The relative impact of barriers to care among military health services personnel: exploring differences using context specific scenarios

Participants were invited to complete an online questionnaire that included items measuring barriers, personal characteristics, and access to care using vignettes. After consenting to the study, participants were randomly assigned to complete one of two versions of the survey: access to care for mental health issues or access to care for physical health issues [24, 29]. Prior to completing the barrier items, participants read a prompt that indicated that the barrier items either related to seeking care for physical health issues exclusively or mental health issues exclusively. Aside from this, the 52-barrier items (see Additional file 1) were identical across the two surveys. Participants rated the extent to which the barrier item would prevent them from seeking care using a 6-point scale (1- Extremely Unlikely to 6- Extremely Likely).

All participants completed demographic, military characteristic, and health-related items across both versions of the survey. Intent to access care and self-rated health were survey specific (see below). Intent to access care was assessed through a single item: “When faced with a [physical/mental] health issue, I intend to access care” rated on a 7-point scale (1- Strongly Disagree to 7- Strongly Agree). Self-rated health was assessed using a single item: “In general, how would you rate your [physical/mental] health?”, rated on a 5-point scale (1- Very Poor to 5- Excellent).

In each version of the survey, participants were presented with two hypothetical four-step scenarios detailing symptoms related to: depression and post-traumatic stress disorder (PTSD) in the mental health survey; and pneumonia and back injury in the physical health survey (see Additional file 2). Each vignette was developed and vetted by the appropriate health professionals to ensure accurate progression of symptoms to the point where the individual should objectively seek formal care. At each step, participants were asked to choose which action they would take in the presented scenario. The responses were dichotomized at step three, the first step where access to care was the only appropriate action: not accessing care (i.e., choosing “I would do nothing/wait and see”, “I would self-treat”, or “I would informally consult a colleague or peer”) was coded as one, while accessing care (i.e., choosing “I would seek formal treatment using CAF health services”, or “I would seek formal treatment using civilian health services”), was coded as zero.

The prevalence of each barrier type was calculated as the percentage of participants who positively endorsed at least one item within a factor. The choice was made to dichotomize barrier score due to a non-linear relationship between the factors and care-seeking. Barrier items were grouped into eight barrier factors as defined in previous research by Frank and Born [24]. Table 1 provides a brief description of the factors and their psychometric properties. Barrier factors included: CAF HCP identity (e.g., If I accessed care, members of my unit might have less confidence in me as a health care provider), Discomfort accessing care at work (e.g., I’m uncomfortable receiving care from colleagues), Conflict with career goals (e.g., Accessing care would harm my career), Staffing and workload resources (e.g., My workload is too heavy for me to leave and access care), Knowledge and ability to access care (e.g., I don’t know how to access the services available to me), Organizational and social support (e.g., My chain of command discourages the use of health services), Treatment preferences (e.g., I want to solve the problem on my own rather than access care), and Concerns about confidentiality (e.g., When I seek care, my medical file may be seen by those who shouldn’t access it). Six barrier items that did not load onto factors were grouped into two additional categories: (1) Past experiences and expectations, and (2) Location related issues. These two additional factors were treated as individual characteristics in the analysis.

Intentions to access care was analyzed as a dichotomous variable with weak intentions (those reporting 1- Strongly Disagree to 5- Somewhat Agree) to the question “When faced with a [physical/mental] issue, I intend to access care” coded as one and strong intentions (those reporting 6- Agree and 7- Strongly agree) coded as zero. Self-rated health was also dichotomized to quantify the proportion in poor health, with poor health coded as one (those reporting 1- Very Poor or 2- Poor) and good health (those reporting 3- Fair to 5- Excellent) coded as zero.

Initially, we used bivariate analysis to calculate the crude odds ratio (crude OR) for not accessing care in each scenario separately for each type of barrier and for all individual characteristics. Then, odds ratios (aORs) were adjusted by logistic regression for variables considered a priori as potential confounders. To account for missing data, the “mi impute mvn” command in Stata [30] with an iterative Markov Chain Monte Carlo (MCMC) method [31] was used to augment data (30 imputations). The estimates, standard errors and 95% confidence intervals, were calculated using the “mi estimate” command [32]. Two models were computed for each outcome, one with and one without, intention to access care. Models were assessed using the largest fraction of missing information (FMI; for number of imputations ≥ 100 × FMI), average relative increases in variance (RVI; closer to zero the less effect missing data had on the variance estimate), and F-Tests (p < 0.05). All analyses were performed using Stata software (Version 14 [33];).

