Vulnerability to the Health Impacts of Drought in Canada in the Context of Climate Change
Bibliographic record
Abstract
Abstract Climate change is likely to increase drought globally and regionally, including within Canada, by the end of the century. In recent years, drought has affected communities across Canada and can have significant impacts on individuals. Health risks relate to the exacerbation of food and waterborne diseases, inadequate nutrition, impacts on air quality, vector-borne diseases, illnesses related to the exposure of toxins, mental health effects, and impacts from injuries (e.g., traffic accidents, spinal cord injuries). In Canada, the impacts of drought on human health and well-being are not well understood and monitoring and surveillance of such impacts is limited. In addition, important factors that make people and communities vulnerable to health impacts of drought require more investigation. These factors may differ significantly among the populations (e.g., rural vs urban) and regions (prairies, coastal, and northern). Vulnerability to drought health impacts in Canada due to climate change may be affected by: (1) changes in exposure as droughts increase or combine with other extreme events (wildfires, heat waves) to harm health; (2) changes in adaptive capacity due to impacts on, for example, health services from increasing extreme weather events; and (3) changes in susceptibility related to demographic (e.g., aging, chronic diseases) and socioeconomic trends. Effective measures to increase the resiliency of Canadians to drought health impacts require proactive adaptation efforts that increase knowledge of factors that make people and their communities vulnerable to this hazard, information as to how droughts might increase in the future, and integration of this information into future policies and programs. This paper identifies a set of indicators that may be used to gauge vulnerability to the impacts of drought on health in the context of climate change in Canada to inform adaptation actions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".