Climate change, poverty, and health: A scoping review of the Canadian context
Bibliographic record
Abstract
Introduction: The understanding of the role of climate change in worsening health outcomes and social disparities is growing, highlighting poverty as a key factor amplifying exposure to environmental hazards and as a result of such exposure. However, there has been insufficient focus on the unique risks encountered in Canada, highlighting the necessity for targeted understanding to inform and evaluate effective responses. The question guiding the present scoping review is: In the Canadian context, what are the impacts and implications of climate change and weather extremes on the physical and mental health of those experiencing poverty, as evidenced in the peer-reviewed academic literature? Methods: = 28). Results: The articles included 23 reports of primary research and five reviews, spanning 2000 to 2022. Six theme areas emerged at the climate change, poverty, and health intersection in Canada: heat, air pollution, food security, pollen, wildfire evacuation, and health systems. Discussion: Addressing poverty reduction is identified as a critical lever for reducing environmental risks and enhancing resilience, although challenges remain in implementing evidence-based interventions due to significant gaps in knowledge. Future research should consider exploring outcomes disaggregated by sociodemographic factors, interconnections between heat and air pollution, and interventions targeting vulnerable groups.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.021 | 0.039 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".