Frequency and method of seeking for information about COVID-19 and its relationship with psychological symptoms and stress levels
Bibliographic record
Abstract
Elevated search for information could increase rumors and misinformation, which significantly impacted the daily lives and mental health of individuals. We tested the association between the frequency and methods of communication used and psychological symptoms. Cross-sectional study that included individuals with COVID-19 and individuals without the disease. Participants completed a questionnaire about the frequency with which were informed about COVID-19. The severity of depressive and anxious symptoms, and stress levels were assessed. The sample included 350 individuals (66% female, mean age 38.09 ± 14.18 years), and 32.6% had a confirmed COVID-19 diagnosis. Most of the sample was informed about COVID-19 almost always or always and the most common method used to search for information was the internet, followed by open TV, social media, WhatsApp, cable TV, radio, and newspaper. Individuals who sought information on social media had greater severity of depressive symptoms when compared to subjects who did not seek information on the media. Individuals who sought information via WhatsApp had lower anxiety symptoms and stress levels when compared to individuals who did not seek information via WhatsApp. The search for information had a negative impact on depressive symptoms and a decrease in anxiety symptoms.
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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 | 0.005 |
| 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 | 0.000 |
| 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".