Finding Hope in Media Hype: The Challenges of Crisis Communications During Disease Outbreak
Bibliographic record
Abstract
Raging influenza, an unthinkable return of measles and the plague, and the scourge of Ebola–such deadly and miserable diseases and viruses shape real and imagined threats all around us. Nowhere does our imagination run wilder, nor does the world appear more on edge, than through news stories and broadcasts. Given this state of affairs, the public relations agenda with respect to health crisis communications planning and execution is seriously challenged. This article uses media-hype theory to examine these unique challenges by comparing what happened and why during an influenza pandemic outbreak in 2009. It draws from a larger study of news coverage and interviews with public relations practitioners, journalists and medical leaders involved with public communications during a pandemic in Canada. Certainly, media-hype was present during the outbreak with the amount and type of news coverage unevenly representing the severity of the outbreak. Findings from this analysis extend the media-hype phenomenon by looking at the triggers for such intense media attention, highlighting the role of not only the news media but public relations practitioners in our hyper health-threatened world.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".