Exposure to the COVID-19 news on social media and consequent psychological distress and potential behavioral change
Bibliographic record
Abstract
Exposure to coronavirus disease 2019 (COVID-19) news pandemic is inevitable. This study aimed to explore the association between exposure to COVID-19 news on social media and feeling of anxiety, fear, and potential opportunities for behavioral change among Iranians. A telephone-based survey was carried out in 2020. Adults aged 18 years and above were randomly selected. A self-designed questionnaire was administered to collect information on demographic variables and questions to address exposure to news and psychological and behavioral responses regarding COVID-19. A multivariate logistic regression analysis was performed to assess the relationship between anxiety, fear, behavioral responses, and independent variables, including exposure to news. In all, 1563 adults participated in the study. The mean age of respondents was 39.17 ± 13.5 years. Almost 55% of participants reported moderate to high-level anxiety, while fear of being affected by COVID-19 was reported 54.1%. Overall 88% reported that they had changed their behaviors to some extent. Exposure to the COVID-19 news on social media was the most influencing variable on anxiety (OR 2.21, 95% CI 1.62-3.04; P < 0.0001), fear (OR 1.95, 95% CI 1.49-2.56; P < 0.0001), and change in health behaviors (OR 2.02, 95% CI 1.28-3.19; P = 0.003) in the regression model. The fear of being infected by the COVID19 was associated with the female gender and some socioeconomic characteristics. Although exposure to the COVID-19 news on social media seemed to be associated with excess anxiety and fear, it also, to some extent, had positively changed people's health behaviors towards preventive measures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".