CRISIS COMMUNICATION DURING HEALTH CRISES: THE CASE OF CANADIAN OFFICIALS’ SOCIAL MEDIA PRESENCE DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
To effectively manage a health crisis, citizens need to have shared Situational Awareness (SA) of the crisis. This study proposes that the public draws upon shared mental models of the crisis to achieve shared SA. Declarative, procedural, and strategic knowledge bases comprise the essential aspects of shared mental models of mission-critical situations like the COVID-19 pandemic. Therefore, public officials must provide a constant flow of crisis declarative, procedural, and strategic knowledge on social media. This study investigates Canadian officials’ presence on Twitter during the COVID-19 pandemic. Analyzing a dataset of 213,089 Canadian officials’ tweets shows that their presence was either for health crisis management (73.26%) or crisis-related topics (46.66%). Declarative (72.03%), procedural (38.1%), and strategic knowledge (30.18%) comprised 96% of the health crisis management tweets. This study informs research and practice by analyzing the essential role of knowledge types in creating a shared SA in managing health crises.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.039 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".