For the Good of the Community: Considering the Impact of Evidence-Based Treatment Adaptation on Tribal Communities

Abstract

Ongoing discussions among tribal communities, professionals, and mental health organizations have centered on the question of whether evidence-based interventions require adaptations to improve mental health outcomes among American Indian communities. Continued efforts to address these questions have resulted in the development of novel, culturally-grounded interventions, adapted interventions, use of original standard protocols, and/or limited use of evidence-based treatments. Consequently, mental health services in tribal communities may be highly variable from one community to another and the effectiveness of services relatively unknown. The current paper explored the state of the literature on adaptations and their utility among American Indian communities and the broader ethnic minority community. Considerations for the various impacts on tribal communities are presented and recommendations for researchers and practitioners are also discussed.

Dissemination and implementation of evidence-based treatments (EBTs) for mental health disorders among American Indian (AI) communities is critical to reducing the mental health burden among these populations. Specifically, researchers and practitioners alike have raised alarm concerning the validity of EBTs developed in the general community for minority populations and the “need” for culturally-tailored adaptations and culturally competent approaches to treatment. Lack of representation of ethnic minority participants in clinical trials and the potential clash between cultural traditions and principles of EBTs are highlighted as particular limitations in this field (Bernal & Scharron-Del-Rio, 2001; Gone & Alcántrara (2007); Griner & Smith, 2006; Hall, 2001). As a result, there has been a movement in ethnic and cultural psychology over the last few decades towards the adaptation of current EBT protocols and the creation of novel, culturally-grounded interventions.

This movement influenced examinations on the effectiveness of EBTs among minority populations and later, examinations of treatment effects (see Barrera, Castro, Strycker, & Toobert, 2013; Domenech Rodriguez & Wieling, 2004; Huey, Tilley, Jones, & Smith, 2014). As a result of this work, frameworks for adaptation have been developed that identify steps to systematically adapt protocols (e.g., Bernal, Bonilla & Bellido, 1995; Barrera & Castro, 2006, Huey et al., 2014; Kumpfer Pinyuchon, Melo, & Whiteside, 2008; McKleroy et al., 2006; Wingood & DiClemente, 2008) and systematically examine the need for adaptation within a particular group (e.g., Barrera & Castro 2006; Davidson et al., 2013; Kumpfer et al., 2008; Lau, 2006). Importantly, these examinations have yielded recommendations for researchers to address the need for minority representation and inclusive reporting including actively recruiting ethnic minorities in effectiveness trials, ensuring representation in study samples, and reporting response to treatment by ethnicity (Lau, 2006). Although a comprehensive review of cultural adaptation is beyond the scope of the current paper, such work has been conducted (see: Barrera, Berkel, & Castro, 2017; Chu & Leino 2017; Hall, Ibaraki, Huang, Marti & Stice, 2017; Huey et al., 2014; Lau, 2006; Pina, Polo & Huey,2019; Smith & Trimble, 2018).

Among the AI treatment literature, discussion of cultural misfit and difficulties with engagement and attrition among AI patients has generated concerns regarding the validity of EBTs. In response to the need for understanding the effectiveness of EBTs among AIs, there has been a growing body of literature addressing the cultural adaptation of treatment. Interestingly, Gone and Alcántrara (2007) reviewed modified interventions for AIs and Alaska Natives (ANs) and found that only 2 of the 56 articles generated were systematically assessed using rigorous scientific standards (i.e., controlled trials, larger sample size, etc.). This finding highlights a gap in the field concerning rigorous evaluation of adapted EBTs among AI communities. Furthermore, the majority of efficacy studies either have too small of a proportion of AI representation for appropriate analysis or no AIs whatsoever, with the latter occurring more frequently (e.g., Huey & Polo, 2008; Huey et al., 2014; U.S. Department of Health and Human Services, 2001). Consequently, there continues to be a significant dearth of evidence concerning the effectiveness of EBTs, or lack thereof, among AI communities to support the claim that adaptations are warranted. As a result, the current commentary: 1) examines the current state of the research and academic comment on cultural adaptations within the AI literature; 2) discusses the impact of adapting EBTs on tribal communities and mental health care delivery; and 3) discusses recommendations for future research and considerations for evaluating and adapting EBTs among AI populations. Notably, this literature is scarce in some areas; thus, the broader ethnic minority literature (e.g., adaptations with African American, Latinx/Hispanic, and Asian American communities) will be reviewed insofar as it informs gaps in the AI-specific literature and illustrates the need for AI representation in efficacy literature broadly.

