Over 50 million Americans experience mental illness in a given year [1,2], but only one-third of those diagnosed with a mental health condition receive treatment from a specialist mental health care provider [3]. Individuals with common mental disorders face many barriers to adequate care, including limited numbers of specialist mental health care providers, geographic barriers due to therapists living mostly in urban areas, and an inability to pay for or use insurance [3,4]. In recent years, there has been an increasing interest in the potential of low-cost, digitally delivered psychological treatments (eg, internet-based guided self-help [GSH] and smartphone apps) to provide treatment remotely and for low cost, such that they can be disseminated at a larger scale than “traditional” treatments, such as one-on-one face-to-face therapy [5]. These digital mental health interventions (DMHIs) may have the potential to expand access to effective psychological treatment [6] with a potentially revolutionary impact on public health, spurring a surge of attention across clinical research [7,8], the private sector [9,10], and government initiatives [11-13]. Robust evidence has now supported the efficacy of DMHIs in various formats, including internet-delivered cognitive behavioral therapy [14], both GSH and unguided self-help formats [14-16], and smartphone apps [16-18]. However, less research has focused on evaluating the potential reach of DMHIs [7,8].

Evidence suggests that public use of DMHIs may be lower than is required for broad public health impact. For example, DMHIs may be difficult for the public to access [19], underused by mental health care providers [20], or simpler and less appealing to the public than treatment developers might predict [21]. Therefore, it is vital that DMHI researchers afford sufficient attention to the actual implementation of DMHIs. Indeed, Ramos et al [7] cautioned that DMHI research risks mimicking the pitfalls of the “Decades of the Brain,” where large volumes of funding and time were spent on seemingly exciting innovations that made virtually no public health impact. Excellent work is underway to address this issue by identifying successful implementation strategies to maximize the reach of DMHIs, drawing from theoretical models and previous successes in implementation and dissemination science [7,22-24]. However, it may also be important to consider factors in individuals’ decisions to uptake and adopt DMHIs, drawing from literature on treatment-seeking behavior and mental health service use [25-28].

The scale of impact of DMHIs is dependent on the extent to which individuals with unmet treatment needs are broadly interested in and would use DMHIs. Although DMHIs are designed to circumvent structural barriers to treatment access (eg, cost, geographic availability of mental health care providers, need for transportation, and time commitment), their reach may still be limited by attitudinal barriers to treatment uptake. Attitudinal barriers are beliefs held by individuals that may affect their treatment-seeking behavior, such as the lack of perceived need for treatment, stigma, beliefs about the efficacy of psychotherapy, and the desire to handle a problem on one’s own. Importantly, attitudinal barriers are at least as common as structural barriers [25-29]. For example, across 24 countries in the World Health Organization (WHO) World Mental Health surveys, only 38% of the individuals with a 12-month mental disorder diagnosis reported a perceived need for treatment, making low perceived need the most commonly reported barrier to treatment use [25]. Therefore, the most common barriers to accessing traditional treatment may not be addressed by the innovations of DMHIs, which could greatly limit their potential public health impact.

Because most common attitudinal barriers (eg, stigma) are likely to impact any form of mental health treatment seeking, it may be likely that barriers commonly reported for traditional psychotherapy will generalize to DMHIs. However, this has rarely been studied directly. A small, promising body of literature currently provides some information on attitudinal barriers that are specific and often unique to DMHIs. However, much of this research often focuses on the acceptability of using DMHIs [20,30], especially pertaining to issues with technology specifically rather than revealing the impact that attitudinal barriers to general mental health treatment seeking might have on DMHI uptake. In a 2021 systematic review, Borghouts et al [31] identified common barriers to user engagement with DMHIs across a variety of study types and DMHI formats (including telehealth treatment), in which information about barriers was often indirectly reported as secondary components of studies, such as clinical trials, or extracted from user reviews. Many of the reviewed studies investigated adherence and retention of participants receiving a DMHI rather than potential uptake among the general population. Across the studies, concerns specific to the use of technology were among the most commonly reported barriers for both participants and mental health care providers, such as privacy, digital literacy, and the ease of use. This literature advances our understanding of users’ experience of using DMHIs, which is essential for the design and dissemination of DMHIs that are acceptable and engaging from both user and provider perspectives. However, among 208 relevant articles, the authors identified only 5 studies that had assessed the relationship between participants’ broader mental health–related beliefs and engagement in DMHIs. For example, in a trial of a smartphone app for relationship stress among adolescents, perceived treatment needs and belief in treatment effectiveness were each associated with greater likelihood of use [32].

