Improving Native American Elder Access to and Use of Healthcare Through Effective Health System Navigation
Conditions: Access to Health Care
Sponsor: Pacific Institute for Research and Evaluation

 
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Improving Native American Elder Access to and Use of Healthcare 
NCT03550404 
February 7, 2023 
 
 
 

 
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Background  
Although health care is a legal right of members of federally recognized Tribes, American 
Indians (AIs) still have persistent disparities in health status and access to services. They 
suffer from higher rates of illness, substance use problems, and mental distress compared to 
other populations in the United States (U.S.). At the same time, AIs are significantly less 
likely to have health insurance coverage and often live in rural areas or on reservations, 
where access to health care is more difficult. These disparities especially affect American 
Indian elders (AIEs), while few data exist on the health, insurance status, and access to health 
care of AIEs specifically. This study employs a participatory and mixed-method research 
design to understand AIEs’ experiences with health care and health insurance in order to 
improve healthcare practices and policies for this population, which is largely excluded from 
national and state discussions of health reform.  
Compared to non-Hispanic whites, AIs report poorer physical and mental health and are less 
likely to see a medical doctor or have a usual source of health care [1]. Adults who are AI 
suffer from disproportionately high incidences of cerebral vascular disease, diabetes mellitus, 
heart disease, hypertension, obesity, and stroke [1–10], and are more likely to have substance 
use problems and mental distress [11–15]. Almost one-fourth of AIs have a disability [16]. 
With more than 1.2 million, or nearly a quarter, of AIs without healthcare coverage in 2011, 
lack of insurance is implicated in these disparities [14]. Historically, this has been especially 
true for AIEs, as many as one in four of whom were uninsured a decade ago [17], with 
estimates of their current insurance rates varying [18]. Moreover, young AIEs, 55–64 years 
old, are more likely to be uninsured compared to all other adults in the same age bracket [14, 
19]. These uninsured AIEs are more likely to go without health care compared with insured 
AIEs, especially on reservations, where AIEs are more likely to report lack of health 
insurance compared to those in urban areas [19]. Geographic areas with high concentrations 
of AIs also have significant disparities in access to and use of health services, and particularly 
preventive care, such as cancer screening [20].  
Despite the common belief that the Indian Health Service (IHS) will fully address AIE 
health-related needs, gaps in insurance adversely affect access to health care for AIEs and 
thus their overall health status [19, 21]. This is largely due to the fact that the IHS is severely 
and chronically underfunded: while per capita healthcare expenditure was $8097 for the 
general U.S. population in 2014, it was only $3107 for IHS users [22]. The IHS is not an 
insurance provider and cannot protect against unforeseen medical expenses [23]. When 
healthcare demands exceed funds, users may be denied provider-recommended services, 
compelling AIEs to either pay major medical bills or do without treatments [24]. 
Additionally, with almost 60% of indigenous people in the U.S. living in non-reservation 
settings [14], many AIs otherwise eligible for services at IHS or tribally-run facilities 
operating under Public Law 93–638 cannot obtain health care [25–27].  
Major public insurance reforms, like the 2010 Patient Protection and Affordable Care Act 
(ACA), have included provisions designed to improve access to and quality of services for 
seniors, including AIEs. For example, the ACA included the reauthorization of the Indian 
Health Care Improvement Act (IHCIA) of 1976 along with specific language to “modernize” 
IHS and tribally-run 638 programs. However, such reforms have often failed to substantively 
reduce the pressing healthcare disparities faced by AI people in general, and AIEs in 
particular, while the future of the promising provisions in the ACA is far from certain. 
Barriers that have prevented AIEs from obtaining insurance under previous reforms include 

 
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reluctance to participate in government-funded programs because of stigma, limited outreach 
and culturally insensitive communication practices, burdensome enrollment procedures, and 
fluctuating eligibility requirements [21].  
The literature concerning AI enrollment in managed care plans is both thin and dated. 
However, general population research suggests that public managed care programs may pose 
greater challenges to accessing coverage and health care for ethnic minorities compared to 
whites, negatively affect community-based healthcare systems, and displace culturally-
informed and linguistically-fluent providers who know the needs of local people [28]. Low 
reimbursements also discourage experienced providers from taking part in such plans [29, 
30], contributing to a two-tiered healthcare system that further disadvantages economically 
insecure minorities [31–33]. Minority enrollees have also reported cultural barriers, more 
problems with access, and lower service utilization and quality of health care [32, 34–37].  
In addition to these barriers, AIEs with variable health literacy must still navigate a 
complicated healthcare system, regardless of whether they seek health care from the IHS, a 
tribally-run 638 facility, or a managed care program available under Medicaid, Medicare, or 
ACA Health Insurance Exchange (HIX) plans. A recent study in a large representative 
sample of adults cautions that, while historically uninsured persons with low health literacy 
are more likely to be eligible for Medicaid expansion than persons with adequate health 
literacy, they are also less likely to make use of the health insurance options available to them 
under the ACA [38]. Forty-eight percent of AIs and 59% of older adults demonstrate low 
health literacy [39], resulting in less use of preventive services, greater risks for emergency 
care, hospitalization, morbidity and mortality, and higher healthcare costs [38, 40–42]. Health 
literacy is influenced by culturally-based beliefs and communication styles, English 
proficiency, and experiences of bias in healthcare settings [43]. Additional challenges 
affecting older adults include difficulty using print materials, interpreting numbers, and 
performing calculations. Older adults tend not to ask questions or elicit clarification of 
information provided in healthcare contexts [44]. Due to cognitive aging, they may process 
information more slowly, have less working memory, and have difficulty comprehending 
abstractions [45]. Vision, hearing, and other impairments may interfere with information 
processing [43, 45–47]. Moreover, health literacy increasingly necessitates the ability to 
operate computers and negotiate the Internet, which precludes many older adults.  
From a social justice perspective, limited access to health insurance and quality, equitable 
health care are major contributors to health disparities for ethnic minority seniors [48]. 
Although national health-related data exist on AIs, they are limited. The Medicare 
Enrollment Database, for example, consistently under-identifies AIs [49]. Researchers lament 
the “severe” lack of state- and sub-state level data concerning insurance status and access to 
care among AIEs [19, 50]. Arguments citing insufficient sample sizes, generalizability 
concerns, and attendant analytic challenges are typically invoked to justify the shameful 
paucity of AIE health and health services data, and the ongoing marginalization of AIEs as a 
“hardly reached” population [16, 44, 51, 52]. Without reliable data relevant to their life 
experiences, AIEs are disadvantaged in terms of advocating for culturally-attuned health 
literacy and service interventions that optimally address their needs [52, 53]. Our study is 
innovative precisely because it illuminates new foci for the study of AIE healthcare 
disparities, and because it offers a potentially replicable model to meaningfully engage AIEs 
and other ethnic minority groups in social and health policy research to improve access to 
health care, services, and health status.  

