Effects of a National Preventive Intervention Against Potential COVID-19–Related Gambling Problems in Online Gamblers: Self-Report Survey Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has been suspected to increase gambling problems in the population. Several governments introduced COVID-19-specific interventions early with the aim to prevent gambling problems, but their effects have not been evaluated. OBJECTIVE: This study aimed to evaluate a Swedish COVID-19-related temporary legislation imposing an automated weekly deposit limit for online casino gambling. METHODS: The study was an anonymous survey sent by a state-owned gambling operator to online gamblers (N=619), among whom 54.0% (n=334) were moderate-risk/problem gamblers who reached the weekly limit on online gambling during the summer of 2020. RESULTS: Overall, 60.1% (372/619) were aware of having been limited by the COVID-19-related deposit limit, and a minority (145/619, 23.4%) perceived the intervention as fairly bad or very bad. Among those aware of the intervention, 38.7% (144/372) believed the intervention decreased their overall gambling, whereas 7.8% (29/372) believed it rather increased it. However, 82.5% (307/372) reported having gambled at more than one operator after the limit, and the most common gambling type reported to have increased at another operator was online casino (42% among moderate-risk/problem gamblers and 19% among others; P<.001). An increase in gambling following the intervention was associated with being a moderate-risk/problem gambler and having negative attitudes toward the intervention. CONCLUSIONS: The weekly deposit limit had relatively high acceptability, but the study highlights the limitations of a single-operator deposit limit, given the high number of gamblers also reporting gambling at other operators and the lower effect in clients with gambling problems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".