SARS Wars: An Examination of the Quantity and Construction of Health Information in the News Media
Bibliographic record
Abstract
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.
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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 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".