Brief Review of Cultural Adaptation for EBTs

Cultural adaptations of EBTs aim to provide a cultural context for the intervention to be disseminated and implemented in a way that is more closely reflecting the particular community and patient (Bernal, Jimenez-Chafey, & Domenech Rodriguez, 2009; Cabassa & Baumann, 2013; Cardemil, 2010; Castro, Barrera, & Martinez, 2004). It allows for contextualizing interventions to ensure fit with the communities that are served and may encapsulate surface level, as well as deep structural, changes to the treatment protocol (Resnicow, Baranowski, Ahluwalia & Braithwaite, 1999). Many EBT protocols include flexibility in tailoring it to individual patients and communities, while maintaining fidelity to the intervention, thus facilitating cultural adaptation. However, some researchers (e.g., Castro, Barrera & Holleran Steiker, 2010) argue the principle of cultural relevance suggests that EBTs, even if delivered with fidelity, would demonstrate minimal effectiveness with a subcultural group. Huey and colleagues (2014) noted that even when adapting EBTs and seeking to achieve cultural relevance or competency there continues to be variance in model approaches which further complicates the assessment and operationalizing of cultural adaptations.

A meta-analysis conducted by Griner and Smith (2006) found moderate effect sizes (d = 0.40) for experimental and quasi-experimental design studies suggesting benefit and support of culturally-adapted interventions. Soto, Smith, Griner, Domenech Rodriguez & Bernal (2018) conducted a meta-analysis review examining the effect size of culturally-adapted interventions (d = 0.50) with similar magnitude on treatment effects. However, Benish, Imel and Wampold (2011) caution that conclusions regarding efficacy relative to standard protocol EBTs are limited due to meta-analytic findings (e.g., Griner & Smith, 2006) including studies comparing cultural adaptations to no treatment. Huey and Polo (2008) further delineated studies by examining effect sizes of culturally-adapted interventions compared to no treatment (d = 0.58) and treatment as usual (d = 0.22) demonstrating that comparisons to treatment as usual reduces effect sizes considerably. However, the authors note that interpretations of their results are limited due to low power and variability in quality and rigor of available studies included in the review (Huey & Polo, 2008). Benish et al. (2011) extended this literature by comparing culturally-adapted interventions to “bona fide” (i.e., direct-comparison of EBT) treatments supporting the benefit of cultural adaptation on treatment outcomes over non-adapted psychotherapy (d = 0.32). Notably, the authors note that the effect of cultural adaptations could not be disentangled from the addition of various other components that differed from the original protocol that were not necessarily cultural in nature (Benish et al., 2011). Although these provide burgeoning evidence for cultural adaptations, these meta-analytic findings (Benish et al., 2011; Huey & Polo, 2008), did not include any AI participants or AI culturally-adapted interventions and suggest additional research is needed examining possible enhanced treatment effects of adapted protocols among AI communities.

Considerations for Adapting Treatments

Extant literature provides a systematic approach to deciding when and how cultural adaptations of EBTs should be carried out. Research suggests that cultural adaptations are most effective when community need is thoroughly assessed and when treatment “add-ons” supplement “core” intervention components (Barrera et al., 2017; Lau, 2006). Add-ons to the existing EBT are based on available data and/or theoretical rationale rather than relying on clinical intuition or “in-the-moment” adaptation, though others have suggested it may occur in the context of individual tailoring of protocols in “real-life” clinical settings (Barrera et al., 2017). Adaptation is particularly warranted when the following considerations are met: 1) differences are demonstrated in treatment outcomes among ethnic minority populations; 2) treatment fidelity is maintained and the optimal dose is applied; and 3) differential engagement among an ethnic group exists. This is important considering some data suggest psychotherapy is generally effective for ethnic minority youth and adults and that there is a lack of sufficient evidence supporting differential treatment outcomes among ethnic minorities (Huey et al., 2014). Specifically, Huey and colleagues (2014) note that up to 70% of the randomized trials and meta-analytic studies did not demonstrate moderated effects by ethnicity. Studies in the Huey and colleagues (2014) review included samples where the majority of participants were ethnically diverse and included analyses of ethnicity moderating treatment outcomes and analyzed treatment effects by ethnicity. With limitations noted, the overall data suggested that standard EBTs reduced symptoms and improved engagement among minority participants.

