Understanding Resilience and Mental Well-Being in Southwest Indigenous Nations and the Impact of COVID-19: Protocol for a Multimethods Study
Bibliographic record
Abstract
BACKGROUND: Despite experiencing many adversities, American Indian and Alaska Native populations have demonstrated tremendous resilience during the COVID-19 pandemic, drawing upon Indigenous determinants of health (IDOH) and Indigenous Nation Building. OBJECTIVE: Our multidisciplinary team undertook this study to achieve two aims: (1) to determine the role of IDOH in tribal government policy and action that supports Indigenous mental health and well-being and, in turn, resilience during the COVID-19 crisis and (2) to document the impact of IDOH on Indigenous mental health, well-being, and resilience of 4 community groups, specifically first responders, educators, traditional knowledge holders and practitioners, and members of the substance use recovery community, working in or near 3 Native nations in Arizona. METHODS: To guide this study, we developed a conceptual framework based on IDOH, Indigenous Nation Building, and concepts of Indigenous mental well-being and resilience. The research process was guided by the Collective benefit, Authority to control, Responsibility, Ethics (CARE) principles for Indigenous Data Governance to honor tribal and data sovereignty. Data were collected through a multimethods research design, including interviews, talking circles, asset mapping, and coding of executive orders. Special attention was placed on the assets and culturally, socially, and geographically distinct features of each Native nation and the communities within them. Our study was unique in that our research team consisted predominantly of Indigenous scholars and community researchers representing at least 8 tribal communities and nations in the United States. The members of the team, regardless of whether they identified themselves as Indigenous or non-Indigenous, have many collective years of experience working with Indigenous Peoples, which ensures that the approach is culturally respectful and appropriate. RESULTS: The number of participants enrolled in this study was 105 adults, with 92 individuals interviewed and 13 individuals engaged in 4 talking circles. Because of time constraints, the team elected to host talking circles with only 1 nation, with participants ranging from 2 to 6 in each group. Currently, we are in the process of conducting a qualitative analysis of the transcribed narratives from interviews, talking circles, and executive orders. These processes and outcomes will be described in future studies. CONCLUSIONS: This community-engaged study lays the groundwork for future studies addressing Indigenous mental health, well-being, and resilience. Findings from this study will be shared through presentations and publications with larger Indigenous and non-Indigenous audiences, including local recovery groups, treatment centers, and individuals in recovery; K-12 and higher education educators and administrators; directors of first responder agencies; traditional medicine practitioners; and elected community leaders. The findings will also be used to produce well-being and resilience education materials, in-service training sessions, and future recommendations for stakeholder organizations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44727.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.013 |
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 source (direct Gemma or distilled Codex), 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".