Reducing the risks of extreme heat for seniors: communicating risks and building resilience
Bibliographic record
Abstract
INTRODUCTION: As the global climate changes, heat waves are having a disproportionate impact on seniors and other socially vulnerable groups. In order to mitigate the threats of extreme heat, it is critical to develop and promote resources for coping during these events. A better understanding of the role of risk perceptions and the factors that influence them is needed in order to improve public responses to threatening events, particularly among seniors. METHODS: This mixed-methods study examined risk perceptions and coping practices in seniors using qualitative interviews (n = 15) and a survey (n = 244) of seniors across Waterloo Region, Ontario. RESULTS: Seniors showed relatively accurate risk tracking as indicated by the link between measures of actual risk and perception of personal risk. While vulnerability to heat is often believed to be associated with inaccurate perceptions of risk, within our sample, vulnerability appears more strongly related to social location and access to resources. Participants described social connections as important resources for resilience, but the stigma surrounding vulnerability, and other social norms, as barriers to seeking support. CONCLUSION: The positive relationship between participants' risk perceptions and actual risk for negative consequences of extreme heat was an important finding, given that problems of emergency preparedness and risk reduction are often framed as issues of awareness of risk, rather than social location and inequality. Along with increased public resources for coping with extreme heat, communicating about resources, fostering social connections and reducing stigma may be important leverage points for increasing the resiliency of seniors to heat waves.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".