Predictors of Anxiety in the COVID-19 Pandemic from a Global Perspective: Data from 23 Countries
Bibliographic record
Abstract
Prior and ongoing COVID-19 pandemic restrictions have resulted in substantial changes to everyday life. The pandemic and measures of its control affect mental health negatively. Self-reported data from 15,375 participants from 23 countries were collected from May to August 2020 during the early phases of the COVID-19 pandemic. Two questionnaires measuring anxiety level were used in this study—the Generalized Anxiety Disorder Scale (GAD-7), and the State Anxiety Inventory (SAI). The associations between a set of social indicators on anxiety during COVID-19 (e.g., sex, age, country, live alone) were tested as well. Self-reported anxiety during the first wave of the COVID-19 pandemic varied across countries, with the maximum levels reported for Brazil, Canada, Italy, Iraq and the USA. Sex differences of anxiety levels during COVID-19 were also examined, and results showed women reported higher levels of anxiety compared to men. Overall, our results demonstrated that the self-reported symptoms of anxiety were higher compared to those reported in general before pandemic. We conclude that such cultural dimensions as individualism/collectivism, power distance and looseness/tightness may function as protective adaptive mechanisms against the development of anxiety disorders in a pandemic situation.
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 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 | 0.002 |
| 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".