Climate crisis risks to elderly health: strategies for effective promotion and response
Bibliographic record
Abstract
The climate crisis significantly impacts the health and well-being of older adults, both directly and indirectly. This issue is of growing concern in Canada due to the country's rapidly accelerating warming trend and expanding elderly population. This article serves a threefold purpose: (i) outlining the impacts of the climate crisis on older adults, (ii) providing a descriptive review of existing policies with a specific focus on the Canadian context, and (iii) promoting actionable recommendations. Our review reveals the application of current strategies, including early warning systems, enhanced infrastructure, sustainable urban planning, healthcare access, social support systems, and community engagement, in enhancing resilience and reducing health consequences among older adults. Within the Canadian context, we then emphasize the importance of establishing robust risk metrics and evaluation methods to prepare for and manage the impacts of the climate crisis efficiently. We underscore the value of vulnerability mapping, utilizing geographic information to identify regions where older adults are most at risk. This allows for targeted interventions and resource allocation. We recommend employing a root cause analysis approach to tailor risk response strategies, along with a focus on promoting awareness, readiness, physician training, and fostering collaboration and benchmarking. These suggestions aim to enhance disaster risk management for the well-being and resilience of older adults in the face of the climate crisis.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".