Public health communication in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: Communication is central to the implementation and effectiveness of public health measures. Informed by theories of good governance, COVID-19 pandemic public health messaging in 3 Canadian provinces is assessed for its potential to encourage or undermine public trust and adherence. METHODS: This study employed a mixed-methods constant comparative approach to triangulate epidemiological COVID-19 data and qualitative data from news releases, press briefings, and key informant interviews. Communications were analyzed from January 2020 to October 2021 in Nova Scotia, Ontario, and Alberta. Interview data came from 34 semi-structured key informant interviews with public health actors across Canada. Team-based coding and thematic analysis were conducted to analyze communications and interview transcripts. RESULTS: Four main themes emerged as integral to good communication: transparency, promptness, clarity, and engagement of diverse communities. Our data indicate that a lack of transparency surrounding evidence and public health decision-making, delays in public health communications, unclear and inconsistent terminology and activities within and across jurisdictions, and communications that did not consider or engage diverse communities' perspectives may have decreased the effectiveness of public health communications and adherence to public health measures throughout the COVID-19 pandemic. CONCLUSION: This study suggests that increased federal guidance with wider jurisdictional collaboration backed by transparent evidence could improve the effectiveness of communication practices by instilling public trust and adherence with public health measures. Effective communication should be transparent, supported by reliable evidence, prompt, clear, consistent, and sensitive to diverse values. Improved communication training, established engagement infrastructure, and increased collaborations and diversity of decision-makers and communicators are recommended.
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".