THE RESILIENCE OF AMERICAN INDIAN AND ALASKA NATIVE OLDER ADULTS IN THE CONTEXT OF MAJOR HEALTH DISPARITIES IN CARDIOVASCULAR DISEASE, DIABETES, ASTHMA, AND ARTHRITIS: A NARRATIVE REVIEW
Bibliographic record
Abstract
Background: Multiple major health disparities have been documented in Indian Country, including cardiovascular disease (Howard et al., 1999), diabetes (Acton et al., 2003), asthma (Mannino et al., 2002), and arthritis (Barbour et al., 2017). Prior research has shown that the prevalence rates of these conditions in American Indians and Alaska Natives (AI/ANs) are among the highest in the United States. Given these health disparities, aging older adults in Indian Country may be especially vulnerable to the development of concurrent negative mental health outcomes, particularly depression (Garrett et al., 2015). Nonetheless, AI/AN older adults continue to age successfully and exhibit substantial mental health resilience in the face of the major health disparities (Lewis, 2016; Schure et al., 2013). Methods: The current study begins with a detailed overview of CVD, diabetes, arthritis, and asthma in Indian Country. The study transitions to a narrative review of resilience in American Indian, Alaska Native, and Canadian First Nations older adults (50 years and older). The goals of the narrative review are to: (a) examine the state of knowledge of resilience in these populations; (b) assess the degree to which the available resilience literature attends to CVD, diabetes, asthma, and arthritis; and (c) use the available literature to identify resilience strategies that can be used to enhance resilience in AI/AN/FN older adults with chronic health conditions. Results: Based on systematic reviews of PsycINFO and PubMed, 14 individual articles and 6 literature reviews were identified. The individual studies included five quantitative studies, eight qualitative studies, and one mixed qualitative-quantitative design. The current state of knowledge on resilience in AI/AN/FN older adults is summarized, including seven common themes. Currently, research on the overlap between these four specific health disparities and resilience is essentially non-existent in these populations. Sources of resilience and resilience strategies in AI/AN/FN older adults are presented under four main themes: (a) social support, connectedness, family, and community; (b) Indigenous culture and identity; (c) spiritual connection and strength; and (d) positive coping and personal healing. The review concludes with a critical examination of the limitations of the current literature and outlines future research directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".