 
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Conceptual Framework  
This research is guided by the seminal socio-ecological model (SEM) [54]. The SEM calls 
attention to determinants of health literacy, access, and utilization at five levels: individual 
(e.g., race, ethnicity, age, and education; employment/housing status; income; health and 
mental health history); social support (e.g., family, friends, and peers); organizational (e.g., 
outreach, health care, and social service programs, and professional staff/providers); 
community (e.g., healthcare systems, socio-economic climate, and social and cultural factors 
that shape help-seeking behavior); and policy (e.g., tribal, state, and national policies, laws, 
and healthcare funding mechanisms) [54, 55]. Our overarching goal in this research is to 
effect change on the individual level (e.g., empowering AIEs to make informed decisions 
about their health care), and promote strategies to leverage assistance from social supports, 
professional staff/providers, and communities that will impact change at higher influence 
levels. The SEM framework is useful to this end, as it facilitates understanding of the ways in 
which lower-level changes dynamically interact with and influence broader forces, including 
the various tribal, state, and national policies impacting how AIEs navigate healthcare 
systems.  
Our methodological approach can be characterized as “concurrent QUAL + quant,” as we 
will simultaneously collect and analyze qualitative and quantitative data using structured and 
unstructured interviews and focus groups with AIEs and other stakeholders. To ensure that 
the most comprehensive and culturally-relevant approach is employed, the qualitative 
component of the study will provide the dominant frame for analyses [56]. We will also 
include concept mapping (CM) [57], a research method consistent with the SEM, in that it 
allows for a multi-level understanding of the many factors bearing on the topics at hand. 
Through triangulation of the qualitative and quantitative data and guided by CM, we will 
integrate both sets of findings to yield overall interpretations within the context of the study 
aims and develop a culturally relevant intervention to improve AIE access to and use of 
health care and health insurance [58].  
Project Aims  
The primary objective of this research is to produce a holistic and descriptive account of how 
AIEs engage with public insurance programs and healthcare systems, and to identify and 
refine strategies to ensure that this neglected population does not remain excluded from large-
scale policy reforms. There are four specific aims:  
1. Assess how AIEs understand, access, maintain, and use insurance coverage.  
2. Characterize AIE help-seeking and healthcare experiences in dominant service delivery 
settings, i.e., IHS, tribally-run 638 facilities, and managed care programs.  
3. Identify and compare factors that affect AIE access to health care as perceived by AIEs 
and other relevant stakeholders, i.e., outreach workers (OWs), healthcare staff and providers, 
public sector administrators, and tribal leaders.  
4. Develop and assess implementation feasibility of a structured intervention for OWs that 
promotes enhanced patient navigation, in addition to healthcare literacy, access, and usage 
among AIEs.  

 
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Key products are a mobile application called the “Seasons of Care American Indian Elder 
Outreach and Navigation Guide” (AIEONG) that will be tailored for use by OWs and 
healthcare staff working with elders, as well as by elders and their families, and training 
materials that will enable specially-trained “AIE Navigators” to function as “cultural brokers” 
and bridges between AIEs and healthcare systems [59].  
Methods  
Study design  
This is a mixed-methods study that is participatory and community-driven in nature, flexible 
in design, and which aims to be primarily descriptive. It is based on a collaboration of 
investigators from the Pacific Institute for Research and Evaluation (PIRE), local experts on 
Native American health policy, the New Mexico Indian Council on Aging (NMICoA), the 
University of New Mexico, and the University of California, San Diego, initiated and guided 
by an Advisory Board of AIE leaders and allies. Below, we describe the setting of the 
research, followed by a description of the four phases of this five-year R01-funded study. 
Table 1 provides an overview of each phase, including participants, methods, and timeline.  
Table 1. Overview of study phases, methods, and timeline 
 
Phase 1: 
Convening AIE 
Advisory Board 
and Training AIE 
Consultants 
Phase 2: Semi-
structured 
Interviews and CM 
with AIEs (Aims 1 
and 2) 
Phase 3: Semi-
structured 
Interviews and CM 
with Key 
Stakeholders (Aim 
3) 
Phase 4: 
Development, 
Implementation, 
and Evaluation of 
AIEONG (Aim 4) 
Participant 
Category 
• AIE leaders and 
Allies (n = 20; 8 
Advisory Board 
members and 12 
AIE Consultants) 
• AIEs (n = 96-
144; 24-36 per 
region) 
• Sample stratified 
by age and 
gender to ensure 
adequate 
representation for 
men and women 
aged 55-64 and 
65+ 
 
• Outreach workers 
(n = 12) 
• Healthcare 
staff/providers (n 
= 12) 
• Public-sector 
administrators (n 
= 12) 
• Tribal leaders (n 
= 12) 
• AIE Navigators 
(n = 16; 8 in P1 
and 8 in P2) 
• AIEs (n = 48; 12 
per region) 
• Healthcare 
staff/providers (n 
= 48; 12 per 
region) 
 
Sampling and 
Recruitment 
Strategy 
• Reputational case 
selection 
(candidates 
identified based 
on 
recommendations 
from members of 
research team 
and local experts 
from NMICoA) 
[86] 
• Stratified 
purposive sample 
(candidates 
selected from Ai 
senior centers, 
healthcare 
settings, AIE 
Consultant 
referrals, and 
advertising to 
capture variations 
in the target 
population) [67] 
• Reputational case 
selection 
(candidates 
identified based 
on 
recommendations 
from local 
experts from 
NMICoA, 
healthcare 
support groups, 
and tribal 
programs) [86] 
AIE Navigators: 
• Reputational case 
selection 
(candidates 
identified based 
on 
recommendations 
from local 
experts and 
interest in 
implementing 
AIEONG) [86] 

