Hot Topic: A Systematic Review and Content Analysis of Heat-Related Messages During the 2021 Heat Dome in Canada
Bibliographic record
Abstract
CONTEXT: During the summer of 2021, western Canada experienced a deadly heat event. From the first heat alert to postevent reporting, thousands of media articles were published that reference the heat event. However, a gap remains in understanding how this communication chain-from the release of a public heat alert to information shared through media outlets to the public-currently operates to disseminate heat-related messaging across Canada. OBJECTIVE: To understand the role of digital media in delivering heat-health messaging during an extreme heat event in Canada. DESIGN: A qualitative content analysis was conducted using Canadian news articles published on the 2021 Heat Dome between June 2021 and February 2022 (n = 2909). The coding frame was designed to align with the basic framework for information gathering used in journalism (who, what, where, when, and how) and included both concept-driven and data-driven codes. RESULTS: Overall, 2909 unique media articles discussing the 2021 Heat Dome were identified, with the majority (74%) published by online news agencies (how). The highest article count was on June 29, 2021 (n = 159), representing 5% of the total data set (n = 2909) spanning 260 days (when); 57% of the identified locations were in British Columbia (where). Although we found that the top voices providing media-based heat-health messages are government officials (who), only 23% of articles included heat-health messaging that aligns with the government health alert bulletins released during extreme heat. In addition, heat-health messaging frequently included contradictory content, inconsistent language, or incorrect advice (what). CONCLUSION: The findings demonstrate clear opportunities to improve health communication related to extreme heat, perhaps most importantly, including updates to mass media messaging educating the public on heat-protective behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".