Improvements in Compassion and Fears of Compassion throughout the COVID-19 Pandemic: A Multinational Study
Bibliographic record
Abstract
During large-scale disasters, social support, caring behaviours, and compassion are shown to protect against poor mental health outcomes. This multi-national study aimed to assess the fluctuations in compassion over time during the COVID-19 pandemic. Respondents (Time 1 n = 4156, Time 2 n = 980, Time 3 n = 825) from 23 countries completed online self-report questionnaires measuring the flows of compassion (i.e., Compassionate Engagement and Action Scales) and fears of compassion toward self and others and from others (i.e., Fears of Compassion Scales) and mental health at three time-points during a 10-month period. The results for the flows of compassion showed that self-compassion increased at Time 3. Compassion for others increased at Time 2 and 3 for the general population, but in contrast, it decreased in health professionals, possibly linked to burnout. Compassion from others did not change in Time 2, but it did increase significantly in Time 3. For fears of compassion, fears of self-compassion reduced over time, fears of compassion for others showed more variation, reducing for the general public but increasing for health professionals, whilst fears of compassion from others did not change over time. Health professionals, those with compassion training, older adults, and women showed greater flows of compassion and lower fears of compassion compared with the general population, those without compassion training, younger adults, and men. These findings highlight that, in a period of shared suffering, people from multiple countries and nationalities show a cumulative improvement in compassion and reduction in fears of compassion, suggesting that, when there is intense suffering, people become more compassionate to self and others and less afraid of, and resistant to, compassion.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".