Reporting evidence on the environmental and health impacts of climate change on Indigenous Peoples of Atlantic Canada: a systematic review
Bibliographic record
Abstract
Abstract While evidence of Indigenous Peoples’ climate knowledge and adaptation practices is readily available in Canada, regional variations are poorly understood, and proper representation and recognition in academic and planning contexts is scarce. Much less still is known about the health and environmental impacts of climate change on these communities. This review sought to report and assess the evidence of such impacts on Indigenous Peoples in Atlantic Canada over the past two decades. Current published studies focused on Indigenous Peoples’ knowledge and perceptions and highlight government policy for adaptation measurements. We systematically searched publications between January 2002 and March 2022 from the Web of Science, PubMed, Google Scholar, and Science Direct databases, screening for (1) environmental and (2) health impacts of climate change on Indigenous Peoples. Fifty-six articles were selected and thoroughly reviewed using the GRADE approach to assess the quality of the evidence. The quality of evidence ranged from low to moderate, and the evidentiary foundation for links between climate change and health effects was weak. We thus find an opportunity for future research to focus on climate-related effects on the health and lands of Indigenous Peoples within Atlantic Canada, especially concerning impacts on mental health.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".