The Mental Health Impact of Daily News Exposure During the COVID-19 Pandemic: Ecological Momentary Assessment Study
Bibliographic record
Abstract
BACKGROUND: Consumption of distressing news media, which substantially increased during the COVID-19 pandemic, has demonstrable negative effects on mental health. OBJECTIVE: This study examines the proximal impact of daily exposure to news about COVID-19 on mental health in the first year of the pandemic. METHODS: A sample of 546 college students completed daily ecological momentary assessments (EMAs) for 8 weeks, measuring exposure to news about COVID-19, worry and optimism specifically related to COVID-19, hopelessness, and general worry. RESULTS: Participants completed >80,000 surveys. Multilevel mediation models indicated that greater daily exposure to news about COVID-19 is associated with higher same-day and next-day worry about the pandemic. Elevations in worry specifically about COVID-19 were in turn associated with greater next-day hopelessness and general worry. Optimism about COVID-19 mediated the relationship between daily exposure to COVID-19 news and next-day general worry but was not related to hopelessness. CONCLUSIONS: This study demonstrates the mental health impact of daily exposure to COVID-19 news and highlights how worry about the pandemic contributes over time to hopelessness and general worry.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".