Compulsive buying gradually increased during the first six months of the Covid-19 outbreak
Bibliographic record
Abstract
Background and aims: The current Covid-19 situation offers a natural experiment to explore the effect of a chronic stressor on compulsive buying tendencies over an extended period of time. Design: Survey method of sampling every three days a new cohort during the first six months of the Covid-19 pandemic (March-October 2020) in the United States. Participants: Total (clean) sample of N = 1,430 (39.3% female, mean age = 36.4 years). Measurements: Online and offline compulsive buying separately, distress, economic position, income and age were assessed. Findings: Both online and offline compulsive buying increased during the data collection period ( τ = 0.24, τ = 0.22, respectively, both P < 0.001). Individuals with self-reported high economic position (EP) reported the highest tendency for compulsive buying throughout the entire time frame, although the increase in compulsive buying tendencies over time was the most pronounced among the economically less privileged. Online compulsive buying increased after the CARES Act (first stimulus package) by an effect size of d = 0.33. When entered into a regression model, EP had the strongest effect on compulsive buying after accounting for the effect of distress, income and age. The high-EP group reported the strongest correlation between distress and compulsive buying (r = 0.67, P < 0.001, 95% CI: 0.57-0.76). Conclusions: Compulsive buying tendency gradually increased during the first six months of the Covid-19 pandemic especially after the CARES Act.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".