Each barrier factor had a similar prevalence in both the mental health and physical health samples. The most endorsed barrier factors in both surveys were: Discomfort accessing care at work, Treatment preferences (e.g., self-treat), Staffing and workload resources, and Conflict with career goals.

Compared to males, females had significantly higher crude odds of not accessing care for pneumonia-like symptoms in the scenarios. Those who prefer to access care in English had higher crude odds of not accessing care for the back injury scenario and for the depression scenario compared to those who prefer to access care in French. Several associations were noted across military characteristics: the crude ORs were higher for those posted to rural locations, compared to those posted to urban areas for the back injury scenario; higher for junior officers, compared to junior non-commissioned members for the pneumonia scenario; and higher for those in specialty occupations, compared to core clinical trades for the depression scenario. Those with poor self-rated mental health had higher crude ORs in both mental health scenarios compared to those reporting better self-rated health. The crude ORs in the physical health scenarios did not differ significantly by self-rated physical health. Those reporting weak general intentions to access care had higher crude ORs across all scenarios. The two additional factors, associated with location related service issues, and experience and expectations about care, also produced significant estimates across all scenarios.

All eight barrier factors were significantly associated with not seeking care in all four vignettes. Specifically, endorsement of a barrier factor was associated with statistically higher crude odds of not accessing care in all the scenarios, compared to those who did not endorse the barrier factor. Staffing and workload resources and Treatment preferences (e.g., self-treat) were the strongest predictors of not accessing care in the mental health scenarios. Staffing and workload resources and Conflict with career goals were the strongest predictors of not accessing care in the physical health scenarios. Discomfort accessing care at work, Concerns about confidentiality, and Knowledge and ability to access care were the weakest barriers associated with not accessing care across all the scenarios. Crude OR estimates ranged from 1.87 to 2.91 for the pneumonia vignette, 2.24 to 4.53 for the back injury vignette, 2.48 to 4.03 for the depression vignette, and 2.74 to 4.71 for the PTSD vignette.

In the model that included intentions, holding all other variables constant, the odds of not accessing mental health care were significantly higher for those endorsing barriers related to resources (aOR_depression = 2.64; aOR_PTSD = 2.01) and treatment preferences (e.g., self-treat) (aOR_depression = 1.79; aOR_PTSD = 2.17). None of the eight barrier factors were significant predictors of accessing physical health care (at p < 0.05). Weak general intentions to access care was the only statistically significant predictor in the model across all scenarios (aOR_pneumonia = 3.39; aOR_{back injury} = 3.28; aOR_depression = 3.38; aOR_PTSD = 2.84). Age and rank were the only demographic and military characteristic predictors with statistically significant aORs, but these differences were only noted for not accessing care in the pneumonia scenario. Having accessed care in the past was significantly associated with not accessing care in the physical health scenarios (aOR_pneumonia = 0.50; aOR_{back injury} = 0.47), but not in the mental health scenarios, where past care predicted increased likelihood of accessing care. Poor health predicted not accessing care in the depression scenario (aOR_depression = 2.14), while the past experiences and expectations factor predicted not accessing care only in the pneumonia scenario (aOR_pneumonia = 2.01). Models not controlling for weak intentions to access care presented similar results.

All the barrier factors significantly increased the odds of not accessing care in both the mental and physical health scenarios. Of note, the crude odds ratios of not seeking care for barriers related to self-treatment, identity, and resources were reasonably large, with some crude ORs above four, indicating that the odds of not accessing formal care when needed in the scenarios was at least four times higher for those who reported these barriers compared to those who did not. This finding is both statistically and clinically significant, and these estimates are divergent from those for actual care-seeking propensity published in other studies of military populations (e.g., Sudom et al., noted crude ORs between 0.7 and 1.6 for all measured barriers [17] noting that negative attitudes were negatively associated, while stigma and structural barriers were positively associated, with hypothetical access to care). Also of note, aside from Staffing and workload resources and Treatment preferences (e.g., preference to self-treat), the barrier factors with the highest endorsement were not the most impactful. Together these findings suggest that perceived impact of barriers may not be as predictive of behaviour as previously thought. For example, barriers related to the discomfort accessing care at work were highly endorsed but were not the strongest predictors of avoiding accessing care in the scenarios. Our results suggest that although respondents perceived accessing care at their workplace as a key barrier, many respondents who endorsed this barrier still indicated they would access care in the scenarios.

