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Record W2615459621

Finding Hope in Media Hype: The Challenges of Crisis Communications During Disease Outbreak

2017· article· en· W2615459621 on OpenAlexaboutno aff
Shelley Aylesworth-Spink

Bibliographic record

VenueWestminsterResearch (University of Westminster) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsnot available
Fundersnot available
KeywordsCrisis communicationPandemicPublic healthOutbreakNews mediaPolitical sciencePublic relationsPlague (disease)JournalismHealth communicationMedia studiesCoronavirus disease 2019 (COVID-19)DiseaseHistorySociologyMedicineInfectious disease (medical specialty)Virology
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.121
GPT teacher head0.355
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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