Indigenous Eco-Relational Engagement and mental wellbeing among American Indian and First Nation adults: Applying the Indigenous Traditional Ecological Knowledge framework
Bibliographic record
Abstract
Addressing Indigenous determinants of health includes understanding the interconnectedness among Indigenous health and wellbeing, relationship to place and Mother Earth. Though persistent challenges exert a disproportionate burden on Indigenous communities, many experience an intersecting risk profile that includes a history of settler-colonial subjugation and historical loss, while navigating loss and damage due to climate change which further impinges on their mental health. Traditional, spiritual, and cultural activities operate as functional observations of Indigenous Traditional Ecological Knowledges (ITEK) and are increasingly recognized as necessary components of adaptation and mitigation to climate change and sustainability of otherwise delicate ecosystems. In addition, corresponding traditional and cultural activities have been associated with improved mental health. The present investigation utilizes land-based cultural and traditional activities, as well as indicators of language revitalization in a composite variable - Indigenous Eco-Relational Engagement (IERE) to determine the relationship to positive mental health among Anishinaabeg in the United States and Canada. The results suggest that IERE shares a positive relationship with positive mental health among Anishinaabeg adults. Results of the present investigation help us to reconcile the relationship between Indigenous and planetary health, such that both may be supplemented through the active observation of ITEKs vis-à-vis engagement in traditional cultural, spiritual activities and language revitalization efforts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".