Anxiety and watching the war in Ukraine
Bibliographic record
Abstract
On 24 February 2022, Russia attacked Ukraine. Millions of people tuned into social media to watch the war. Media exposure to disasters and large-scale violence can precipitate anxiety resulting in intrusive thoughts. This research investigates factors related to anxiety while watching the war. Since the war began during the ongoing coronavirus pandemic, threat from COVID-19 is seen as a predictor of anxiety when watching the war. A theoretical model is put forward where the outcome was anxiety when watching the war, and predictors were self-reported interference of watching the war with one's studies or work, gender, worry about the war, self-efficacy and coronavirus threat. Data were collected online with independent samples of university students from two European countries close to Ukraine, Germany (n = 348) and Finland (n = 228), who filled out an anonymous questionnaire. Path analysis was used to analyse the data. Findings showed that the model was an acceptable fit to the data in each sample, and standardised regression coefficients indicated that anxiety, when watching the war, increased with interference, war worry and coronavirus threat, and decreased with self-efficacy. Women reported more anxiety when watching the war than men. Implications of the results are discussed.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".