Updating Health Canada’s Heat-Health Messages for the Environment and Climate Change Canada Heat Warning System: A Collaboration with Canadian Experts
Bibliographic record
Abstract
It is critical to inform the public of the threat heat poses to health and provide actionable guidance on mitigating this risk before, during, and after heat events. To help educate the public during heat events, Health Canada works closely with Environment and Climate Change Canada (ECCC) to distribute heat-health messaging through a weather warning system. However, the warning system’s current list of messages dates back over a decade. Continually evaluating and updating messages is critical to ensure they are based on the best evidence available. A review was conducted to assess the existing heat-health messages and propose new messages based on recent empirical studies. The proposed messages were reviewed to ensure that readability and equity considerations were integrated. Academic, public health and meteorology experts across Canada reviewed the proposed messages and applied further revisions. The original list of heat-health messages included 12 messages. Based on the evidence and external reviews provided by 42 experts (academic: n = 9; public health: n = 22; meteorology: n = 11), messages were removed, merged, added and revised. The final list used by ECCC includes 30 messages. Health Canada’s heat-health messages disseminated through ECCC’s weather warning system were revised to ensure they are important, action-oriented, evidence-based, readable, equitable, regionally applicable, and timely. Ensuring these messages reflect current knowledge will be an ongoing and iterative process to support the public’s preparedness efforts to protect themselves and others during heat events.
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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 | 0.137 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".