Suppression May Improve Adaptation to Worry When Facing Uncertainty: Studying COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has been associated with increased uncertainty, fear and worry in everyone's life. The effect of changes in daily life has been studied widely, but we do not know how emotion-regulation strategies influence adaptation to a new situation to help them overcome worry in the face of uncertainty. Here, 1,064 self-selected Farsi speaking participants completed an online battery of questionnaires that measured fear of virus and illness, worry, intolerance of uncertainty, and emotion regulation (two subscales: reappraisal, suppression). We also documented the number of daily COVID-19 cases and deaths due to COVID-19 on the day in which participants completed the questionnaire. Our findings suggest a correlation between contamination fear and the number of daily-confirmed cases (r = 0.11), and the number of reported deaths due to COVID-19 (r = 0.09). Worry mediated the relationship between intolerance of uncertainty and fear of virus and illness (b = 0.16, 0.1141 < CI < 0.2113). In addition, suppression moderated the relationship between intolerance of uncertainty and worry (p < 0.01). Our results suggest that suppression (at least in the short term) can be an adaptive response to the worry associated with uncertainty. Suppression can reduce worry, which in turn can decrease fear of contamination and improve adaptation to social distancing requirements. Although, the observed correlations were significant, but considering the sample size, they are not strong, and they should be interpreted cautiously.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".