Rumination about the <scp>Russo‐Ukrainian War</scp> and its related factors among individuals in Poland and Ukraine
Bibliographic record
Abstract
The present study aimed to investigate the factors associated with the level of rumination about the war among people living in Poland and Ukraine. This cross-sectional study recruited internet users from advertisements on social media. Levels of rumination, Depression, Anxiety and Stress Scale (DASS), Impact of Event Scale-Revised (IES-R), time spent on news of the war, and related demographic variables were collected. The reliability and construct validity of rumination were estimated. Potential factors associated with the level of rumination were identified using univariate linear regression analysis, and further entered into a stepwise multivariate linear regression model to identify independent factors. Due to the non-normality of distribution, multivariate linear regression with 5000 bootstrap samples was used to verify the results. A total of 1438 participants were included in the analysis, of whom 1053 lived in Poland and 385 lived in Ukraine. The questionnaires on rumination were verified to have satisfactory reliability and validity. After analysis with stepwise and bootstrap regression, older age, female gender, higher DASS and IES-R scores, and longer time spent on news of the war were significantly associated with higher levels of rumination for both people living in Poland and Ukraine. Lower self-rated health status, history of chronic medical illness and coronavirus disease 2019 infection were also positively associated with rumination for people living in Poland. We identified several factors associated with the level of rumination about the Russo-Ukrainian War. Further investigations are warranted to understand how rumination affects individuals' lives during crises such as war.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".