 
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AIEs and 
healthcare 
staff/providers 
• List sampling 
(candidates 
selected from 
master lists that 
will be compiled 
from attendance 
records of 
individuals who 
participate in 
AIEONG-related 
activities or have 
contact with an 
AIE Navigator) 
Inclusion Criteria 
AIE Advisory 
Board: 
• Expertise and 
experience 
related to AIE 
health and 
insurance issues 
• Willingness and 
ability to 
participate in AIE 
Advisory Board 
activities 
AIE Consultants: 
• Language and 
communication 
skills 
• Availability for 
training and data 
collection 
activities 
• History of 
sustained 
community 
involvement 
• Age 55+ 
• Identifying as AI 
• Able to consent 
and complete 
study procedures 
• For CM subset, 
able to read in 
English 
• Individuals who 
champion, 
develop, 
implement, 
and/or engage in 
outreach, 
enrollment, and 
service delivery 
planning or 
provision to AIEs 
AIE Navigators: 
• Working in 
health and 
insurance 
outreach to AIEs 
in a variety of 
healthcare 
settings (IHS, 
tribally-run 638 
programs, senior 
centers) 
AIEs: 
• Age 55+ 
• Identifying as AI 
• In contact with 
an AIE Navigator 
• Able ot consent 
and complete 
study procedures 
• Able to read in 
English 
Healthcare 
staff/providers: 
• Working in a 
healthcare or 
social service 
profession 
• Interacting with 
AIEs as part of 
their jobs 
• In contact with 
an AIE Navigator 

 
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Data Collection 
Method 
N/A 
• AIE Health 
Questionnaires 
(AIEHQ) 
(Quantitative) 
• Semi-structured 
interview 
(Qualitative) 
• CM (with a 
subset of 48 
AIEs) 
• Demographic 
survey 
(Quantitative) 
• Semi-structured 
interview 
(Qualitative) 
• CM 
Period 1 AIE 
Navigators: 
• Pre- and post-
evaluation 
interviews 
• Monthly rating 
questionnaire 
Period 2 AIE 
Navigators: 
• Pre- and post-
evaluation 
interviews 
AIEs and 
healthcare 
staff/providers: 
• Focus groups 
Goals 
AIE Advisory 
Board: 
• Community 
oversight of 
study goals and 
progress 
• Approve data 
collection 
procedures 
• Prioritize data 
analysis plans 
• Help interpret 
findings 
• Guide 
development and 
evaluation of 
AIEONG 
AIE Consultants: 
• Increase local 
participation in 
study 
• Enhance cultural 
and linguistic 
relevance 
• Ensure ethical 
data collection 
procedures 
• Offer essential 
content expertise 
AIEHQ and 
Interview: 
• Compare health, 
healthcare and 
insurance access 
and utilization, 
health 
satisfaction, 
health literacy, 
etc., among AIEs 
and other aging 
U.S. populations 
• Understand key 
issues affecting 
help-seeking, 
healthcare, 
access, and 
satisfaction for 
AIEs at all SEM 
levels 
CM: 
• Further explore 
issues identified 
in interviews 
• Generate relevant 
action items to 
improve 
healthcare access 
and utilization for 
AIEs 
Survey and 
interview: 
• Understand key 
issues affecting 
help-seeking, 
healthcare, 
access, and 
satisfaction for 
AIEs from the 
perspective of 
key stakeholders 
at all SEM levels 
CM: 
• Further explore 
issues identified 
in interviews 
• Generate relevant 
action items to 
improve 
healthcare access 
and utilization for 
AIEs 
• Promote 
healthcare 
literacy, access, 
and use for AIEs 
• Develop a 
replicable and 
culturally tailored 
model to enhance 
health system 
navigation 
among 
underserved 
populations 

 
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Timeline 
Convene Advisory 
Board: 
• Months 1-60 
Train research 
assistants and AIE 
Consultants: 
• Months 1-9 
• Months 9-24 
• Months 8-24 
AIEONG Planning 
and Training: 
• Months 24-30 
AIEONG 
Feasibility 
Assessment: 
• Months 33-39; 
Months 45-51 
Research setting  
As a culturally and geographically diverse state with the fourth-largest AI population in the 
U.S., New Mexico (NM) is an ideal setting in which to investigate the implications of 
federal-, state-, and/or tribal-led efforts to expand insurance options to AIEs under the ACA. 
The state also provides a unique opportunity to contextualize and compare AIE experiences 
in a range of healthcare venues located in both reservation and non-reservation settings. AIs 
comprise over 10.4% of 2,085,287 NM residents [60] and nearly 15% of NM’s Medicaid 
population [61]. Prior to the first ACA enrollment period, close to 40% of all AIs in NM were 
uninsured. In 2016, 10.8% of AIs in NM were uninsured [60].  
This research centers largely on the experiences of AIEs from NM’s 19 Pueblos that are 
members of the NM Indian Title VI Coalition, Inc. (a confederation of programs that focus 
on AIEs, i.e., senior centers). The Pueblos are commonly divided into three broad 
geographical regions: North, South, and West, and include some tribes that have assumed 
total control over healthcare delivery from the IHS, some that rely on a combination of 
tribally-run 638 programs and IHS, and some that rely on the IHS at this time. The scope of 
options allows for richer comparisons of administrative and healthcare practices and policies 
affecting AIEs across regions. To broaden our sample and facilitate limited comparisons 
between rural and urban AIEs, we will also recruit non-reservation AIEs in the Albuquerque 
metropolitan area, home to an estimated 32,571 AIs from varied tribal backgrounds [62].  
Phase 1: Convening AIE advisory board and training AIE consultants 
This study originated with AIEs from several Pueblos who were troubled by ongoing health 
disparities and the capacity of health insurance and healthcare systems to ameliorate them. 
These AIEs approached the researchers about collaborating, and then sought organizational 
and tribal support to ensure the study’s feasibility and acceptability. This research aims to 
overturn the traditional paradigm of conducting research projects “on” rather than “with” AI 
communities, with each partner bringing unique strengths.  
In order to meet this goal, this study employs a participatory approach involving AIEs from 
start to finish. First, an 8-person Advisory Board, comprised of AIEs and allies, will meet 
bimonthly throughout the study to provide input into study protocols. Members of the 
Advisory Board will draw on their expertise and experiences related to AI communities and 
elder issues to: help with recruitment; guide development and evaluation of strategies to 
promote healthcare literacy, access, and usage among AIEs; review study progress and help 
address potential implementation problems; assist in prioritizing data analysis plans, interpret 

