Potentially addictive behaviours increase during the first six months of the Covid-19 pandemic
Bibliographic record
Abstract
Background and aims: In this study we aimed to assess multiple potentially addictive behaviours simultaneously for an extended period of time during the Covid-19 pandemic and their relation to distress. Methods: Data were collected every three days from Amazon's MTurk between 26.03.2020 and 02.10.2020 in repeated cross-sectional samples of 25 participants resulting in a total sample of 1430 US adults (60% men, mean age 36.6 years, SD = 11). General distress and Covid-19 related fear were assessed as well as self-reported frequency of eight potentially addictive behaviours: shopping (compulsive buying), alcohol, smoking, legal substances, illegal substances, gambling, gaming and overeating. Results: We found a positive relationship between time and the frequency of each self-reported potentially addictive behaviour ( τ = 0.15-0.23, all P < 0.001), and their frequency is linearly related to the intensity of (Covid-19-related and general) distress ( τ = 0.12-0.28, all P < 0.001). Most popular activities were gaming and compulsive buying, and the relative frequency of the behaviours remained about the same during the data collection period. Discussion: It is possible that people seek other maladaptive substitutes when other coping mechanisms (e.g. social recreation) are hindered depending on their level of distress. Conclusion: Given the evidence for the increasing frequency of potentially addictive behaviours and their relevance to distress, special attention needs to be paid to reduce potential harmful effects of maladaptive coping during and after this demanding period.
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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 | 0.003 |
| 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 | 0.001 |
| 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".