Interest consistency can buffer the effect of COVID-19 fear on psychological distress
Bibliographic record
Abstract
In the context of a recent outbreak of the coronavirus disease (COVID-19), the present study investigated the buffering effect of grit on the relationship between fear of COVID-19 and psychological distress. The data were collected from 224 Japanese participants (98 females; mean age = 46.56, SD = 13.41) in July 2020. The measures used in this study included the Fear of COVID-19 Scale (FCV-19S), Short Grit Scale, and Depression, Anxiety, and Stress Scale 21 (DASS). The results of mediation analyses revealed significant indirect effects of consistency of interest, a major component of grit, on psychological distress; we also found non-significant indirect effects of perseverance of effort, another major component of grit, on psychological distress. These results suggest that consistency of interest buffers the psychological distress induced by fear of COVID-19. Based on these results, it can be concluded that individuals with higher consistency of interest are less likely to experience worsening of their mental health, even if they experience fear of COVID-19 during 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".