 
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findings, and enhance our understanding of their significance; and serve as a forum for 
ensuring community involvement in the research design and its execution, and interpretation 
and application of data.  
Second, data collection will be conducted by pairing researchers with 12 “AIE Consultants” 
(e.g., seniors interested in collecting data from other AIEs about experiences with insurance, 
care, and systems change), hired in consultation with the Advisory Board. AIE Consultants 
will be recruited based on language and communication skills (including persons fluent in 
languages likely to be spoken by AIE research participants, such as Keres, Tewa, Southern or 
Northern Tiwa, Towa, and Zuni), availability, and histories of sustained community 
involvement, a proxy measure for their likely commitment to the study. The AIE Consultants 
will participate in a three-day training, which can be repeated as needed. The training to be 
finalized with Advisory Board input will include team-building exercises to: foster trust, 
communication, and respect; define the boundaries of each person’s roles; and establish 
shared knowledge regarding AIE health and healthcare disparities, public insurance 
programs, the ACA, and the IHCIA. Second, the training will familiarize all participants with 
the study protocol, involve a review of data collection instrumentation, and provide hands-on 
training via the use of role-play exercises to facilitate uniform implementation of data 
collection methods [63]. Third, we will apply case-based, problem-solving methods to help 
ensure that all trainees recognize the delicacies of human subjects research within AI contexts 
and among aging populations. By pairing trained AIE Consultants with researchers in the 
field, we will increase local participation and enhance the cultural and linguistic relevance of 
the study. Researchers working in indigenous contexts concur that in-person interviews by AI 
community members, including AIEs, are effective in gathering data about AI healthcare 
needs [2, 64, 65]. Our AIE collaborators offer essential content expertise on healthcare 
challenges that they, their peers, and fellow community members face, and have the “know 
how” to ask questions in a respectful, ethical, and culturally appropriate manner.  
Phase 2: Semi-structured interviews and concept mapping (aims 1 and 2) with AIEs 
Participants and recruitment 
To achieve Aims 1 and 2, we will use multiple strategies to recruit a total of 96 AIEs for 
completion of semi-structured interviews. We will identify potential partici-pants by 
regularly visiting AI senior centers in the four regions (North, South, West, and 
Albuquerque), plus veterans organizations, community health fairs, and quarterly NMICoA 
meetings. We will use a qualitative sampling strategy designed to represent the range of 
views within a group (or region) to determine similarities and differences in knowledge, 
beliefs, and experiences related to the issues investigated [66, 67]. The domains of interest 
here are insurance coverage, help-seeking and healthcare experiences, and factors affecting 
AIE access and utilization. We will use purposive sampling to recruit candidates able to 
discuss the elements of these domains. Unlike probability sampling procedures, in purposive 
sampling there is no way to precisely estimate how many of each type of participant might be 
required for a study. However, qualitative researchers generally agree that in-depth 
interviewing requires between 12 and 26 persons within a designated group [67– 69]. We 
have calculated the interview sample sizes with AIEs accordingly; each is large enough to 
examine a range of experiences related to the topics at hand. If, during the process of 
obtaining informed consent from AIE participants, the researcher or AIE Consultant feels that 
a potential participant may not be able to understand or complete study procedures, they will 

 
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administer the MINI-COG [70] to test for cognitive impairment. Participants deemed positive 
for cognitive impairment will not be eligible to participate.  
After completion of the semi-structured interviews, we will use the same strategies to recruit 
48 AIEs who can read in English to participate in the remaining CM exercises (described 
below). These may, but will not necessarily, include individuals who participated in semi-
structured interviews.  
Data collection  
Quantitative data  
Participants will first complete the “American Indian Elder Health Questionnaire” (AIEHQ), 
based on four surveys administered among AIEs across the country, thus providing a 
comparison between our NM sample and national multiethnic datasets [64]. To construct the 
AIEHQ, we reviewed and selected pertinent questions on service experiences from the 2011 
Access to Care component of the Medicare Current Beneficiary Survey [71], an annual in-
person longitudinal panel survey. We also included relevant questions on health and health 
care from the 2012 National Health Interview Survey [17, 19, 72, 73], a cross-sectional in-
person survey with a nationally representative household sample of the civilian non-
institutionalized population; the Behavioral Risk Factor Surveillance System [2, 4, 6, 74], a 
telephone survey of adults; and the 2002 National Survey of American Families [75], based 
on a representative sample of the civilian, non-institutionalized population in 13 states. The 
AIEHQ features mostly closed-ended questions covering demographics; basic health status; 
healthcare access, utilization (place/ location and provider type), and barriers; health 
insurance (type of plan[s] and coverage of alternative medicine); culturally competent health 
care; healthcare satisfaction; health literacy; use of general/health-related technology; and 
anticipated health assistance. The AIEHQ takes approximately 30 min to complete.  
Qualitative data  
After the AIEHQ or during a separate meeting (to minimize respondent burden), participants 
will take part in a semi-structured qualitative interview that investigates the changing 
healthcare environment under the ACA and other reforms from the perspective of the AIEs, 
focusing specifically on contextual issues that bear upon their individual-level experiences 
and perceptions. Questions on the interview guide yield richly descriptive data on key issues 
affecting help-seeking and health care for AIEs; social, cultural, organizational, and system-
related factors that influence access to and use of needed services; location of and general 
satisfaction with services; knowledge of and experience with the ACA, Medicare, Medicaid, 
and other insurance programs; enrollment into public insurance programs; the role of 
managed care (e.g., financing and service authorization mechanisms) in elder services; and 
overall health literacy concerns. Additionally, the interview guide will include the first step of 
the CM process (discussed in greater detail below), which asks participants to free-list or 
brainstorm items related to two focal questions with pertinent probes (i.e., “What factors 
make it easy or hard for American Indian elders to get good health care?” “What factors 
make it easy or hard for American Indian elders to get good health insurance?”). Each in-
person interview will last approximately 45 min, be digitally recorded (depending on the 
language preferences of the participant, as described below), and occur in locations deemed 
private, accessible, and safe by participant and interviewer. Use of the guide increases the 

