How culturally unique are pandemic effects? Evaluating cultural similarities and differences in effects of age, biological sex, and political beliefs on COVID impacts
Bibliographic record
Abstract
Despite being bio-epidemiological phenomena, the causes and effects of pandemics are culturally influenced in ways that go beyond national boundaries. However, they are often studied in isolated pockets, and this fact makes it difficult to parse the unique influence of specific cultural psychologies. To help fill in this gap, the present study applies existing cultural theories via linear mixed modeling to test the influence of unique cultural factors in a multi-national sample (that moves beyond Western nations) on the effects of age, biological sex, and political beliefs on pandemic outcomes that include adverse financial impacts, adverse resource impacts, adverse psychological impacts, and the health impacts of COVID. Our study spanned 19 nations (participant N = 14,133) and involved translations into 9 languages. Linear mixed models revealed similarities across cultures, with both young persons and women reporting worse outcomes from COVID across the multi-national sample. However, these effects were generally qualified by culture-specific variance, and overall more evidence emerged for effects unique to each culture than effects similar across cultures. Follow-up analyses suggested this cultural variability was consistent with models of pre-existing inequalities and socioecological stressors exacerbating the effects of the pandemic. Collectively, this evidence highlights the importance of developing culturally flexible models for understanding the cross-cultural nature of pandemic psychology beyond typical WEIRD approaches.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".