Ethnobiology and ethnomedicine of the First Peoples of America (Cree of Eeyou Istchee, Parikwene and Pekuakamilnuatsh) : the impact of diet and traditional medicine on the health and well-being of diabetics
Bibliographic record
Abstract
Indigenous Peoples around the world are disproportionately affected by diabetes. Amongst them, the Cree of Eeyou Istchee and the Pekuakamilnuatsh, from Québec (Canada), and the Parikwene, from French Guiana (France), resort to their local medicines to treat this disease. In 173 semi-structured interviews, 208 participants from these communities and/or working in their healthcare services described these medicines. A mixed-methods research approach, combining thematic analyses with multivariate statistics, was developed to analyse these descriptions.These analyses showed that Cree, Ilnu and Parikwene participants described their medicines related to diabetes through different elements of the natural world, the local practices and customs which result from them, as well as concepts linking them to the Land. Animal and plant-based pharmacopoeias are among the most discussed topics. In total, more than 381 species cited, including 109 animals, 267 plants, as well as five lichens and mushrooms, link the local dietary and medicinal systems together via notions associated with well-being or their organoleptic properties. In Québec, where Indigenous Peoples are more involved in their healthcare services, the representation of local medicines is much closer between healthcare workers and users.In general, the place of food in local medicines cannot be neglected in the context of diabetes. In addition, these medicines are inseparable from the Land which offers a space for healing, subsistence, and cultural continuity. This brings up important questions about the recognition of Indigenous rights and land rights.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".