The impact of COVID-19 on gambling and gambling disorder: emerging data
Bibliographic record
Abstract
PURPOSE OF REVIEW: The lockdown response to the COVID-19 pandemic has significantly impacted commercial gambling in many jurisdictions around the world. The goal of this review is to systematically identify and describe the survey data and findings to date examining the effect on individual gambling and gambling disorder. RECENT FINDINGS: Of the 17 publications meeting inclusion criteria, the majority reported cross-sectional assessments (n = 11, 65%) and remainder were longitudinal in that they had earlier gambling data for participants (n = 6, 35%). Not surprisingly given the closure of land-based gambling, an overall reduction in gambling frequency and expenditure was reported in all studies. The estimate of the proportion of participants in both the general population and the population that gambles who increased overall gambling or online gambling was variable. The most consistent correlates of increased gambling during the lockdown were increased problem gambling severity, younger age groups, and being male. SUMMARY: These results suggest that the impacts of the COVID-19 pandemic on gambling and problematic gambling are diverse - possibly causing a reduction in current or future problems in some, but also promoting increased problematic gambling in others. The longer-term implications of both the reduction in overall gambling, and the increase in some vulnerable groups are unclear, and requires assessment in subsequent follow-up studies. However, in the short term, individuals with existing gambling problems should be recognized as a vulnerable group.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".