 
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comparability of responses and affords discretion to follow up on new or unexpected 
information.  
An AIE Consultant will accompany the researchers in the field, consult on interview 
etiquette, help conduct interviews, and provide cultural and linguistic translations when 
necessary. After each interview, the researchers and AIE Consultant will “debrief” verbally 
about the encounter, i.e., highlights of what was learned, factors affecting data quality, and 
issues to explore in future interviews. The researchers will compile a written record of this 
debrief with review by the AIE Consultant.  
We will conduct most interviews in English, with provisions for persons preferring to speak a 
Pueblo language. Due to the incredible linguistic diversity across Pueblos and the proximity 
of these communities to English-speaking populations, Pueblo residents are generally fluent 
in English [76]. Moreover, only tribal members appropriately have access to written and 
recorded texts in Pueblo languages [77], meaning that producing written interview scripts in 
Pueblo languages may violate local models of information control. Given these constraints, 
we will inform AIE participants that interviews (except for the CM component) can be 
conducted in the language of their choice. The AIE Consultants, who will be fluent in their 
respective AI languages, will implement the interview protocol when participants elect a 
language other than English. The researcher and the AIE Consultant will compile 
comprehensive field notes in English during and immediately after such interviews. With 
permission from participants, we will record the qualitative portions of all interviews 
undertaken in English. While in other research contexts it would be preferable to create audio 
or textual recordings of non-English interactions for back translation and quality assurance 
purposes (i.e., making certain that instrument implementation and accurate documentation of 
participant responses are uniform), doing so in this context would conflict with common 
cultural rules among the Pueblos regarding the collection and circulation of information 
concerning their heritage languages, and would likely result in feasibility and acceptability 
problems, and violate cultural codes. Cognitive pretesting of all instruments will ensure that 
their use does not create undue stress and burden for participants, are appropriate for the 
study population, and will yield desired information regarding insurance and healthcare 
issues.  
CM data  
The remaining CM exercises of pile-sorting and ranking will take place at a later date to 
reduce the possibility of participant fatigue, and last between 45 and 60 min [78–80]. The 
CM method is becoming increasingly prominent in community-driven, participatory research 
that seeks to determine locally relevant intervention strategies [81, 82]. It is useful when 
working with diverse stakeholders who may hold different perspectives on insurance and 
healthcare services. Two important CM goals are to further explore issues and themes 
identified in the qualitative interviews and to then generate a list of action items that are truly 
relevant to improving access and utilization among AIEs. The research team will identify 
approximately 80 unique statements from AIE answers to the focal questions contained in the 
semi-structured interview (i.e., “What factors make it easy or hard for American  
Indian elders to get good health care?” “What factors make it easy or hard for American 
Indian elders to get good health insurance?”) and inscribe each statement on a card (e.g., 
“Having to travel long distances to get to the clinic,” “Having to wait too long for an 
appointment”). During the CM exercise, the participants will be asked to put similar 

 
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statements into piles. After the participant has created piles by grouping the statements, the 
researcher will ask the participant to describe the reasons for these choices and label each pile 
with a theme (e.g., “Transportation”). Finally, each participant will be asked to rank or rate 
each statement using a 1-to 10-point scale on three dimensions: 1) how much each statement 
affects American Indian elders; 2) how common each statement is among American Indian 
elders; and 3) how easy each statement would it be to change. This may appear a daunting 
task, but participants often see it as fun and engaging. Members of our research team have 
successfully deployed this technique with other vulnerable populations, including persons 
with serious mental illnesses.  
Data analysis  
Quantitative analysis  
We will use the quantitative data to summarize and compare characteristics of the AIE 
participants. Where possible, we will conduct descriptive comparisons that assess for 
differences between our study and AI and non-AI samples from other external studies that 
use the same questions. These results will help evaluate the extent to which the AIEs in our 
sample experience health access and utilization-related disparities. The anticipated 
quantitative analytical techniques include chi-square tests, t-tests, and ANOVAs. We will use 
multivariate regression strategies in exploratory analyses to identify characteristics associated 
with insurance and healthcare access and utilization outcomes among AIEs. An estimate of 
power is inappropriate given the qualitative sampling strategy guiding our overall sample 
selection.  
Qualitative analysis  
We will employ an iterative process to analyze textual qualitative data. First, we will assign 
codes to segments of text ranging from a phrase to several paragraphs based a priori on topics 
and questions in the interview guides. We will then engage in open coding to identify and 
define new codes related to themes and issues not previously considered, followed by 
focused coding to determine which of these themes/issues recur and which represent unusual 
concerns to participants [83]. By constantly comparing and contrasting codes with one 
another, we will group codes with similar content or meaning into broader themes linked to 
retrievable segments of text [83, 84]. As part of the process, we will triangulate interview 
findings across several dimensions  
(e.g., gender, region, age group, insurance/coverage type, etc.). Here, we will create a matrix 
detailing specific themes pertinent to key study issues outlined in our specific aims and the 
SEM (e.g., enrollment implicated at the individual, organizational/community, and policy 
levels), and supporting data from participants. We will then engage in a side-by-side 
comparison of various perspectives from AIEs across regions or other key dimensions to 
identify points of convergence and divergence in statements related to the specific 
themes/issues under consideration. In this staged approach, researchers will code sets of notes 
and transcripts, create detailed memos that describe and link codes to each theme/issue, and 
then pass on this work to the co-investigators for review. Discrepancies in coding and 
analysis will be identified during this review process and resolved during regular team 
meetings. Products of this process will include a summary report of key themes/issues that 
cross cut and are particular to specific types of participants.  

 
13 
CM analysis  
We will use Concept Systems software to conduct the analytical steps to identify and create 
visual representations of emergent themes and empirically assess whether key demographic 
characteristics such as gender, region, age group, insurance/coverage type, etc., are associated 
with systematic thematic variations. First, we will construct a similarity matrix that identifies 
how often statements are grouped together across all participants [57, 85]. Next, we will 
apply multidimensional scaling techniques to calculate the two-dimensional “distance” 
between each statement. This will result in a two-dimensional visual representation of the 
location of each statement relative to other statements. The final step will involve hierarchical 
cluster analysis to group similarly located statements within a specific participant category. 
As a participatory study, this step will be accomplished collaboratively by including review 
and feedback from the Advisory Board and AIE Consultants. Final concept maps and labels 
will be presented to the Advisory Board for approval, and then used to empirically assess (via 
t-tests) the extent to which demographic characteristics influence the rankings of each 
concept cluster (e.g., importance or feasibility of addressing specific issues).  
Mixed-methods triangulation  
The research team will integrate the quantitative, qualitative, and CM-derived findings to 
develop a more comprehensive understanding of the data [56, 58]. First, quantitative analyses 
of key participant characteristics will sharpen and guide deeper-level analyses of the 
qualitative data. Second, we will summarize the various analyses and then conduct a side-by-
side comparison of findings from the three data sources to evaluate the degree of 
convergence related to our aims. Such comparisons will make it possible to link our data 
across sources; for example, how people enumerate their healthcare experiences 
(quantitative), how they describe their experiences in their own words (qualitative), and 
which experiences they would prioritize if given the opportunity to change (CM). Such 
comparisons will prompt more nuanced analyses when findings diverge, increase the validity 
and credibility of overall results, and foster a comprehensive understanding of AIE 
experiences with insurance and healthcare systems, and potential strategies for enhancing 
these experiences.  
Phase 3: Semi-structured interviews and concept mapping with key stakeholders (aim 3) 
Participants and recruitment 
To achieve Aim 3, we will use reputational case selection to solicit local expert 
recommendations for persons who best exemplify OWs (n = 12), healthcare staff/providers (n 
= 12), public administrators (n = 12), and tribal leaders (n = 12) in championing, developing, 
implementing, or engaging in outreach, enrollment, and service delivery planning or 
provision to AIEs [86]. Local experts will include NMICoA Health Committee members and 
local partners with expertise in Native American health policy. We will create a final list of 
candidates and, based on Advisory Board advice, rank those to contact first via phone, email, 
and mail to participate.  
Data collection  
Quantitative data  

