Compulsive buying gradually increased during the first six months of the Covid-19 outbreak
Why this work is in the frame
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Bibliographic record
Abstract
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 = 1430 (39.3% female, mean age = 36.4 years). Measurements : Online and offline compulsive buying separately, distress, SES, 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). High-SES individuals reported the highest tendency for compulsive buying throughout the entire time frame, although the increase in compulsive buying tendencies over time was the highest among the socioeconomically less privileged. Online compulsive buying increased as a result of the CARES Act (first stimulus package) by an effect size of d = 0.33. When entered into a regression model, SES had the strongest effect on compulsive buying after accounting for the effect of distress, income and age (online: ß PSS = 1.3***, ß SES = 5.13***, ß income = 2.6***, ß age = -0.20*, F(4, 709) = 53.01, R 2 = 0.23, RSE = 29.5; offline: ß PSS = 1.45***, ß SES = 4.86***, ß income = 2.16***, ß age = -0.08 p > 0.4; F(4, 695) = 49.54, R 2 = 0.22, RSE = 30.41). The high-income 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 as a result of the CARES Act.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it