Returning to Our Roots: Tribal Health and Wellness through Land-Based Healing
Bibliographic record
Abstract
(1) Background: Settler colonialism has severely disrupted Indigenous ancestral ways of healing and being, contributing to an onslaught of health disparities. In particular, the United Houma Nation (UHN) has faced large land loss and trauma, dispossession, and marginalization. Given the paucity of research addressing health for Indigenous individuals living in Louisiana, this study sought to co-identify a United Houma Nation health framework, by co-developing a community land-based healing approach in order to inform future community-based health prevention programs. (2) Methods: This pilot tested, co-designed and implemented a land-based healing pilot study among Houma women utilizing a health promotion leadership approach and utilized semi-structured interviews among 20 UHN women to identify a UHN health framework to guide future results. (3) Results: The findings indicated that RTOR was a feasible pilot project. The initial themes were (1.) place, (2.) environmental/land trauma, (3.) ancestors, (4.) spirituality/mindfulness, (5.) cultural continuity, and (6.) environment and health. The reconnection to land was deemed feasible and seen as central to renewing relationships with ancestors (aihalia asanochi taha), others, and body. This mindful, re-engagement with the land contributed to subthemes of developing stronger tribal identities, recreating ceremonies, and increased cultural continuity, and transforming narratives of trauma into hope and resilience. Based on these findings a Houma Health (Uma Hochokma) Framework was developed and presented. (4) Conclusions: Overall, this study found that land can serve as a feasible therapeutic site for healing through reconnecting Houma tribal citizens to both ancestral knowledges and stories of resilience, as well as viewing self as part of a larger collective. These findings also imply that revisiting historically traumatic places encouraged renewed commitment to cultural continuity and health behaviors—particularly when these places are approached relationally, with ceremony, and traumatic events tied to these places, including climate change and environmental/land trauma, are acknowledged along with the love the ancestors held for future generations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".