Examining the concept of One Health for indigenous communities: A systematic review
Bibliographic record
Abstract
This paper examines whether the usage of the concept of One Health in Canada-based research aligns with traditional Indigenous notions of health and wellness. A comprehensive search of the literature was conducted using primary databases, including Scholars Portal, ProQuest Social Science, Sociological Abstracts (ProQuest), OVID Healthstar, Embase, Medline, Pubmed and Google Scholar. Papers discussing One Health and Indigenous Health were selected and analyzed through Nvivo12 to generate common themes across the studies. The analysis identified three major themes that focused on One Health as it relates to climate change, zoonosis, and social relationships between humans and animals. Climate change was seen to have affected the environmental health of Northern latitude areas where many Indigenous communities reside. Infectious diseases within Indigenous communities were a frequent topic of study and indicated that infections transmitted by dogs are likely to be addressed with One Health interventions. One Health interventions are likely to equally address the health of humans, animals, and the environment. No significant connection between One Health and Indigenous knowledges was established in the analyzed articles. Articles discussed One Health as it pertains to epidemiological surveillance and research. The implications of utilizing One Health towards Indigenous Peoples and culture were not explicitly addressed.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".