Toxic cocktail of media and politics: an emerging challenge in public health
Bibliographic record
Abstract
BACKGROUND AND AIM: Recent trends in a complex combination of media and political interference have become potentially detrimental to spreading the correct public health information and eventually influencing public opinion and behavior in negative ways. This presentation aims to present data on the impact political views, media consumption, and COVID-19 exposure has on prevention behavior in the United States. METHOD: A survey (n=258) assessing demographics, political ideology, COVID-19 exposure, and mask adherence was distributed throughout various social media platforms from April - May 2021. STATA was used to conduct chi-squared tests, spearman rank correlations, and regression analyses. RESULTS: Findings suggest that political ideals and media consumption impact COVID-19 prevention behavior in the US, particularly significant associations between media linked with political ideology. For example, people who reported getting news from Facebook (p 0.01) or Fox News (p 0.01), both reported more by Republicans (Facebook and Fox News p 0.01), were less likely to wear their mask “always.” Additionally, those with high conservativism scores were less likely to wear masks. CONCLUSIONS: The interactions between politicized and polarized media, political ideals, and COVID-19 prevention behavior highlights the importance of unbiased media and unified federal responses to significant health crises like the COVID-19 pandemic. Similarly, polarized and biased media may influence public policy responses for other public health disasters, like climate crises and mitigation (such as promotion of renewables, carbon tax).
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 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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".