Cross-Cultural Perspectives on the Role of Empathy during COVID-19’s First Wave
Bibliographic record
Abstract
The COVID-19 pandemic has spread throughout the world, and concerns about psychological, social, and economic consequences are growing rapidly. Individuals’ empathy-based reactions towards others may be an important resilience factor in the face of COVID-19. Self-report data from 15,375 participants across 23 countries were collected from May to August 2020 during the early phases of the COVID-19 pandemic. In particular, this study examined different facets of empathy—Perspective-Taking, Empathic Concern, and Personal Distress, and their association with cross-cultural ratings on Individualism, Power Distance, The Human Development Index, Social Support Ranking, and the Infectious Disease Vulnerability Index, as well as the currently confirmed number of cases of COVID-19 at the time of data collection. The highest ratings on Perspective-Taking were obtained for USA, Brazil, Italy, Croatia, and Armenia (from maximum to minimum); on Empathetic Concern, for the USA, Brazil, Hungary, Italy, and Indonesia; and on Personal Distress, from Brazil, Turkey, Italy, Armenia, Indonesia. Results also present associations between demographic factors and empathy across countries. Limitations and future directions are presented.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".