“It Matters Who Defines It”—Defining Nutrition through American Indian, Alaska Native, and Native Hawaiian Worldviews
Bibliographic record
Abstract
Background Adequately assessing nutrition is deeply important for understanding and addressing health inequities, especially for American Indian, Alaska Native, and Native Hawaiian (AI/AN/NH) peoples who have been subjected to the malnourishing effects of settler colonialism. Mainstream approaches to nutrition rely heavily on indicators of physical diet consumption, while ignoring the contributions of food to spiritual, social, and emotional health. Considering only physical aspects of dietary consumption paints a narrow picture of AI/AN/NH foodways and nutritional health further contributing to deficit-based narratives about AI/AN/NH peoples. To adequately understand and address the nutritional health of AI/AN/NHs, strengths-based approaches to nutrition that are rooted in Indigenous ways of knowing are urgently needed. Objective To define nutrition through AI/AN/NH worldviews. Methods Three focus group discussions (n = 3) and in-depth interviews (n = 6) were conducted with 26 purposively identified AI/AN/NH food sovereignty experts and knowledge holders. Data collection and analysis were conducted iteratively. Through a process of open, focused, and axial coding, primary themes and supporting sub-themes were established and refined, and a conceptual framework (the Indigenous Nourishment Framework) was developed to reflect the relationships between primary themes, sub-themes, and the overall meaning of nourishment for AI/AN/NHs. Results The Indigenous Nourishment Model contains four main domains of nourishment (Physical, Spiritual, Emotional, and Relational), and 8 sub-themes representing concepts, practices and beliefs which contribute to overall nourishment. Conclusions Despite the importance of physical aspects of nutrition, our findings assert that spiritual, emotional, and relational wellbeing are inseparable and integral to the overall goal of nourishment for AI/AN/NHs. The resulting Indigenous Nourishment Framework developed with a diverse group of AI/AN/NH food knowledge holders reflects the strengths of AI/AN/NH foodways and builds upon existing approaches to nutrition by offering new directions for approaching and measuring nutrition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".