A Comprehensive Approach to Enhance Older Adults’ Preparedness for Extreme Heat: COPE -Engage
Bibliographic record
Abstract
Abstract Climate change has resulted in increase in the frequency of extreme heat days and older adults are disproportionately affected due to physiological vulnerabilities and systemic barriers to adaptation. In this study, we conduct focus group interviews with older adults and semi-structured interviews with community partners (e.g., healthcare and housing providers, city planners and community service organizations) to identify best practices and challenges in existing heat response strategies. Focus groups findings provide rich, narrative-driven insights into lived experiences, enabling us to understand how older adults interpret and respond to heat-related risks as they navigate extreme heat and access community services. Questions are asked about awareness and preparedness; perception of vulnerability; socioeconomic influence; thermal comfort and heat relief. Findings from the semi-structured interviews provide in-depth information on how public organizations and service sectors deliver service and programs during these events. The community partners are asked about access and quality of community facilities, services and programs to mitigate and manage extreme heat events. Preliminary findings demonstrate that older adults who are supported through specific community-based organizations during heat events have better awareness about risk factors and are able to access and use services more effectively. Transportations play a key role in utilization of services like cooling centres. Community organizations emphasize the need for more multi-sectorial collaboration to better serve older adults. Both groups highlight the need for better communication and awareness campaigns around risks factors of extreme heat event. A multi-year approach integrates co-creation workshops to refine intervention strategies based on study findings.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".