Empathy, Fear of Disease and Support for COVID-19 Containment Behaviors: Evidence from 34 Countries on the Moderating Role of Governmental Trust
Bibliographic record
Abstract
Abstract The current study investigated the motives that underlie support for COVID-19 preventive behaviorsin a large, cross-cultural sample of 12,758 individuals from 34 countries. We hypothesized that the associations of empathic prosocial concern and fear of disease, with support towards preventive COVID-19 behaviors would be moderated by the individual-level and country-level trust in the government. Results suggest that the association between fear of disease and support for COVID-19 preventive behaviors was strongest when trust in the government was weak (both at individual and country-level). Conversely, the association with empathic prosocial concern was strongest when trust was high, but this moderation was only found at individual-level scores of governmental trust. We discuss how both fear and empathy motivations to support preventive COVID-19 behaviors may be shaped by socio-cultural context, and outline how the present findings may contribute to a better understanding of collective action during global crises.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".