News media coverage of COVID-19 public health and policy information
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.
Bibliographic record
Abstract
Abstract During a pandemic, news media play a crucial role in communicating public health and policy information. Traditional newspaper coverage is important amidst increasing disinformation, yet uncertainties make covering health risks and efforts to limit transmission difficult. This study assesses print and online newspaper coverage of the coronavirus disease COVID-19 for March 2020, when the global pandemic was declared, through August 2020 in three countries: Canada (with the lowest per-capita case and death rates during the study timeframe), the United Kingdom (with a pronounced early spike), and the United States (with persistently high rates). Tools previously validated for pandemic-related news records allow measurement of multiple indicators of scientific quality (i.e., reporting that reflects the state of scientific knowledge) and of sensationalism (i.e., strategies rendering news as more extraordinary than it really is). COVID-19 reporting had moderate scientific quality and low sensationalism across 1331 sampled articles in twelve newspapers spanning the political spectrums of the three countries. Newspapers oriented towards the populist-right had the lowest scientific quality in reporting, combined with very low sensationalism in some cases. Against a backdrop of world-leading disease rates, U.S. newspapers on the political left had more exposing coverage, e.g., focused on policy failures or misinformation, and more warning coverage, e.g., focused on the risks of the disease, compared to U.S. newspapers on the political right. Despite the generally assumed benefits of low sensationalism, pandemic-related coverage with low scientific quality that also failed to alert readers to public-health risks, misinformation, or policy failures may have exacerbated the public-health effects of the disease. Such complexities will likely remain central for both pandemic news media reporting and public-health strategies reliant upon it.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it