These findings provided some support for the idea that attitudinal barriers to mental health treatment seeking may generalize to DMHIs. However, none of these studies were conducted with the primary purpose of investigating this relationship. They did not gather comprehensive data surveying commonly experienced access barriers, as is typically done in studies of perceived barriers to mental health care [25-28]. Therefore, this work did not explore the relative impacts of reduced structural barriers versus remaining attitudinal barriers when considering the potential reach of DMHIs. More research is needed to understand the extent to which attitudinal and structural barriers to help seeking may limit the reach of DMHIs to individuals with unmet treatment needs.

In addition to hopes that DMHIs may expand treatment access in the general population, there is also an often-stated assumption that DMHIs have the potential to reduce racial, ethnic, and socioeconomic disparities in access to mental health care [33-35]. These groups are often underserved due to multifaceted, often systematic sets of access barriers. The relationship between income and actual treatment use may be more complex than is often assumed. For example, middle-income families may have lower access than low-income families due to the latter’s ability to use government-funded mental health services [36]. Nonetheless, the ability to pay for treatment or use insurance is one of the most commonly reported perceived barriers to mental health treatment seeking [5,25,26], especially in the United States [27]. In addition to the ability to pay [4], lower-income individuals face associated structural barriers, such as geographic restrictions, for example, specialty mental health care providers are twice as likely to be available in the highest-income communities relative to the lowest-income communities [37]. Socioeconomic disadvantages disproportionately impact racial and ethnic minorities, who are overrepresented in lower-income communities. In 2012, the rate of access to mental health care for non-Hispanic White American individuals was 20%, whereas the rates for Black, Hispanic, and Asian American individuals were 10%, 9%, and 5%, respectively [38]. It is very reasonable to assume that making DMHIs available can reduce these disparities by reducing the structural barriers that often contribute to them. However, only a limited body of research has collected data to substantiate this assumption, particularly in regard to racial and ethnic minorities [33], suggesting that the reach of DMHIs risks replicating the same inequities seen in traditional treatments [8,33,39].

It is essential to consider the role of attitudinal barriers in the ability of DMHIs to ameliorate mental health care disparities, given their great influence on treatment use in the general population. First, the impact of DMHIs on mental health disparities may be limited by attitudinal barriers because attitudinal barriers can limit the extent to which services are used even if they are made accessible. Second, some literature suggests that certain attitudinal barriers may disproportionately affect underserved groups, raising the concern that targeting disparities via the dissemination of DMHIs may disregard potential contributions of attitudinal barriers as an important mechanism of these disparities. For example, some studies suggest that individuals with lower incomes and lower educational attainment have a lower perceived need for treatment than individuals with higher incomes [40]. Similar findings regarding racial and ethnic minorities are mixed, partially because attitudes vary both across different minority groups and intersectionally within them. For example, Asian American and Black American individuals report lower perceived need for treatment than non-Hispanic White American individuals, but Hispanic American individuals report approximately the same level of need as non-Hispanic White American individuals [41]. This picture is further complicated by intersectional differences. For example, despite those group-level differences, Hispanic men report greater need for treatment than non-Hispanic White men, and Black women report approximately the same level of need as non-Hispanic White men [41]. Cultural beliefs about mental health treatment seeking also vary by immigration status (immigrant vs US born) and country of origin, often, despite similar racial identities [40,41].

Existing literature exploring racial differences in perceived barriers to treatment use is severely limited [31]. Some data exist regarding racial differences in interest in DMHIs. For example, Hispanic adults may be more interested in DMHIs than non-Hispanic White adults [34,42-44]; however, researchers have suggested this may be attributable to greater rates of smartphone use rather than greater willingness to use treatment overall. Unfortunately, there is limited data to further clarify these patterns because individuals from both lower-income and racial and ethnic minoritized groups are often left out of DMHI trials, such that their preferences and attitudes may be overlooked in the design delivery and content of DMHIs [33,34,39,45]. Neglecting to understand actual interest in DMHIs among underserved and marginalized groups may limit the potential of DMHIs to attract members of these groups and serve their mental health needs [33,34,46]. Because attitudinal barriers are such a significant deterrent to treatment seeking and treatment access, paying special attention to how attitudinal barriers may translate to DMHIs in these groups will be key to ensuring DMHIs reach them as intended.

