Western Canada’s 2021 heatwave will happen again: Why we need to better protect older adults
Bibliographic record
Abstract
The sweltering heat experienced by Canadians during the 2021 heatwave in western Canada is a stark reminder that climate change is not just some far-off problem. It is already here, and we are already reeling from its impacts. Previously thought to be a once-in-a-millennium event, extreme events like this one could occur with a frequency of once every five to ten years. Compared to the rest of the population, older adults – an increasingly large share of the Canadian population – are more susceptible to heat-related trauma because of impaired thermoregulatory responses from aging and other chronic conditions. The compounded effect of climate change and an increasingly older population will necessitate that we expand the availability of health resources and the capacity of health systems in response to these stressors. Our current health care funding mechanisms, as they stand, do not address either of these problems. This commentary explores how the increasing frequency and intensity of temperature extremes impact older adults at both an individual and health systems level. Climate-related stressors in an aging demographic will require that we redefine health resilience – including a serious conversation about health systems resources – and how we currently operationalize it in the Canadian context.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".