 
14 
Participants will first complete a brief survey to capture demographic data and key 
quantifiable information regarding their work experience in public administration, health care 
and/or insurance, and involvement in ACA and other reforms.  
Qualitative data  
Participants will take part in a semi-structured interview specific to each participant category. 
The OWs and healthcare staff/providers will be asked about their work roles and 
responsibilities related to AIEs; organizational factors affecting their work with AIEs; the 
effect of the ACA and other key reforms on this work, insurance, and healthcare options for 
AIEs; the circumstances in which AIEs can act upon these options; and the health literacy 
challenges faced in communicating health-related information to AIEs. Interviews with 
public sector administrators and tribal leaders will center on state and tribal efforts to 
incorporate AIEs into public insurance programs, facilitate better access to affordable health 
care, increase health literacy at the level of patients and organization, and reduce AIE health 
disparities. We will inquire into the particular role that they and others play in these efforts 
and factors that help or hinder initiatives to address AIE needs in the changing healthcare 
environment. Interview guides for all stakeholders will include the same two focal questions 
with pertinent probes (i.e., “What factors make it easy or hard for American Indian elders to 
get good health care?” “What factors make it easy or hard for American Indian elders to get 
good health insurance?”) posed to AIEs as the first step of the CM process.  
CM data  
At a later date, we will invite these diverse participants to take part in the same CM activities 
discussed above for Phase 2 (i.e., sorting and ranking).  
Data analysis  
Data analysis for Phase 3 will follow the same procedures enumerated above for Phase 2, 
including qualitative, quantitative, CM analysis, and mixed-methods triangulation. Data 
analyses from Phase 3 will also be integrated into those generated during Phase 2 in order to 
evaluate the degree of convergence or divergence across stakeholder types (i.e., AIEs, OWs, 
healthcare staff/providers, public sector administrators, and tribal leaders), as well as to 
expand on, and provide nuance to, Phase 2 quantitative and qualitative findings.  
Phase 4: Development, implementation, and evaluation of the Seasons of Care American 
Indian elder outreach and navigation guide (AIEONG) (aim 4) 
To promote healthcare literacy, access, and use for AIEs as specified in Aim 4, we will 
incorporate findings from Phases 2 and 3 and data-driven intervention strategies into the 
development of a web-based mobile application (app), the Seasons of Care AIEONG. A 
preliminary logic model for the AIEONG is in Table 2. The model responds to the National 
Action Plan to Improve Health Literacy and calls from the Agency for Healthcare Research 
and Quality for patient navigators to help surmount health literacy challenges in complex 
healthcare systems [87, 88]. It includes three aspects that align with our theoretical 
framework, the SEM: (1) Promote literacy about insurance and health care among AIEs of 
diverse cultural backgrounds. The AIEONG will provide AIE Navigators with user-friendly 
information regarding insurance/healthcare options, requirements of and services available 
through these options, how to enroll, and strategies to effectively communicate this 

 
15 
information to AIEs and their families (e.g., creating a welcoming environment, allowing 
time for interaction, eliciting questions, active listening, verifying comprehension, etc.). This 
information will provide the basis for outreach efforts with the AIEs. (2) Educate 
staff/providers within healthcare delivery systems serving AIEs in reservation and non-
reservation communities. Conventional navigator programs under the ACA do not usually 
focus on the broader context of healthcare delivery, nor do they center on “hardly reached” 
AIEs specifically. Yet, staff/providers within IHS, tribally-run 638 programs, and non-
reservation healthcare venues may lack basic knowledge of eligibility requirements and 
enrollment procedures for public assistance programs or special provisions for AIs under the 
ACA. They may fail to recognize the learning styles and preferences of AIEs, how aging can 
impact cognitive function, how hearing and vision affects health literacy skills, and the 
cultural, organizational, and bureaucratic barriers specific to healthcare settings that prevent 
AIEs from making informed decisions in health situations [43, 45–47]. The AIEONG will 
provide the AIE Navigators with a data-based overview of the barriers encountered by AIEs 
(as prioritized via the CM activities), and describe feasible strategies—developed with the 
Advisory Board—for engaging staff/providers to reduce these barriers and provide correct 
information to elder patients. (3) Encourage the inclusion of AIE perspectives in development 
of healthcare policy. Policy is rarely formulated or enacted with input from AIEs, nor are 
outreach, enrollment, and eligibility systems in public insurance programs developed with 
their unique needs in mind. Insufficient attention to AIE input means that policymakers and 
other decision makers, i.e., healthcare executives, may remain unaware of the complexities of 
contemporary insurance arrangements, the nuances of tribal and non-tribal healthcare 
systems, health literacy barriers, and other challenges specific to aging AIs.  
Table 2. Preliminary logic model of Seasons of Care American Indian Outreach and 
Navigation Guide (AIEONG) 
Assumptions 
Inputs 
Activities/Outputs 
Outcomes 
Impact 
Promote healthcare literacy among AIEs of diverse cultural backgrounds 
On individual 
and social 
support levels, 
AIEs/families 
may lack 
knowledge of 
rights and 
coverage options 
under public 
insurance plans, 
and encounter 
difficulties 
getting and 
understanding 
information to 
make informed 
decisions 
• AIE Navigators 
trained in applying the 
AIEONG 
• AIE Navigators 
recognize how cultural 
issues and aging 
processes affect health 
literacy for AIEs 
• Information on AIE 
rights, coverage, and 
health literacy 
• Strategies for making 
this information 
accessible and 
meaningful to AIEs 
• AIE Navigators 
develop accessible 
content regarding 
rights, coverage 
options, and 
implications for 
group 
presentations and 
one-on-one 
consultations with 
AIEs/families 
• AIE Navigators 
hold group 
presentations and 
one-on-one 
consultations with 
AIEs/families to 
share accurate 
information on 
coverage options 
and enrollment 
• AIEs and families 
know more about 
their rights and 
coverage/healthca
re options 
• AIEs successfully 
enroll in public 
insurance 
programs 
• AIEs understand 
how their 
insurance works 
• AIEs stay insured 
• Increased 
use of 
healthcare 
services and 
decreased 
AIE health 
disparities 
• Shift in 
individual 
attitudes, 
beliefs, and 
behaviors to 
create a 
“Culture of 
Coverage” 
Educate staff/providers within healthcare delivery systems serving AIEs in reservation and non-reservation 
communities. 
At organization 
and community 
• AIE Navigators 
trained in skills to 
• AIE Navigators 
undertake 
• More competency 
and self-efficacy 
• Enhanced 
response of 