Existing literature has focused on developing innovative approaches to address structural barriers (ie, DMHIs), leaving a substantial gap in the effort to address attitudinal barriers. Therefore, it is unclear to what extent attitudinal barriers that limit access to traditional psychotherapy might generalize to also limit access to DMHIs, reducing the potential reach of DMHIs to populations with unmet treatment needs. In this study, we aimed to conduct an exploratory investigation of the relationship between attitudinal barriers to traditional psychotherapy access and potential use of DMHIs. We placed an emphasis on currently underserved populations such as racial and ethnic minorities and low-income groups in order to focus on those whom DMHIs are most intended to target. We captured both participants’ interest in GSH and their self-reported likelihood of using it. This allows us to parse the appeal of the intervention from its actual potential to engage participants. Research applying the theory of planned behavior [47] supports the use of behavioral intentions as a reliable predictor of mental health help seeking [48,49], and behavioral estimates may be an even stronger predictor than intentions [50].

Study procedures were approved by the Indiana University Human Subjects and Institutional Review Board (#14172). Participants were provided with an informed consent document with information about our laboratory, the study’s purposes and procedures, payment, risks and benefits, confidentiality and data security, and their right to withdraw consent at any time. The document was written at an approximately seventh-grade reading level to ensure accessibility across differing literacy or English fluency. Participants were required to attest that they were aged ≥18 years before proceeding. Participants were paid at a rate of approximately US $10 per hour (US $3.02 total for a median time expenditure of 17 minutes and 59 seconds; IQR 13 minutes 53 seconds to 24 minutes and 18 seconds). First, we chose Prolific over other platforms such as Amazon Mechanical Turk (MTurk) due to its stated commitment to ethical treatment of its online workers, including its “ethical reward” payment policy [51-53]. Second, we designed our study to address established ethical considerations in DMHI research. For example, we used liberal inclusion and exclusion criteria in order to capture a broad population of individuals with treatment needs (eg, no suicidality exclusion) [54,55]. Finally, online DMHI research must carefully consider clinical risk monitoring [56]. We provided participants with crisis resources for suicidality as well as information about how to find noncrisis treatment and direct access to a free online self-help booklet (refer to the subsequent sections) [57].

All analyses were performed in R software (version 4.3.2; R Foundation for Statistical Computing) [72]. First, we generated descriptive statistics for sociodemographic characteristics, psychological distress severity, and each of the primary outcomes (GSH interest and likely GSH use). We analyzed the relationship of individual characteristics (sociodemographic characteristics and psychological distress) with GSH interest via multivariate polychoric regression in the MASS package (version 7.3-60) [73] and the relationship of these predictors with likely GSH use via multivariate logistic regression. For all analyses involving income as a predictor, 1 participant was omitted due to a missing value for income. We additionally conducted univariate analyses for race and income, in order to isolate these key sociodemographic variables emphasized in the DMHI literature via 2 univariate polychoric regressions for GSH interest and 2 univariate logistic regressions for likely GSH use. These univariate analyses were informative in addition to the multivariate analyses of sociodemographic characteristics because we were substantively interested in the potential for DMHIs to reach low-income groups and marginalized racial-ethnic groups regardless of whether other demographic characteristics (eg, education) account for the difference.

Next, we generated descriptives for past-year psychotherapy use and analyzed its relationship with individual characteristics via logistic regression. We analyzed past-year psychotherapy use as a predictor of each GSH interest and likely GSH use via polychoric regression and logistic regression, respectively, controlling for psychological distress in both analyses. For all polychoric regressions, P values are not reported because the analysis does not directly generate P values, and simulated P values may not be reliable; our interpretations of statistical significance were made from CIs. Next, for participants who denied past-year psychotherapy use, we generated frequency statistics regarding endorsement of (1) perceived need for psychotherapy, (2) all contributing barriers to psychotherapy use, (3) primary barrier to psychotherapy use, and (4) primary barrier type. We only compared the frequency of lacking perceived need versus other primary barriers, rather than versus the frequency of all contributing barriers, due to the structure of our survey. Because participants who denied perceived need were not given the opportunity to select additional barriers, the level of detail collected about this subgroup’s access barriers was lower than for the subgroup that selected multiple contributing barriers.

