Factors Associated With Highest Symptoms of Anxiety During COVID-19: Cross-Cultural Study of 23 Countries
Bibliographic record
Abstract
The COVID-19 restrictions have impacted people's lifestyles in all spheres (social, psychological, political, economic, and others). This study explored which factors affected the level of anxiety during the time of the first wave of COVID-19 and subsequent quarantine in a substantial proportion of 23 countries, included in this study. The data was collected from May to August 2020 (5 June 2020). The sample included 15,375 participants from 23 countries: (seven from Europe: Belarus, Bulgaria, Croatia, Hungary, Italy, Romania, Russia; 11 from West, South and Southeast Asia: Armenia, India, Indonesia, Iran, Iraq, Jordan, Malaysia, Pakistan, Saudi Arabia, Thailand, Turkey; two African: Nigeria and Tanzania; and three from North, South, and Central America: Brazil, Canada, United States). Level of anxiety was measured by means of the 7-item Generalized Anxiety Disorder Scale (GAD-7) and the 20-item first part of The State-Trait Anxiety Inventory (STAI)-State Anxiety Inventory (SAI). Respondents were also asked about their personal experiences with COVID-19, attitudes toward measures introduced by governments, changes in attitudes toward migrants during a pandemic, family income, isolation conditions, etc. The factor analysis revealed that four factors explained 45.08% of variance in increase of anxiety, and these components were interpreted as follows: (1) personal awareness of the threat of COVID-19, (2) personal reaction toward officially undertaken measures and attitudes to foreigners, (3) personal trust in official sources, (4) personal experience with COVID-19. Three out of four factors demonstrated strong associations with both scales of anxiety: high level of anxiety was significantly correlated with high level of personal awareness of the threat of COVID-19, low level of personal reaction toward officially undertaken measures and attitudes to foreigners, and high level of presence of personal experience with COVID-19. Our study revealed significant main effects of sex, country, and all four factors on the level of anxiety. It was demonstrated that countries with higher levels of anxiety assessed the real danger of a pandemic as higher, and had more personal experience with COVID-19. Respondents who trusted the government demonstrated lower levels of anxiety. Finally, foreigners were perceived as the cause of epidemic spread.
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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 | 0.001 |
| 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 | 0.001 |
| 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".