Although outcome data may be mixed or absent, support for cultural adaptations also includes evidence suggesting that ethnic minorities are harder to recruit and less likely to engage and remain in services than European Americans (Barrera et al., 2017; Cabassa & Baumann, 2013). A recent review (Barrera et al., 2017) indicated lower levels of treatment engagement (primarily enrollment) among various ethnicities when compared to each other and European Americans. Conversely, the review noted very few adaptation protocols demonstrated enhanced effects on engagement (attendance), retention, and treatment satisfaction when compared to the original efficacy trials This review highlights the critical need for further research to delineate elements of cultural adaptations, which may improve effectiveness and evaluate quality assurance (Huey & Polo, 2014). Relatedly, Miranda et al. (2005) propose a prerequisite determination of required “dosing” of adaptation needed to enhance treatment. For example, examinating whether surface-level (e.g., therapist ethnic match, pamphlets written in native language) or deep structural changes (e.g., incorporation of cultural and spiritual practices) are warranted.

Adaptations Among AI Communities

AI sociopolitical history, differing worldviews, and cultural practices are often cited as factors indicating the need for adaptations to ensure the appropriateness and acceptability of treatment and measurement (e.g., Greenfield, et al., 2013; Walls, Whitesell, Barlow, & Sarche, 2017). Gone and Alcántrara (2007) identified two controlled trial studies of preventative interventions which included a modification of an established EBT (Manson & Brenneman, 1995) and the creation of a culturally-grounded program (American Indian Life Skills Curriculum; LaFromboise & Howard-Pitney, 1995) that demonstrated positive treatment outcomes. Recent evidence has shown favorable outcomes in the areas of suicide prevention, substance use disorder treatment, and behavior management/parent training among tribal communities (Greenfield et al., 2013).

While AI treatment literature is expanding on adaptations, Gone and Alcántrara (2007) noted the paucity of AI research to support the claim that adaptations are “more culturally relevant or sensitive- and therefore more effective” for AI patients compared to standard EBTs (p. 359). Thus, recommendations for adaptations across the board may be premature in instances where there is a lack of data clearly demonstrating EBTs do not work with AIs. Consequently, there is an ongoing need for systematic assessment of EBTs among AI communities and rigorous examination of adaptations (i.e., quasi-experimental and randomized controlled trials) in comparison to the original EBT and control groups. These recommendations for future research mirror those that are put forth for minority research broadly. Additional guidance includes: increased sample sizes for adequate statistical power, detailed descriptions of cultural modifications, examining whether ethnicity (or acculturation for within-group comparisons) moderates treatment effects, and examining the appropriateness of outcome measures with minority groups (Huey & Polo, 2008; Huey et al., 2014). Specifically, additional work on treatment acceptability of EBTs would be informative and help to address concerns for cultural incongruence and external validity of EBTs (Lau, 2006). Also, given the heterogeneity within AI communities, variability in the levels of cultural identity would add valuable information on possible differences of effects between those who more strongly identify with being AI and are engaged in the community versus those who are less so (Castro et al., 2010; Huey et al., 2014).

Impact on Tribal Communities and Practice

From the experience of those working and living in the AI community, the notion that adaptations are needed is shared between professional (doctoral and masters-level) and layperson alike. However, the argument for use of adaptations appears to be more often presented in terms of absolutes, in the sense that adaptations are absolutely needed and treatment will not work without them, rather than the actual state of the literature. This perspective occurs in professional conversations, national conferences, and state meetings focused on AI mental health both in our experience and as cited in Gone and Alcántrara (2007). While not directly resolving the question of whether or not to develop a treatment adaptation, these conversations have significant implications on tribal research and practice both at the micro (e.g., individual behavioral health provider) and macro levels (e.g., government funding, tribal administration). This is especially critical to the discussion on implications since tribal communities, and minorities in general, are less likely to receive evidence-based mental healthcare (Wang, Berglund, & Kessler, 2000). Furthermore, adapting evidence-based treatment comprises a significant burden on the community (i.e., time and labor-intensive requirements) and may delay implementation and treatment.