Finally, we grouped the primary barriers into 3 categories: attitudinal, structural, and “other” (refer to the Primary Access Barriers and Barrier Type section). We analyzed sociodemographic characteristics and psychological distress as predictors of primary barrier type via multinomial regression in the nnet package (version 7.3-19) [73] to accommodate the 3-category outcome. Next, we analyzed primary barrier type as a predictor of each GSH interest and likely GSH use via polychoric regression and logistic regression, respectively, controlling for psychological distress in both analyses. Finally, we analyzed the relationship between each contributing barrier and each outcome via a series of univariate linear regressions.

Due to the high number of statistical tests, we applied Benjamini-Hochberg adjustment to P values across all analyses.

We report on how we determined our sample, all data exclusions, all manipulations, and all measures in the study. All data, analysis code, and research materials are available on the Open Science Framework website online [74]. This study’s design and its analysis were not preregistered. No other papers currently use these data.

Most of the 971 participants identified as non-Hispanic White (n=665, 68.5%) and heterosexual (n=688, 70.9%), with an approximately even gender split (women: n=538, 55.4%) and a median age of 32 (IQR 25-41) years. The median K6 score was 11 (IQR 8-15) of 0 to 24 possible points. Full sample characteristics are reported in Table 1.

Most of the 971 participants (n=782, 80.5%) reported that they were at least “somewhat interested” in GSH. Nearly half (n=458, 47.2%) of the participants were at least “moderately interested,” and 17.1% (n=166) were “very interested.” However, a slightly greater proportion (n=189, 19.5%) of participants was “not at all interested.” We found that 38.6% (n=375) of the participants reported that they believed they were likely to complete at least one GSH session if it were offered to them.

None of the sociodemographic characteristics, including psychological distress, were statistically significant predictors of self-reported interest in GSH. For self-reported likelihood of GSH use, only age met statistical significance at P<.05 after Benjamini-Hochberg adjustment (odds ratio [OR] 1.02, 95% CI 1-1.03; P=.045). Table 2 gives full results for both outcomes.

In this study, we found reasons to question the extent to which DMHIs may reach individuals with unmet needs by circumventing structural barriers alone. In a sample of individuals with psychological distress, those who did not use psychotherapy in the past year were less interested in GSH and less likely to predict that they would use GSH relative to those who had used psychotherapy. This difference might be explained by the high prevalence of attitudinal (vs structural) barriers observed among those without past-year psychotherapy use, especially the lack of perceived treatment need. Attitudinal barriers were the most commonly reported barriers to past-year psychotherapy use and were associated with lower interest in GSH and lower self-reports of likely GSH use. Taken together, these results suggest the potentially 3 substantial role of these barriers in limiting DMHI uptake for individuals with unmet treatment needs. Interestingly, we did not find support for differences in any of the major study variables by racial-ethnic identification, and results for differences by income were mixed.

A limitation of this study is that our analyses are restricted to participants’ opinions drawn from a brief description of a single DMHI. Our results about an internet-delivered GSH bibliotherapy might not generalize to other DMHI formats if barriers to DMHI use vary across digital delivery formats [31]. For example, privacy could be a greater concern when using a smartphone app, and attitudes might differ when human guidance is not included [25,31].

Our sample also presents limits to generalizability. First, this study focused exclusively on US residents, which limits generalizability to other countries, for example, due to different health care systems [27]. In addition, because all participants were recruited online, it is possible that unique features of this population might alter our sample’s interest in DMHIs relative to the general population (eg, they may have higher digital literacy [75,76]). Future work extending this research to varying samples will be required to establish generalizability.

Our measurement methods may have introduced error. Self-reported likelihood of GSH use may overestimate actual likelihood of GSH use, and future research should measure observable behavior instead. Our measure of interest in GSH may also have lost sensitivity in its format as a 4-point scale [77]; however, Leung [78] presented a discussion of psychometric performance relative to 5-, 7-, and 11-point scales. Finally, our survey design also imposes some limitations on the interpretability of these data. Participants without a perceived need for psychotherapy were not queried about any other barriers, obscuring the possible role of multiple barriers for this group. We also queried participants about their experience of multiple specific barriers via a checklist-format question, which may distort results by encouraging participants to overendorse answer choices [48]. Therefore, this work might be stronger if measurement allowed participants to rate the relevance of each barrier dimensionally to capture greater complexity.

