Changes in Self-Reported Web-Based Gambling Activity During the COVID-19 Pandemic: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The COVID‑19 pandemic has affected not only somatic health with over 3.7 million deaths worldwide, but also has had a huge impact on psychological health, creating what amounts to a mental health crisis. The negative effect of the pandemic on traditional addictions is well described and concerning, and the same has been seen for gambling. OBJECTIVE: This study explores self-reported web-based gambling behavior during the COVID‑19 pandemic in Sweden. We investigated overall changes, but also changes in specific web-based gambling types, and whether they are associated with certain risk factors or lifestyle changes. METHODS: Our study is based on an anonymous web-based survey of web panel participants in Sweden (N=1501) designed to study a range of behavioral changes during the COVID‑19 pandemic. Increases in gambling were analyzed using logistic regression models against sociodemographic data and psychological distress. RESULTS: The majority of the respondents who gambled reported no changes in their gambling habits during the COVID‑19 pandemic. We found significant associations with the problem gambling severity index (PGSI), the Kessler score (indicating psychological distress), employment status, changes in alcohol habits, and self-exclusion when looking at overall changes in gambling activity in the pandemic. In the subgroup that reported an increase in gambling activity, we found an association with both the PGSI and Kessler scores. The PGSI score was also an independent predictor for all specific web-based gambling (horses, sports, poker, and casino) whereas the Kessler score only had a significant impact on changes in casino gambling. In addition, male gender was an independent predictor for gambling on sports and casino gambling. CONCLUSIONS: The majority of respondents who gambled reported no changes in their gambling activity during the COVID‑19 pandemic. The group that reported an increase in overall gambling activity during the COVID-19 pandemic represent a group with gambling problems and psychological distress. The group that reported increased sports and casino gambling were often male, and this group seemed to experience more psychological 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".