One Health in Indigenous Communities: A Critical Review of the Evidence
Bibliographic record
Abstract
Indigenous populations around the world face disproportionately high rates of disease related to the environment and animals. One Health is a concept that has been used effectively to understand and address these health risks. One Health refers to the relationships and interdependencies between animal, human, and environmental health and is an emerging research field that aligns with indigenous views of health. To understand the applicability of One Health in indigenous communities, a critical review was undertaken to investigate evidence of One Health research in indigenous communities internationally, assess the strength of evidence, and understand what gaps are present. This review included the appraisal of twenty-four studies based in five regions: Canada, Africa, Australia, South America, and Central America. The review found that there is a need for studies of high strength, with rigorous methods, local leadership, and active involvement of indigenous viewpoints, to be undertaken in indigenous communities internationally that focus on One Health. It highlights the need to further consider indigenous viewpoints in research to reduce limitations, increase effectiveness of findings, consider appropriateness of recommendations, and benefit communities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".