Exploring Communication by Public Health Leaders and Organizations During the Pandemic: A Content Analysis of COVID-Related Tweets
Bibliographic record
Abstract
OBJECTIVES: Health communication is an essential competency in public health practice. The increasing use of social media and the connectivity between the general public and public health leaders present a unique opportunity to explore how digital communications tools were leveraged in the COVID-19 pandemic. This study explores Twitter-based communications from public health leaders and organizations across Canada and compares them with those from the World Health Organization (WHO). This research aimed to understand Twitter communications strategies to address the COVID-19 pandemic, other public health emergencies, and non-emergency public health issues. METHODS: A content analysis of COVID-related Twitter content during the first wave of the pandemic (January 1-August 31, 2020) was performed. The Canadian Institute for Health Information (CIHI) Policy Intervention Scan was used as a framework to analyze messaging from public health leaders and the WHO. RESULTS: Findings demonstrate that most tweets from public health leaders and organizations in Canada and the WHO focused on case management and public information. Gaps and areas of weakness identified include the lack of Twitter participation by some public health leaders and a narrow range of policy intervention topics, limiting the breadth and depth of public health messages. CONCLUSION: Strengthening communications can serve to improve information sharing in future pandemics or public health crises. Further research should assess how public health leaders and organizations applied communication best practices on all social media platforms and across different policy interventions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".