Assessing the language availability, readability, suitability and comprehensibility of heat-health messaging content on health authority webpages and online resources in Canada
Bibliographic record
Abstract
Heat-health communication initiatives are a key public health protection strategy. Therefore, understanding the potential challenges that all Canadians and specific groups, such as those facing literacy barriers and non-native language speakers, may experience in accessing or interpreting information, is critical. This study reviewed and evaluated the language availability, readability, suitability, and comprehensibility of heat-related webpages and online resources ( n = 417) published on public health authority websites in Canada ( n = 73). Six validated readability scales and a comprehensibility instrument were used. Most content was presented in English (90 %); however, only 7 % of the online resources were available in more than one language. The average reading grade level of the content (grade 8) exceeded the recommended level (grade 6), and only 22 % of the content was deemed superior for suitability and comprehensibility. Our study evaluating web-based materials about extreme heat published by Canadian health authorities provides evidence that the current language availability, readability, suitability, and comprehensibility may be limiting the capacity for members of the public to discern key messaging. To ensure all Canadians can access and interpret information related to heat-health protection, public health authorities may consider translating their materials into additional languages and incorporating a readability evaluation to improve public understanding. • Heat events are public health emergencies that impact those with literacy barriers. • Heat-health communication initiatives are a key public health protection strategy. • The average reading grade level of the content exceeded the recommended level. • The current readability may limit the public's ability to discern key messaging. • Health authorities may consider translating their materials into more languages.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".