The Impact of COVID-19 Pandemic on Distress Intolerance: Among Panic Buyers in Turkey
Bibliographic record
Abstract
In this study, the factors affecting levels of distress intolerance during the Covid-19 pandemic are statistically analyzed among panic buyers in Turkey. Distress intolerance also increased as health status deteriorates. Construct consistency was achieved in measuring distress intolerance during the Covid-19 period. Confirmatory factor analysis (CFA) was performed for participants who engage in panic buying behavior. CFA showed that the reliability and consisteny of this scale was ensured. It was seen that enduring uncomfortable emotions was the condition that affected distress intolerance the most. Doing everything to avoid feeling distressed or sad was found to be the least affecting distress intolerance in the Covid-19 period. When all of the fit criteria were considered, it was evident that the proposed model was valid for sample. Consequently, it is recommended that public health services develop health strategies with respect to the stated risk factors and to provide interventions that increase psychological flexibility to reduce Covid-19 related intolerance to distress.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".