Content Analysis of Official Public Health Communications in Ontario, Canada during the COVID-19 Pandemic
Bibliographic record
Abstract
Effective communication by governmental organizations is essential to keep the public informed during a public health emergency. Examining the content of these communications can provide insight into their alignment with best practices for risk communication. We used content analysis to determine whether news releases by the Ontario government contained key elements of effective risk communication, as outlined by the Health Canada and Public Health Agency of Canada Strategic Risk Communication Framework. News releases between 25 January 2020 and 31 December 2022 were coded following the five elements of the framework: situational transparency, stakeholder-centered content; evidence-based rationales for decisions; continuous improvements in updating information; and descriptions of risk management. All 322 news releases contained at least one element of the framework, and all five elements were identified at least once across the dataset. Risk management, transparency, and stakeholder-centered content were the most frequently identified elements. News releases near the beginning of the pandemic contained most elements of the framework; however, over time, there was an increase in the use of vague language and lack of evidence-based rationales. Increasing transparency regarding evidence-based decisions, as well as changes in decisions, is recommended to improve risk communication and increase compliance with public health measures.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".