Fears of compassion magnify the harmful effects of threat of COVID‐19 on mental health and social safeness across 21 countries
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic is a massive global health crisis with damaging consequences to mental health and social relationships. Exploring factors that may heighten or buffer the risk of mental health problems in this context is thus critical. Whilst compassion may be a protective factor, in contrast fears of compassion increase vulnerability to psychosocial distress and may amplify the impact of the pandemic on mental health. This study explores the magnifying effects of fears of compassion on the impact of perceived threat of COVID-19 on depression, anxiety and stress, and social safeness. METHODS: Adult participants from the general population (N = 4057) were recruited across 21 countries worldwide, and completed self-report measures of perceived threat of COVID-19, fears of compassion (for self, from others, for others), depression, anxiety, stress and social safeness. RESULTS: Perceived threat of COVID-19 predicted increased depression, anxiety and stress. The three flows of fears of compassion predicted higher levels of depression, anxiety and stress and lower social safeness. All fears of compassion moderated (heightened) the impact of perceived threat of COVID-19 on psychological distress. Only fears of compassion from others moderated the effects of likelihood of contracting COVID-19 on social safeness. These effects were consistent across all countries. CONCLUSIONS: Fears of compassion have a universal magnifying effect on the damaging impact of the COVID-19 pandemic on mental health and social safeness. Compassion focused interventions and communications could be implemented to reduce resistances to compassion and promote mental wellbeing during and following the pandemic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".