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Record W1987662731 · doi:10.1080/10410230701283322

SARS Wars: An Examination of the Quantity and Construction of Health Information in the News Media

2007· article· en· W1987662731 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHealth Communication · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsUniversity of VictoriaWilfrid Laurier University
FundersWilfrid Laurier University
KeywordsPersuasionHealth communicationElaboration likelihood modelContext (archaeology)PsychologySocial mediaPublic healthPerceptionAdvertisingPublicationThe InternetHealth riskNews mediaSocial psychologyComputer scienceMedicineEnvironmental healthWorld Wide WebBusiness

Abstract

fetched live from OpenAlex

The media have the power to sway public perception of health issues by choosing what to publish and the context in which to present information. The media may influence an individual's tendency to overestimate the risk of some health issues while underestimating the risk of others, ultimately influencing health choices. Although some research has been conducted to examine the number of articles on selected health topics, little research has examined how the messages are constructed. The purpose of this article is to describe an examination of the construction of news reports on health topics using aspects of the social amplification of risk model and the elaboration likelihood model of persuasion for theoretical direction. One hundred news media reports (print, radio, television, and Internet) were analyzed in terms of message repetition, context, source, and grammar. Results showed that health topics were more often discussed in terms of risk, by credible sources using strong language. This content analysis provides an empirical starting point for future research into how such health news may influence consumer's perceptions of health topics.

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.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.076
GPT teacher head0.400
Teacher spread0.324 · 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