Climate Risks and Impacts on the Physical Health of Indigenous Peoples in Canada
Bibliographic record
Abstract
Indigenous nations are seeing the effects of climate change, despite contributing relatively little to greenhouse gas emissions. The immediate and long-term health impacts of these changes are attracting growing attention from public health organizations, Indigenous organizations, and researchers across Canada. A scoping review of the scientific literature was conducted primarily to guide public health actions aimed at preventing the health impacts of climate change. The review sought to answer the following question: What is known scientifically about climate risks and Indigenous health in Canada? The review focused specifically on factors influencing physical health. Analysis of 21 articles revealed that: • The climate risks most frequently reported in Indigenous contexts include: the thawing and freezing of permafrost and/or sea ice, extreme precipitation, temperature departures, unpredictable weather, and storms. • The most commonly cited health impacts of climate risks are: unintentional injuries and unintentional injury deaths, food poisoning, chronic and respiratory diseases, and parasitic infections. The determinants of health primarily studied are: access to land, diet, culture, and knowledge transfer. Impacts on public services, self-determination and governance, and schooling have been less explored. • The authors focused most on the lived experiences of Inuit and First Nations, and not at all on those of the Métis. Arctic communities have been studied more extensively than Subarctic communities. • The research primarily adopted a participatory and collaborative approach emphasizing the observations and experiences of those considered most directly affected by climate change—for example, hunters and Elders. Several areas remain to be explored. The effects of climate risks on the physical health of Indigenous Peoples do not appear to have been measured quantitatively, representing a gap in the scientific knowledge on the subject. Moreover, there are no articles on First Nations or Inuit in Quebec, in either community or urban settings. Several hazards, including wildfires, and their impacts on health and its determinants, have been little or not at all studied in the scientific literature.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".