A longitudinal study of the effects of risk perception and response efficacy on adaptation of elderly individuals and individuals with chronic health conditions to heat waves
Bibliographic record
Abstract
BACKGROUND: In this study, protection motivation theory (PMT) was used to understand the drivers of adaptation of elder individuals and individuals with chronic health conditions to heatwaves. More particularly, it examines the influence of the perception of heatwave risks and of the effectiveness of five heat protection actions commonly recommended by health authorities on the adoption of these actions. METHODS: The data for this study were collected using three surveys on a sample of residents of Longueuil city - Canada. This longitudinal design allowed the use of individual and time fixed effects model to test the effects of risk perception and response efficacy on adaptation to heatwaves. In addition, the random assignment of a subset of study participants to a group that was exposed to heat warning (treatment group) and a group that was not exposed (control group) enabled the use of instrumental variable method to assess the robustness of the panel fixed effect model findings. RESULTS: A positive association was found between the perception of the effectiveness of heat protection actions and the likelihood of adopting these actions. However, risk perception and its interaction with the perceived effectiveness of protective actions show no significant effects. CONCLUSION: This study suggests that improving the perception of the effectiveness of heat prevention actions increases their adoption. However, it questions the relevance of raising risk awareness to achieve this objective.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".