 
16 
levels, AIEs are 
likely to 
encounter barriers 
within healthcare 
settings, i.e., lack 
of knowledge 
among 
staff/providers, 
which can reduce 
AIE access to 
insurance and 
healthcare 
services 
educate staff/providers 
about effective 
outreach with AIEs 
• Information on 
application, eligibility 
determination/enrollm
ent processes 
• Information on 
cultural, organization, 
and bureaucratic 
barriers specific to 
healthcare 
• List of resources 
(including training 
opportunities) for 
staff/providers on 
health reform and AIE 
health literacy 
informational 
outreach with 
staff/providers in 
IHS, tribally-run 
638 programs, and 
other facilities 
• AIE Navigators 
education 
staff/providers 
about common 
barriers, their 
implications for 
insurance/healthca
re access, and 
processes to enroll 
AIEs in public 
insurance 
programs 
• AIE Navigators 
offer advice 
and/or conduct 
role plays with 
staff/providers to 
enhance skills in 
communicating 
and presenting 
health information 
to AIEs 
• AIE Navigators 
provider 
staff/providers 
with resources 
(e.g., training 
options) 
to engage AIEs 
among 
staff/providers 
• Increased access 
to resources (i.e., 
training and 
knowledge of 
evidence-based 
health literacy 
strategies) for 
staff/providers 
• Reduction in 
barriers most 
encountered by 
AIEs in 
healthcare 
settings 
staff/provide
rs in 
healthcare 
systems to 
the unique 
needs of 
AIEs 
• More 
effective 
outreach and 
services to 
AIEs 
• Shift in 
organization
al and 
community 
attitudes, 
beliefs, and 
behaviors to 
create a 
“Culture of 
Coverage” 
Encourage the inclusion of AIE perspectives in development of healthcare policy 
AIEs/families/O
Ws may lack 
experience in 
sharing feedback 
and input into 
insurance options 
and healthcare 
systems for aging 
AIs on the policy 
level 
• AIE Navigators 
trained to identify 
community-based 
partners (e.g., 
nonprofit, voluntary 
and professional) 
• Information on AIE 
views and experiences 
with insurance and 
healthcare systems 
• List of strategies to 
remove barriers from 
insurance and 
healthcare systems 
among AIEs 
• List of policymakers, 
healthcare executives, 
and tribal leaders who 
create or manage 
policy regarding AIE 
healthcare 
• AIE Navigators 
undertake 
informational 
outreach with 
community 
partners to enlist 
AIE support 
• AIE Navigators 
create social 
spaces where 
AIEs, families, 
and community 
partners meet to 
share experiences 
and identify 
policy issues 
• AIE Navigators 
collaborate with 
AIEs, families, 
and partners to 
enact strategies to 
address policy 
issues 
• AIE Navigators, 
AIEs, families, 
• More access to 
support systems 
for AIEs and 
families 
• More attention to 
AIE-specific 
issues in policy 
formulation 
• More 
involvement of 
AIEs/families in 
policy 
development 
• Developmen
t of 
healthcare 
policy that 
contributes 
to a “Culture 
of 
Coverage,” 
and 
addresses 
healthcare 
disparities of 
AIEs 

 
17 
and partners target 
policymakers, 
healthcare 
executives, and 
tribal leaders for 
education on 
insurance/healthca
re issues 
Based on the findings from Phases 2 and 3, we will consult with the Advisory Board to revise 
the logic model and create a more detailed implementation plan for the AIEONG and 
tracking outcomes. We will use the CM findings to determine what stakeholders want to 
prioritize and what they view as changeable. The qualitative data will also offer insight into 
contextual factors likely to influence the adoption of particular health literacy strategies. 
Upon finalizing the AIEONG with approval from the Advisory Board, we will train and co-
locate two groups of OWs within IHS, tribally-run 638 programs, and community agencies 
and systems that serve AIEs. These OWs will become “AIE Navigators.”  
During and after implementation of the AIEONG, we will conduct a two-phase feasibility 
assessment informed by field-level implementation science models (e.g., Consolidated 
Framework for Implementation Research [CFIR] [89, 90] and Exploration, Preparation, 
Implementation, and Sustainment [EPIS]) [91], which identify a range of factors at multiple 
SEM levels that influence the introduction, usage, and sustainability of new health practices. 
Awareness and measurement of implementation and sustainability factors are critical during 
intervention development so that the AIEONG can be successfully integrated into standard 
OW practice.  
Participants and recruitment  
For the first of two six-month intervention periods (P1), an initial group of eight OWs will be 
recruited voluntarily with the consultation of the Advisory Board and will take part in a two-
day training to become AIE Navigators. Trainees will be comprised of individuals who 
already do health and health insurance outreach work with AIEs, including community health 
representatives, benefits coordinators, senior center employees and/or volunteers, and public 
health nurses. They will be located in a variety of healthcare contexts (e.g., IHS and tribally-
run 638 facilities, senior centers, social service offices). For the second six-month 
intervention period (P2), a new group of eight OWs will be recruited and trained to be AIE 
Navigators using the same procedures, for a total of 16 AIE Navigators.  
At the end of P2, we will use list sampling to recruit 48 AIEs and 48 healthcare 
staff/providers to participate in focus groups. Focus group participants will be selected from 
master lists of individuals who participated in AIEONG-related activities, or have been in 
contact with or received individual consultation from an AIE Navigator. We will invite these 
persons to a focus group in their location via phone, email, and mail. The groups will 
comprise six to eight participants.  
Implementation of intervention  
AIE Navigators will be trained using a curriculum created under the supervision of the 
Advisory Board, which will instruct AIE Navigators in the use of the AIEONG app. 
Emphasis will be placed on ensuring AIE Navigators are “healthcare literate” with AIE elders 

