Association Between the Time Spent on and Sources of the News of Russo-Ukrainian War and Psychological Distress Among Individuals in Poland and Ukraine: The Mediating Effect of Rumination
Bibliographic record
Abstract
Background: The Russo-Ukrainian War has resulted in massive social, economic, and psychological burdens worldwide. This study aimed to investigate the associations between time spent on the war-related news and psychological distress, including depression, anxiety, and post-traumatic stress disorder (PTSD) and the mediating effects of rumination on the associations in people residing in Poland and Ukraine. Methods: This cross-sectional study recruited 1438 internet users in Poland and Ukraine, and collected data on levels of rumination, psychological distress, and the amount of time spent on and sources of the news of the Russo-Ukrainian War. Structural equation modeling with bootstrapping methods was used to evaluate the mediation effect. Multivariate linear regression was used to identify predictive effect of the source of the war-related news on psychological distress and rumination. Results: The results showed a mediating effect of rumination on the association between the amount of time spent on the war-related news and psychological distress among participants in Poland (β = 0.16, p < 0.001) and Ukraine (β = 0.15, p < 0.001). Approaching the news from television was associated with rumination (β = 0.607, p < 0.001) and PTSD symptoms in Poland (β = 2.475, p = 0.009), while approaching news from the internet was associated with rumination in Poland (β = 0.616, p = 0.001). Conclusion: The study identified the mediating effect of rumination and the associations of approaching the war-related news from television and the internet with mental health.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".