytic Groups.
Received Mental Health Services at Some Point During Past Two YearsAt Least One Reported Mental Health Problem Now
NoYes
No Group 1: No NeedGroup 2: Unmet Need
Do Not Intend to Seek Services in Next Three Months86.7%13.3%
[8.2% of sample]
Do Intend to Seek Services in Next
Three Months		66.1%33.9%
[1% of sample]
Yes Group 3: Other NeedGroup 4: Met Need
71.6%28.4%
[9.9% of sample]
Table 4. Multinomial Regression Results.
Table 4. Multinomial Regression Results.
Variable/Category ComparisonBSEtp
Total Mental Health Problems
   Met Need vs. Unmet Need0.710.04295.84<0.001
Peer Stigma
   Met Need vs. Unmet Need−0.020.030.400.53
   Other Need vs. Unmet Need−0.470.03266.48<0.001
   No Need vs. Unmet Need−0.430.03276.05<0.001
Institutional Stigma
   Met Need vs. Unmet Need0.020.040.230.63
   Other Need vs. Unmet Need−0.480.04162.90<0.001
   No Need vs. Unmet Need−0.470.03194.02<0.001
Negative Attitude toward Mental Health Services
   Met Need vs. Unmet Need 0.080.044.470.03
   Other Need vs. Unmet Need−0.400.03136.42<0.001
   No Need vs. Unmet Need−0.280.0389.81<0.001
Knowledge Barriers
   No Need vs. Unmet Need−0.230.02106.65<0.001
Logistic Barriers
   No Need vs. Unmet Need−0.350.02217.21<0.001
Mental Health Services Attractors
   No Need vs. Unmet Need0.180.0348.09<0.001
Table 5. Attractors to Services by Category.
Table 5. Attractors to Services by Category.
Service Members with Unmet NeedsService Members with Met Needs
Attractors by CategoryMSDMSD
(If I thought) [I believed] that seeking mental health care would help me 2.581.022.561.07
(If) My emotional/personal problems were interfering with my daily activities and responsibilities 2.521.012.491.16
(If I believed) [I thought] that mental health care would help me 2.501.022.521.12
If I had thoughts or feelings that are frightening or distressing 2.471.042.151.15
(If) I received treatment before and found it useful2.381.012.021.06
(If) my family and/or friends recommended I go to mental health care 2.330.961.921.04
(If) someone I knew received treatment before and found it useful 2.280.961.951.02
(If) my physician recommended mental health care treatment 2.280.951.750.97
(If) my commander/supervisor thought that mental health care would be good for me 2.170.951.660.94
Note. Item phrasing for those with unmet needs is in parentheses; item phrasing for met needs is in brackets. Scale options: 1 = “not at all important”, 2 = “somewhat important”, 3 = “very important”, and 4 = “extremely important”.
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Eddy, J.M.; Heyman, R.E.; Smith Slep, A.M.; Lorber, M.F.; Xu, S.; Makin-Byrd, K.N.; Foster, R.E. A Large-Scale Survey of Barriers and Attractors to Mental Healthcare Utilization for Active-Duty Service Members in the U.S. Department of the Air Force. Psychol. Int. 2025, 7, 30. https://doi.org/10.3390/psycholint7020030

AMA Style

Eddy JM, Heyman RE, Smith Slep AM, Lorber MF, Xu S, Makin-Byrd KN, Foster RE. A Large-Scale Survey of Barriers and Attractors to Mental Healthcare Utilization for Active-Duty Service Members in the U.S. Department of the Air Force. Psychology International. 2025; 7(2):30. https://doi.org/10.3390/psycholint7020030

Chicago/Turabian Style

Eddy, J. Mark, Richard E. Heyman, Amy M. Smith Slep, Michael F. Lorber, Shu Xu, Kerry N. Makin-Byrd, and Rachel E. Foster. 2025. "A Large-Scale Survey of Barriers and Attractors to Mental Healthcare Utilization for Active-Duty Service Members in the U.S. Department of the Air Force" Psychology International 7, no. 2: 30. https://doi.org/10.3390/psycholint7020030

APA Style

Eddy, J. M., Heyman, R. E., Smith Slep, A. M., Lorber, M. F., Xu, S., Makin-Byrd, K. N., & Foster, R. E. (2025). A Large-Scale Survey of Barriers and Attractors to Mental Healthcare Utilization for Active-Duty Service Members in the U.S. Department of the Air Force. Psychology International, 7(2), 30. https://doi.org/10.3390/psycholint7020030

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