This study’s major novel contribution is its direct investigation of the relationship between perceived barriers to accessing “traditional” psychotherapy and potential DMHI use. Previous literature has extensively reported barriers to general mental health treatment seeking [25-28,79,80] and DMHI interest and adherence [81] separately but often neglected to measure them together. Barriers to DMHI acceptability and engagement have often been studied inductively, often among participants in clinical trials or institutional settings already using DMHIs [30,31]. By contrast, our approach offered a unique contribution to the literature by reflecting potential barriers to the uptake of DMHIs. First, we captured a wide range of both attitudinal and structural barriers, rather than focusing on only those commonly addressed in the DMHI literature (eg, cost and geography). This allowed us to capture common barriers to help seeking in general, derived from robust existing literature [25-28] rather than technology-specific concerns. In addition, we sampled from a general population of individuals with potential treatment needs rather than trial participants who have already “overcome” barriers to uptake.

Importantly, we avoided the common negligence to investigate racial and ethnic differences in DMHI research [33,39,45], placing special emphasis on both racial and ethnic minorities and low-income individuals. In addition, we designed our sample to focus on individuals with unmet treatment needs by (1) requiring at least mild psychological distress and (2) conducting primary analyses on the subsample that had not accessed psychotherapy.

Finally, another unique strength of this study is that we parsed participants’ interest in GSH from their self-reported likelihood of actually using GSH. The predictors of these outcomes were similar across analyses, but their differing base rates contributed unique information. For example, among individuals who did not access psychotherapy in the past year, the percentage of the participants who predicted that they would use GSH (205/640, 32%) was approximately half the percentage of the participants who were at least somewhat interested (497/640, 77.7%). According to the theory of planned behavior, participants’ estimates of their likely treatment-seeking behavior should be a stronger predictor of actual treatment use than attitudes [47,49,50]. Understanding factors that lead to differences in attitudes versus perceived likelihood of using DMHIs may be informative for dissemination efforts.

Our results suggest that the attitudinal barriers that limit traditional psychotherapy use may also limit DMHI use. Individuals who are not in treatment due to the lack of perceived need for any treatment are likely to forgo use of DMHIs. Our results were also consistent with previous literature by suggesting that attitudinal barriers to psychotherapy use may be more common than the structural barriers that DMHIs may target [25,27]. Failing to address attitudinal barriers may substantially limit the potential public health impact of DMHIs if this obstacle is left unaddressed.

In addition, our results suggest that individuals who already have access to psychotherapy may be more likely to use DMHIs than those with unmet needs. This is concerning because a major aim of DMHIs is to reach individuals who cannot access traditional treatments. However, this finding does not contradict all models of DMHI dissemination. For example, DMHIs may improve access by expanding the spectrum of care intensities available [7]. If individuals with lower-intensity needs rely on lower-intensity treatments, they may free up “higher-intensity” resources (eg, specialized clinicians) for others. Whereas institutional systems of stepped care enforce this model (eg, Improving Access to Psychological Therapies program of the United Kingdom [13]), the public’s use of stand-alone DMHIs may not always follow this pattern. The hope that individuals using publicly available DMHIs (eg, commercially available apps [9]) might then forgo traditional treatment is only a hypothesis. Our results suggest that the contrary is possible, such that, individuals with positive treatment attitudes may use both DMHIs and traditional treatment. This would risk widening, rather than reducing, the treatment gap by benefiting those with existing access rather than those without it. However, stand-alone DMHIs may have particular public health utility when they offer services that are difficult to access in the community (eg, specialty treatments such as cognitive behavior psychotherapy for insomnia [82]).

We did not find evidence supporting racial and ethnic differences in our study’s indicators of potential DMHI use, supporting the broad appeal of DMHIs. This is consistent with previous literature, which has generally found little support for differences in interest and engagement in DMHIs across racial and ethnic groups, although research is still limited [33]. We also did not find racial-ethnic differences in the relative rate of attitudinal versus structural barriers to treatment access. This contrasts with previous findings that attitudinal barriers to general help seeking may vary by race [41,83], especially stigma [84]. However, results are inconsistent and suggest that these patterns may be nuanced across individual minority groups and by treatment type (eg, medications vs psychological treatment [34]). In addition, we found some evidence for differences in attitudinal barriers and GSH interest by income, but the results were mixed, such that it is difficult to interpret a pattern. Future research should clarify the potentially complex relationship between income, access barriers, and help seeking [36].