An additional complicating factor for cultural adaptation is the heterogeneity of AI communities both within and across tribes, and generalizability of one adaptation across AI populations is likely to be limited. Researchers have indicated continual pursuit of newly adapted versions of EBTs may prove inefficient particularly in the absence of strong evidence to support a need for adapted protocols (Lau, 2006). In combination, these considerations lead to the following questions: How are communities faring while researchers work to develop culturally adapted EBTs when data has not indicated the need to adapt EBTs? In the absence of data clearly indicating need for adaptation, we wonder whether it is ethical to conduct time and labor-intensive adaptations with tribal communities?

Risk and Resilience Processes in AI Communities

Barrera et al. (2017) and Lau (2006) recommend adapting interventions when: there exists a difference in risk and resiliency mechanisms, differences in treatment efficacy (e.g., quantitative, qualitative, and behavioral observation methods), and/or limited social validity (e.g., low treatment acceptability, poor attitudes towards treatment, differences in attrition compared to norming population) has been demonstrated. Subsequently, effective adaptation would include modifications of malleable components rather than core components of the treatment given evidence of need. For example, Lau’s (2006) conservative framework approach advocates for systematically identifying and targeting specific problems using data to direct adaptation of protocols where needed with care to maintain fidelity to core principles of the EBT identified for adaption. Such an approach aims to ensure that adaptation is indicated as a need and rigorously evaluated for efficacy.

Among AI communities, Walters and Simoni (2002) developed the Indigenist Stress-Coping Model that specifies the particular links between AI social experience and health outcomes including both mental health and substance abuse. This model describes how unique aspects of AI historical trauma (e.g., colonization, forced removal, boarding school era) and discrimination affect health and how culture may serve as a buffer against the negative impacts of trauma or impact one’s vulnerability. Important considerations for subcultural AI groups (rural vs urban vs reservation, acculturation, socioeconomic status) are recommended to further delineate risk and resilience processes (Walters & Simoni, 2002). Several studies have utilized this model to help conceptualize processes in substance use (e.g., Walters, Simoni, Evans-Campbell, 2002), HIV/AIDs (Walters, Beltran, Evans-Campbell & Simoni, 2011), and diabetes management (Coser, Sittner, Walls & Handeland, 2018), among AI communities. Whitbeck and colleagues (2004) likewise demonstrated the distinctive role of historical loss (e.g., loss of culture, language, traditional spirituality) mediating the link between perceived discrimination and increased alcohol misuse.

Walters and Simoni (2002) also discussed previous literature supporting the link between enculturation and improved psychological distress. As a result, they recommended strategies for utilizing cultural practices (e.g., sweat lodge) to provide an avenue for possibly enhancing treatment. Adapting therapeutic content to include a common cultural practice is common for AI-adapted EBTs. However, given that more enculturated patients are by definition more likely to be engaged in their communities and ceremonies, the question is raised: Who do adaptations serve? Quite possibly these types of adaptations are more effective for those who identify as more acculturated or bicultural and thereby [re]connecting these patients with cultural practices in an attempt to harness these potentially buffering effects. This seems to be an additional layer of examination to investigate, especially given the vast within-group differences among AI communities. Continued research and data supporting distinctive risk and resilience processes, like that of Walters and Simoni (2002) and Whitbeck and colleagues (2004), for AI communities is critical to provide further evidence supporting the need for specific adaptations for a clearly defined problem.

Implications: Potential Burdens and Suggested Paths Forward

Thus far, we have reviewed literature that informs conditions under which cultural adaptations of EBTs may be appropriate for AIs and/or diverse populations in general. Taken together, the need for widespread cultural adaptation of EBTs remains an empirical question that requires continued examination of current EBTs, their outcome data, and a high degree of evaluation among communities to determine the need for adaptation. In recognizing tribal sovereignty- with respect to mental health research – researchers are encouraged to present all information, including outcome data and limitations, on EBTs to ensure an informed decision can be made by the tribe in determining their involvement. Further discussion of potential research burdens on AI communities is discussed below.

