The Intersection of the COVID-19 Pandemic and the 2021 Heat Dome in Canadian Digital News Media: A Content Analysis
Bibliographic record
Abstract
During the 2021 Heat Dome, 619 people in British Columbia died due to the heat. This public health disaster was made worse by the ongoing COVID-19 pandemic. Few studies have explored the intersection of heat with COVID-19, and none in Canada. Considering that climate change is expected to increase the frequency of extreme heat events, it is important to improve our understanding of intersecting public health crises. Thus, this study aimed to explore media-based public health communication in Canada during the COVID-19 pandemic and the 2021 Heat Dome. A qualitative content analysis was conducted on a subset of media articles (n = 520) related to the COVID-19 pandemic which were identified through a previous media analysis on the 2021 Heat Dome (n = 2909). Many of the articles provided conflicting health messages that may have confused the public about which health protective actions to take. The articles also showed how the COVID-19 pandemic may have exacerbated the health impacts of the 2021 Heat Dome, as pandemic-related public health measures may have deterred people away from protecting themselves from heat. This study, which provides novel insight into the prioritization of public health messaging when an extreme heat event occurs concurrently with a pandemic, supports the need for consistent heat health guidance.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".