Finally, our findings have implications for DMHI development research. DMHI trials may primarily attract participants who are already interested in psychological treatment but unable to access traditional psychotherapy due to structural barriers. Therefore, these samples might underrepresent individuals facing attitudinal barriers to treatment. This biased sample would not reflect the total target population of individuals with unmet needs. Therefore, the estimates of DMHI use could be inflated, and design elements intended to increase DMHI use might not be generalizable to the total population. This would be particularly concerning because the rates of DMHI use are already low, and much work has been devoted to testing the effectiveness of design alterations intended to increase engagement [81].

Future work should first substantiate this exploratory research by investigating these questions in additional samples [60]. Recruiting dedicated samples of racial and ethnic minorities may also be key to developing a clearer picture of this phenomenon (eg, capturing intersectional effects). The lack of representation of racial-ethnic minorities is a well-documented problem in DMHI research [8,33,34,39,45], so this should be prioritized in future DMHI work. In addition, evidence suggests that attitudinal barriers may vary across countries [27], and treatment-seeking pathways vary across countries according to their health care systems [85]. Therefore, research with international samples and replication across research groups in several countries is essential for broad applicability and public health impact on a global scale. Finally, community-based sampling may improve upon potential generalizability and data integrity issues introduced by online recruitment [62]. Community-based sampling might also facilitate improved measurement approaches, such as observed uptake of clinical services, rather than relying on self-report.

In addition, future research should seek to better understand the lack of perceived need for treatment. Our results and previous literature [26] suggest that a lack of perceived need may be the most common reason individuals do not access treatment. Therefore, using measurement approaches that are able to parse the components of perceived need (and lack thereof) may be particularly informative. For example, low mental health literacy [86] may contribute to a lack of perceived need when an individual does not have sufficient understanding of mental health concerns to recognize their symptoms. In addition, reporting a lack of perceived need for treatment may reflect a cultural preference for other types of help, such as informal community support [87]. Developing a more nuanced understanding of perceived need may be the first step toward understanding how lacking perceived need for treatment might generalize to DMHIs as well as how perceived need might differ across treatment modalities.

Future work should also endeavor to synthesize barriers to general treatment seeking with findings about barriers to DMHIs specifically. Indeed, both barriers to help seeking in general [25,29] and barriers specific to DMHIs [30,31] cumulatively serve as bottlenecks in DMHIs’ ability to reach individuals with unmet needs. Therefore, attempts to ameliorate these issues and expand the reach of DMHIs will need to address both types of barriers. In addition, individuals may sometimes seek treatment despite attitudinal barriers [88]. Therefore, in addition to interventions aimed at modifying attitudes, future research might also seek to promote factors that circumvent perceived barriers. For example, research informed by the theory of planned behavior [47] suggests that subjective social norms and perceived behavioral control predict treatment-seeking behavior [48,49].

Finally, future work might pay greater attention to existing approaches to increasing mental health treatment seeking. Help-seeking interventions [89,90] are often designed to target barriers such as stigma [79] and mental health literacy [91]. In addition to encouraging general treatment seeking, interventions could be tailored to encourage the use of DMHIs. For example, models for “direct-to-consumer marketing” of evidence-based psychological interventions have been proposed as an adjunct to treatment innovation and implementation efforts [92]. Tailoring such interventions to build public awareness of the diverse “portfolio” of treatment options [7] available for their direct access [93] could potentially increase the reach of DMHIs. Raising awareness of DMHIs’ existence and efficacy [89] could empower treatment seekers to choose the options that are most fitting for their attitudes and preferences [86].

Overall, this work suggests the importance of questioning assumptions about how potential solutions to the mental health treatment gap will reach those who most need them. DMHIs clearly have an advantage over traditional psychotherapy in their scalability because their low resource requirements and low intensity of use can circumvent structural barriers. However, understanding the wide range of obstacles to DMHIs’ dissemination and uptake may be essential to maximizing their impact. In the quest to expand treatment access, the first hurdle that DMHIs face is the willingness to seek mental health treatment of any kind. Our work suggests that addressing individuals’ attitudinal barriers may be an important step to ensure that DMHIs maximally achieve their promise of expanding access to treatment and reducing the public health burden of psychopathology. Devoting resources to help-seeking interventions [89] may be key to addressing the treatment gap [6] rather than focusing on developing solutions to structural barriers alone (ie, scalable treatments). Importantly, adequate representation of the underserved groups most in need of accessible interventions is vital throughout this research [8,33].