The Associations Between Sources of Vaccine News and the Intention to Change Adherence to COVID-19 Preventive Health Measures
Bibliographic record
Abstract
Background: Although the scientific literature has extensively discussed the impact of the media on people’s health-related behaviors, there is little evidence of the effect of different sources of COVID-19 vaccine news on changing the intention to adhere to health protocols. Therefore, the present study was conducted to investigate the sources of news on the COVID-19 vaccine and the association of each of these sources with the intention of changing adherence to COVID-19 preventive health measures (ICA-COVID-19-PHM). Methods: This cross-sectional study was conducted on 1000 public population (67.4% female and 32.6% male) of Mazandaran province (age range: 18 to 60 years). The data were collected between January and April 2021 by completing an unidentified online “Google form” questionnaire via social media platforms, such as Telegram, WhatsApp, Soroush, ETA, and Instagram. The two-level linear regression analysis was used to examine the association between the sources of news on the COVID-19 vaccine and the ICA-COVID-19-PHM (social and personal aspects). Results: The most common sources of receiving news were mass media (radio and television with 54%) and virtual networks with 49%. The results showed the ICA-COVID-19-PHM for the news source via virtual workgroups was positive (B=1.36; 95% CI, 0.31%, 2.41%; P=0.01) and for the news sources via virtual networks (B=-0.83; 95% CI, -1.62%, -0.05%; P=0.04) and satellites and foreign news agencies (B=-1.50; 95% CI, -2.64%, -0.36%; P=0.01) was negative. While, the ICA-COVID-19-PHM was not significant (P>0.05) for other sources, such as groups of friends and neighbors, newspapers and magazines, radio and television, and news websites. Conclusion: The management of news sources in epidemics is important because they have associations with adherence to preventive health measures. Policymakers should consider the distribution of vaccine news sources on the ICA-COVID-19-PHM.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".