Then we modelled the association between the barrier factors and the health scenarios while controlling for the demographic, military, and health variables. Weak intent to access care was consistently the strongest predictor of not accessing both physical and mental health care in the scenarios, even when controlling for other barriers, demographic, military, and health variables. The findings appear to be in line with many studies that have reported that intention is a consistent predictor of health care providers’ professional behaviour [34, 35].

The literature suggests that cognitive factors (such as beliefs about capabilities, beliefs about consequences, social influences, professional role, and identity), past behaviour, emotion, and to a lesser extent, environmental influences, and knowledge often influence a health care professional’s intentions towards a variety of behaviours such as care seeking [34]. In other words, the other barrier factors included in this study may be indirectly impacting care seeking through their association with intent to access care. Thus, interventions targeting barriers related to these domains may concurrently reduce barriers and improve intentions to access care which should subsequently improve access to care.

Interestingly, none of the barrier factors significantly increased the odds of hypothetically seeking physical health care in the adjusted models. However, staffing and workload resources was a marginally significant predictor of the likelihood of not accessing care for the back pain scenario, but not for the pneumonia scenario. It is important to note that though we have created distinct barrier factors, they are derived from theoretical domains that have conceptual overlap. As an example, barriers in the organizational and social support factor relate to how the perceptions or actions of others influence whether an individual accesses care, which could also impact one’s identity (CAF HCP identity factor). Though there were no issues with multicollinearity in the model (and no univariate correlations greater than 0.60), the conceptual overlap between the factors may be contributing to the lack of significant findings in the model. As discussed above, barriers may be indirectly influencing access to care in the scenarios through intent to access care. Even if the barrier factors were not significant in the adjusted model, they did have strong associations with hypothetical physical care seeking when examined alone. Additionally, the adjusted models themselves were significant, suggesting that the barriers are playing a role.

Endorsing Staffing and workload resources significantly increased the odds of not accessing care across both mental health conditions. This barrier factor includes barriers such as not having time to access care and not wanting to leave their colleagues short staffed. Treatment preferences (e.g., preference to self-treat) also was a significant predictor in the adjusted mental health models (but not in the physical health models). This barrier factor includes barriers such preferring to solve problems on their own, preferring to treat others first, or anticipating that a problem will get better on its own. This finding is in line with research that has found higher estimates for the preference to self-treat in the mental health context compared to the physical health context in samples of military members [16] and healthcare providers [36].

As mentioned above, the lack of significance in the adjusted model does not necessarily indicate that no other barriers are impactful. However, the findings do suggest that resources and preferences to self-treat are strong barriers when considering whether to access mental health care. Studies have used behavioural change techniques such as mental rehearsal and goal setting/planning to address motivation constructs [19] and evidence suggests that providing information about the consequences of not accessing care could target attitudinal and emotional barriers related to the preference to self-treat [19, 37]. However, we must remember that accessing care is not a single action. Behaviour often involves a complex sequence of actions over time [38] and the barriers may differ at each step. For example, a CAF health care provider may need to first recognise the need for formal care, deal with resource issues when scheduling a time to seek care, then face privacy concerns to attend care at their place of work. The impact of a barrier on a behaviour may be attenuated by one or more barriers acting at a different step in the access to care pathway [38]. Just as the barriers encountered at each step differ, so might the processes required to change behaviour. Interventions that target barriers risk not improving access to care behaviour if other barriers in the sequence are not also addressed or poorly defined. Recommendations by Presseau and colleagues highlight that the action, actor, context, target, and time should be detailed for each behaviour [39]. Therefore future studies should explore and define the steps to accessing to care in order to determine the sequencing of step-specific barriers in the access to care pathway.