Treatment as Usual as a Comparison for Culturally Adapted Protocols

Examining adapted or novel treatments compared to tribes’ treatment as usual are often of interest for researchers and critical for evaluation of these novel protocols. As with broader treatment evaluation research (e.g., RCT) it is crucial to consider the comparison groups to draw accurate conclusions regarding outcomes. Often newly developed treatment protocols involve high degrees of training and fidelity assessment, which may be lacking in a treatment as usual comparison (Spirito et al., 2002). In fact, it has been noted the TAUs can be highly variable and often poorly characterized in RCT reporting; thus, conclusions drawn are often tenuous at best (Spirito et al., 2002) Thus, clinically desirable effects found cannot be attributed to the cultural adaptation of EBTs in absence of rigorous well-defined comparators. This is problematic because implementation of new interventions represents a significant expense for the tribal system, both monetarily and in reduced time in provision of clinical services. On a related note, if providers are not inclined toward fidelity in ongoing treatment (i.e., TAU), the adapted treatment may not represent an actual improvement in the absence of ongoing fidelity evaluation. Such ongoing evaluation represents further resource burden of the newly adapted treatment. The potential resource burden on tribes highlights the critical need for examination of the effectiveness of established EBTs in tribal communities, and the need for and potentially detrimental impact of ongoing rigorous evaluation of newly developed protocols (see: Gone & Alcantra, 2007). The potential for resource burden on tribes is discussed herein.

Research Priorities and Community Engagement

A common pattern of the research process within tribal communities has been discerned in nearly a decade of tribal IRB service: A research team (AI or non-AI) of some renown has a pre-conceived research idea or methodology and then contacts one or more tribes to discuss processes to garner community feedback that will be incorporated to develop and submit a competitive grant application in response to a funding opportunity. If the team is funded, they engage in an initial phase of community-based participatory research (CBPR), or partial implementation of CBPR, via key informant interviews with a small number of homogenous tribal members. Based on the input from the “community” the research team makes adjustments to their pre-determined design, implements an intervention in a second phase, and publishes the results of the study. Following the conclusion of funding the research team may or may not maintain a relationship from which future collaborations manifest. Although lauded at professional meetings and conferences by researchers and academics, tribes can suffer from perils of the unintended consequences of this model. In such scenarios attempts at community engagement may prematurely assume the need and/or desire of the community for cultural adaptation of EBTs and/or the inclusion of cultural components without requisite justification from extant data or tribal input.

We would be remiss if we did not mention that the scenario above is not always unwelcome; however, funding agencies and independent researchers alike should consider the long-term effects of this approach on tribes and sustainability of newly developed mental health intervention and prevention programs. In under-resourced and under-served populations, tribal health systems are mostly service-based and research is either a luxury or nonexistent due to limited resources and outsized demand. External research teams impacting health services often request from the tribe an investment in the form of a portion of full-time employment (FTE) of one or more staff members to assist with data collection, de-identification, and/or navigating tribal policies and procedures. In service-based tribal health systems this reduces overall productivity and service delivery of the departments, behavioral health in the case of EBTs, from which staff time is drawn. Whereas researchers benefit from publications and future grant funding, the tribe may suffer lost productivity from bought-out FTE amounts and be under-resourced to explore resultant data or implement the intervention even if the research results support a new or adapted protocol. Further, once the research team no longer has salary support from the previously funded grant, demands of new grants make it unlikely the research team will be able to respond to or fulfill requests to analyze the data produced through the grant. Thus, the tribe is then left to await contact from the next researcher or university with a desire to work with the tribe to be awarded funding for the newest federal priority. In other words, a potential unintended consequence of well-intended funding to increase research with tribal partners is the potential fostering of dependence on outside entities for research and evaluation.

Data Ownership, Capacity to Self-Determine

At the end of a funded research project, the tribe frequently retains full or partial ownership of the data, but in many cases they do not have the capacity or staff for database management, secondary data analysis to answer additional questions, or to make actionable the data their tribal citizens provided. Few tribes are resourced with the capacity to allocate the time of skilled individuals to focus on systematically improving behavioral health processes and outcomes. Quality of care is always a point of interest and dovetails seamlessly with questions related to adapting EBTs, but in limited-resource systems, patient outcomes are a lower priority than access. Specifically, in behavioral health where therapeutic outcomes should be of primary importance, the demand for services far outweighs supply of services despite significant time and effort being put into addressing barriers to access.

A potential avenue for increasing tribal self-determination in the research process beyond simple cultural adaptations of prevention and intervention protocols would be to include models of research wherein researchers assist tribes to build internal capacity to capitalize on extant data to answer questions generated within tribal agencies themselves. One area of rich data available to tribes comes from the role that Indian Health Service plays in tribal healthcare. There is a tremendous amount of behavioral health care data and funding agencies could call for researchers to submit proposals aimed at enhancing the capacity of tribes to develop their own empirical questions and processes for determining areas of investigation and need for adapting protocols. Alternatively and far more sustainably, investigators should work with tribal partners to develop protocols to apply for applicant initiated funding mechanisms.