 
18 
and teaching them to enhance the health literacy capacity of others with whom they come 
into contact. The training will use problem-based learning methods and interactive techniques 
(e.g., role plays) to build skills in helping “hardly reached” AIEs and professional 
staff/providers to sort through information regarding insurance, eligibility requirements, 
supporting documentation (e.g., Tribal census identification number), understanding what 
services are covered, recognizing cost-sharing and premium responsibilities, and choosing a 
provider. We will center on the pragmatic “do’s and don’ts” of sharing information about 
insurance and health care with AIEs. Here, the “do’s” include keeping information focused 
and in plain language, repeating information as needed, allowing time to process information, 
using face-to-face communication and vetted videos and pictures to make information 
personally relevant, emphasizing the short-term benefits of taking a particular action, and 
being available in the future to answer remaining questions. The “don’ts” include equating 
health literacy with reading ability, assuming that AIEs are comfortable asking questions 
within intimidating healthcare contexts or using computers and the Internet, and 
overwhelming them with technical jargon and information or complex visuals [46]. We will 
base initial training content on the SEM, interview data, and CM findings, and the literature 
on best-practice and evidence-based health literacy strategies [43, 45, 87, 92–94]. We will 
repeat the training for newly recruited AIE Navigators in instances of turnover.  
After training, the AIE Navigators will implement the AIEONG in the context of their 
everyday outreach work with AIEs over two six-month intervention periods (P1 and P2). 
Their goal will be to facilitate health literacy to shift attitudes, beliefs, and behaviors to create 
a “Culture of Coverage” for AIEs at individual, organizational/community, and policy levels. 
Possible activities are described in Table 2. Separated by distance, the AIE Navigators will 
receive coaching as necessary, using virtual meeting space, to help refine their 
implementation skills from a member of the research team with experience in AIE health 
outreach.  
We will align the feasibility assessment with both phases of implementation with a thorough 
midpoint review and revision of the AIEONG prior to the start of P2. At the beginning of P2, 
we will re-train the P1 AIE Navigators on the updated AIEONG, but randomly select only 
half to continue with coaching. Those not selected will be interviewed again at the end of P2 
to learn the extent to which they still used the AIEONG and whether other untrained OWs 
now utilized all or parts of it. This will provide useful information about AIEONG 
sustainability and diffusion after intervention experts are no longer directly involved in 
supporting its use. P2 AIE Navigators will be trained using the updated P2 AIEONG. 
Therefore, during P2, approximately one-third of AIE Navigators will have P1 experience 
and two-thirds will be new. This strategy will afford us access to the on-the-ground insights 
of persons who have used both versions of the AIEONG and to get feedback about the 
training and initial implementation of the updated version of the AIEONG.  
Data collection  
Training evaluation and feedback (P1 and P2)  
All AIE Navigator trainees will complete a pre- and post-evaluation interview consisting of 
open-ended qualitative and closed-ended quantitative questions at the start and end of 
training. The interview will measure perceived competence and confidence in sharing 
information regarding AI rights, coverage options, and health care with AIEs and their 
families, undertaking informational outreach with and providing resources to staff/providers, 

 
19 
and creating social spaces within which community stakeholders can spearhead policy- and 
system-level discussions about AIE healthcare coverage and access. Trainees will also 
complete written feedback forms consisting of open-ended questions.  
Feasibility assessment (P1)  
Each month in P1, the AIE Navigators will complete a self-administered 10-item rating 
questionnaire to assess key characteristics of the AIEONG. Based on implementation models 
such as CFIR and EPIS, the following characteristics are critical to intervention uptake: 
relative advantage over education/outreach activities as usual, compatibility with pre-existing 
navigation system, complexity or difficulty to learn, trialability or testability, organizational 
support, and observed effects [95]. We will explore these characteristics in greater depth 
during the post-evaluation interview with AIE Navigators at the end of P1.  
Intervention evaluation (P2)  
At the end of P2, we will hold two focus groups with AIEs and two with staff/providers in 
each of the four regions (n = 8). Members of the research team will moderate the sessions 
with assistance from a researcher or an AIE Consultant. The groups with AIEs will be 
conducted onsite at local senior centers; the groups with staff/providers will likely take place 
in a healthcare facility or tribal administrative office. Sessions will be held at various times of 
the day to accommodate staff schedules and participant travel. Each session will begin with a 
welcome and an explanation of how we will conduct the session. General ground rules will 
be established about respectful listening (e.g., no criticism of others’ statements). Participants 
will be cautioned about revealing confidential information and informed that they are free to 
participate as little or as much as they desire, including withdrawing from the group. The 
moderator will ensure that each individual can participate as much as s/he is willing, without 
being made to feel pressured. Each focus group will consist of 8–10 open-ended questions 
posed in a structured, sequential manner. The questions will center on knowledge of, 
exposure to, and general experiences with the AIE Navigators and the strategies advocated 
for in the AIEONG. For staff/providers, we are particularly interested in the extent to which 
they can integrate aspects of the AIEONG (based on their contact with the AIE Navigators) 
into their workplaces. Focus groups will include a short CM exercise (see description of CM 
above) so that participants can help us sort and rank factors likely to facilitate or inhibit 
broader adoption and sustainment of AIE Navigators and the AIEONG. The groups will take 
up to 90 min and will be digitally recorded and transcribed.  
Data analysis  
Training evaluation and feasibility assessment  
First, we will analyze data collected from the pre- and post-evaluation interviews of AIE 
Navigators using the qualitative analysis methods described for Phases 2 and 3 above. 
Second, we will analyze data from the monthly rating questionnaire given to AIE Navigators 
in order to obtain overall assessments of the AIEONG, including acceptability, feasibility, 
perceived effect on OW practice, and observed influence on achievement of outcome goals 
(defined by the Advisory Board) at the individual/ social support, organizational/community, 
and policy levels. We will also analyze the need for possible mid-course corrections, solicit 
recommendations to modify the curriculum/training and AIEONG, and make revisions. Data 
from the rating questionnaire and qualitative interviews in P2 will also enable us to identify 

 
20 
areas of actualized improvement from P1 and incorporate any new learning that would 
facilitate the implementation of the AIEONG in both practice and future clinical trials.  
Intervention evaluation  
Focus group data will be analyzed in keeping with the qualitative data analysis procedures 
described for Phases 2 and 3 above. The focus groups represent the culmination of this 
research: to deliver a quality tool based on careful research that can aid in improving AIE 
health care.  
 
 

 
21 
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