Explaining People’s Worry Levels During the Covid-19 Pandemic: An Analysis of Socio-Economic and Cultural Dimensions
Bibliographic record
Abstract
This paper examines the influence of socio-economic and cultural dimensions (measured at the country level) on what concerns people the most about the Covid-19 pandemic. Based on secondary data, the study considers the opinion of more than 24,000 individuals living in 30 different countries, with national samples weighted to match each country's general population older than 18years of age. A set of linear Bayesian regressions was applied to 10 different types of worries reported for economic, health, and safety domains. Results demonstrate that socio-economic variables and cultural dimensions complement each other in explaining people's concerns about the effect of the Covid-19 pandemic. An overall view of the analysis also reveals that cultural dimensions exceed socio-economic variables in explaining peoples' worries about health and safety domains. Socio-economic variables are slightly more effective in explaining the worries of the economic domain. Among the cultural dimensions, long-term orientation and uncertainty avoidance are the best in explaining people's worries. The higher the score in long-term orientation, the lower the worry levels expressed by the respondents. Likewise, low scores on uncertainty avoidance generate lower levels of worries due to the Covid-19 pandemic. Finally, health worries produce a positive outcome because they explain a significant reduction in the fatality rate.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".