Elderly care facility location in the face of the climate crisis: A case study in Canada
Bibliographic record
Abstract
The climate crisis poses a dual threat to the environment and human health, with older adults being particularly vulnerable. This study aims to establish a network of elderly care facilities designed to respond to climate-induced disasters—an essential element in creating resilient cities that address the challenges of elderly care within the context of the climate crisis. First, we formulate an equation grounded in expert opinions to estimate demand in each region based on factors such as the elderly population, average age, frailty, and proximity to healthcare service nodes. We then introduce an Integrated Coverage and Distance Facility Location problem (ICDFL), a novel approach tailored to address the unique needs of seniors during disasters while considering governmental budget constraints. The ICDFL, therefore, simultaneously pursues two key objectives: (i) maximizing coverage while respecting a maximum allowable coverage radius, and (ii) minimizing overall travel distance. The first objective ensures the resilience of elderly health, guiding healthcare policymakers, while the second considers transportation and service costs faced by government agencies. To address dual-objective optimization, we utilize the epsilon-constrained optimization technique, which enables precise management of both coverage and distance goals through an iterative problem-solving approach. We apply the proposed model to a real-world case study in southern Ontario, Canada, to validate its effectiveness. This is an important aspect of the study, as Canada’s warming trend is happening twice as fast as the rest of the world and its elderly population is expanding at an alarming rate. The results of our model provide guidance to policy makers and healthcare planners in improving emergency preparedness, thus creating a sustainable community for our growing elderly population and improving the well-being of older people during climate-induced disasters.
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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 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".