Though much of the literature has focused on stigma as the top barrier to mental health care in military populations [25], more recent studies have noted the relative importance of barriers related to intentions [16], attitude towards care [17], and capabilities (such as resources, support and structural barriers [17]). Stigma related items were classified under the CAF HCP identity factor, (including items such as “I feel that others will discriminate against me if I access care” and “I feel embarrassed when I have to access care”). The unadjusted odds ratio for CAF HCP identity predicting not accessing care for mental health was relatively high. However, CAF HCP identity was not one of the most endorsed factors and, in the adjusted models, was not a significant predictor of hypothetical access to care in any of the scenarios. This suggests that though stigma remains an important construct to consider, its influence on hypothetical behaviour may be indirect or simply an indicator of motivation.

Several sources of bias may have resulted from the study methods and design. Though the use of multiple imputation to account for missing data is a strength of this study, it is also has certain limitations. Multiple imputation allows for limited types of analyses and, in the case of logistic regressions, there are limited model fit statistics available. However, we believe the strength of imputing missing data is stronger than the limitation of restricted analytical options and outputs.

Although we were able to impute missing data, we must note that less than half of the target population completed the survey. Therefore the results may not be generalizable to the entire CAF health care provider population. We expect that the experience of certain types of barriers may also be associated with non-participation. For example, those who experience resource issues, such as lack of time to seek care, likely also lack the time to complete a survey. Another limitation is that some of the barriers are specific to CAF HCPs (e.g., healthcare provider identity or accessing care at work), and may differ from those experienced by other military trades or civilian providers, particularly those who do not seek treatment where they are employed. However, the items could be modified (e.g., items removed or reworded) to better reflect barriers experienced by other populations. Furthermore, we designed this study using the original TDF [22], with 12-domains. We measured intention using a single item similar to what had been used in other research (e.g., Squires and colleagues [40]). However, the revised TDF by Cane and colleagues [28] includes intention as a discrete domain. Thus, intention should have been assessed using multiple items as recommended by as Huijg and colleagues [41].

As with most research, all predictors and outcomes were self-report and thus subject to known biases [42, 43]. We were not able to measure actual care seeking behaviour. The literature suggests that subjective measures (i.e., self-report) are more effective at predicting a self-reported behaviour outcome than objective measures [44], thus participant’s predicted behaviour in the hypothetical scenarios is likely not a perfect reflection of what they would actually do in that scenario [43‐45] and may inflate the proportion of variance explained in the models. Future studies should explore whether the results of this study are reproducible using measures of actual behaviour.

Despite the limitations, this is the first published example of a study that has quantified the impact of a variety of barriers to care developed using the TDF. Interventions designed to change behavior rely on, and are heavily impacted by, the accuracy of assessments. Therefore, employing effective methodologies [46, 47] to identify barriers and can lead to better interventions. Unique to this study, we were able to demonstrate the relative impact of a breadth of theory-based barrier factors on hypothetical care-seeking across a variety of health contexts (i.e., two discrete mental health issues; and both an acute and chronic physical health issue). The use of vignettes helps to define the context of the behaviour [33, 48], allows us to highlight key differences in actions between health conditions. Studies that are too narrowly focused on a single type of barrier, or context overlook important factors and miss opportunities to intervene. For example, preference to self-treat significantly impacted hypothetical care-seeking for mental health issues. Other research on barriers to care, has not included these types of barriers before. Thus, any recommended interventions would fail to address this barrier type.

Additionally, the use of theory-based barrier factors provides a deeper understanding of access to care issues using established methods and psychological constructs [22, 23, 28]. This research will enable the development of context specific and evidence-based interventions to improve access to care by targeting psychosocial constructs [19] that, unlike demographics, are amenable to change. For example, our results suggest that interventions should target motivation (such as one’s intention to self-treat and conflicts with career goal) and opportunity (such as context specific induced lack of time) in order to improve access to both physical and mental health care. The behavioural change literature proposes goal setting could address barriers related to motivation [19]. Specifically, having health care providers set health-related goals may increase their intention to access care. Modelling behaviours is another intervention that targets motivational barriers. Specifically, ensuring that senior HCPs access care when needed and model care seeking behaviour should increase intention to access care. Additionally, educating health care providers on the consequences of self-treatment may address barriers related treatment-preference [19]. Staffing and workload resources are likely better addressed at the organizational-level. Specifically, providing health care personnel with mandated time off to access care may alleviate opportunity barriers.