It is important to point out that tribes have been engaging in the scientific method for hundreds of years (for example, Pawnee’s genetic selection of corn, A. Echohawk, personal communication, July 24^th, 2018). Combining the history of scientific endeavor with the wealth of healthcare data available, most tribes have the data they need to study the impact of their current healthcare practices on their citizens and/or service populations. What is desperately needed are the resources and skillsets devoted to planning and implementing methodologies to collect actionable data to be analyzed to answer tribes’ questions. Access to the cutting edge scientific literature, up-to-date analysis software and the training to use it, research and evaluation trained staff are but a few examples. Tribes being able to know ahead of time the impact, reach, and effectiveness of their current behavioral therapies and services will not only allow them to make changes at their discretion but would also increase their agency in working with the researchers by whom they are approached. Additionally, it could provide comparison data for treatment as usual conditions that would be of exceptional scientific value when they are approached by potential research collaborators.

Regenerative Research

Regenerative agriculture is agriculture that is focused on strengthening the health and vitality of farm soil. Analogous to this practice, we recommend the adoption of a paradigm of regenerative research. In the current context, this would include a detailed plan to leave tribal communities, departments, and programs better off than they were and as determined by the tribe. Examples of this effort can be seen in the focus of the service-based Native Connections Approach on sustainability of grant activities within communities (SAMHSA, 2019). In adopting regenerative research, results would need to demonstrate that the tribe has improved (as defined and assessed by the tribe) from the research endeavor. This could be through training, education, services, or agreements for the university or research teams to continue a partnership for some length of time following funding to provide these things at the behest of the tribe. Thus, optimal research on cultural adaptation of EBTs goes beyond demonstrating treatment benefits over and above established EBTs but also includes a plan for mitigating burden to tribal services during the research process, and plans for sustainability of research benefits.

Conclusion

Cultural adaptation of EBTs for mental health problems has been and continues to be a burgeoning area of interest for addressing disparities in psychological and psychiatric disorders (e.g., Bernal & Scharron-Del-Rio, 2001; Coleman, Wampold & Casali, 2003; Griner & Smith, 2006; Hall, 2001). Accordingly, this has garnered interest among researchers focused on mental health intervention and prevention efforts in AI communities (e.g., Gone & Alcántrara, 2007). Research suggests that cultural adaptations may be beneficial for treatment outcomes; however, it has also been noted that more research is needed to support the need and efficacy of these protocols (Huey & Polo, 2008; Huey et al., 2014). Furthermore, the rigorous research needed to adapt EBT protocols to be culturally informed potentially places a significant burden on AI communities and health/mental health services, underscoring the importance of thorough assessment of need. Specifically, researchers have called for evidence that a particular EBT demonstrates limited efficacy within a population and a systematic approach to adaptation (e.g., Barrera & Castro 2006; Davidson et al. 2013; Kumpfer et al. 2008; Lau, 2006). Based on the experience in tribal IRB processes we extend these recommendations to include early, deep community engagement with particular emphasis on collaboration between tribe and research in delineating research development, tangible benefit to the community, and sustainability (e.g., SAMHSA, 2019) of EBT research. It is important to note that conducting any research within tribal communities presents unique ethical considerations; for full consideration of these issues see Saunkeah and colleagues (2021).

Importantly, many standard EBT protocols include flexibility to tailor treatment components to meet the needs of individual clients and patients. Thus, culturally informed care would enable some degree of cultural influence in EBT care in such a scenario. In order to capitalize on this flexibility, training with respect to AI historical context (e.g., exploitation in research, displacement and relocation, social and environmental determinants of health) and tribal specific culture may improve idiographic culturally-informed care in the context of extant EBTs and would be an essential feature of successful adaptation of established protocols, if needed.

We conclude with the following recommendations for cultural adaptations of EBTs for treatment of mental health disorders: 1) more representation of AIs in the development of EBT protocols and RCT samples broadly to provide data necessary to evaluate efficacy of standard protocols; 2) increased training aimed at cultural humility for providers of EBTs working with AI communities; 3) rigorous evaluation of standard EBT engagement and outcome limitations to determine if cultural adaptations are warranted; 4) collaborative development of culturally-adapted EBT protocols with particular AI communities, if deemed necessary; and 5) specified efforts and plans to ensure benefit and sustainability of gains based on research activities beyond the life of the grant or study.