Temperature Check: Designing Support Systems \nfor Older Adults in Heat Waves
Bibliographic record
Abstract
The climate crisis is escalating and extreme heat events are becoming more frequent, more intense, and longer in duration. The health risks associated with extreme heat are well documented. However, ensuring the health of older adults –the fastest growing demographic in Canada – is a complex challenge that we are already facing today. Taking a design research approach, this study goes through a process of first diverging to explore the issue, and then converging on the ways in which communities can support older adults in adopting adaptive health-related behaviours in times of extreme heat. In exploration, this research connects with the voices of older adults, as well as professionals working in health, social and emergency service role to gain a deeper understanding of the challenge at hand. \n \nThe Health Belief Model, a framework for understanding health-related behaviours, is used to push examination further, and to define a set of design principles for innovation: encourage self-sufficiency and independence, promote learning and understanding of extreme heat risks, remove barriers or incentivize benefits to taking adaptive measures, support psychological wellbeing as well as physical health, maximize existing community resources, and broaden engagement of stakeholders. These principles are used to generate five community-based interventions suitable even for smaller cities that can help to protect older adults’ psychological and physical health while promoting new social practices and norms in times of extreme heat.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".