Heat-health messaging in Canada: A review and content analysis of public health authority webpages and resources
Bibliographic record
Abstract
Background: With the growing threat posed by extreme heat, heat-health messaging communicated by public health authorities is critical for raising community awareness and action. This study sought to (i) identify what heat-health content is shared online by Canadian public health authorities and (ii) analyse the material to develop an understanding of the content included within the resources. Study design: Qualitative content analysis. Methods: We reviewed public health authority websites in Canada (n = 99) and extracted all available heat-health content. Content analysis of each resource was performed using descriptive codes related to three categories - populations at greater risk, actions to reduce risk and awareness and knowledge. Results: Within the public health authority websites searched, 417 webpages and online resources were identified (range: 1-43). Over half of the material came from regional health authorities (56 %), primarily located in Ontario and British Columbia (60 %). At least one population at greater risk of heat stress (e.g., older adults, children) (range: 0-24) was mentioned in 59 % of the materials, 81 % mentioned at least one action or behaviour to reduce risk (e.g., stay hydrated) (range: 0-40), and 91 % provided material related to raising awareness and knowledge (range: 0-12). Conclusions: Although a wide array of webpages and online resources were identified, the material content and availability varied considerably across authorities and provinces and territories. These results provide important insights into the composition of heat-health webpages and online resources within Canada and can help guide relevant revisions and additions to the existing heat-health materials.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.036 | 0.067 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 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".