A portrait of online gambling: a look at a transformation amid a pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic brought about an extraordinary societal context in which the gambling offer was modified to meet public health measures intended to curb viral transmission. With many land-based gambling venues being forced to close, gambling opportunities were left almost exclusively to the online domain, thus possibly instigating changes in the population's online gambling habits. Using a sequential mixed methods design, this study aimed to (1) investigate the self-reported changes in gambling habits of adults in the province of Québec (Canada) following the declaration of the COVID-19 pandemic and ensuing public health responses, and (2) report on their lived experiences of these changes during the first year of the pandemic. METHOD: A population survey was conducted with a representative sample of 4,676 online gamblers residing in the province of Québec, which was selected through random digit dialing for telephone interviews and from a web panel. From the initial sample, 96 online gamblers were recruited for in-depth semi-structured interviews inquiring about their gambling experiences during the first year of the pandemic. RESULTS: The prevalence of online gambling was estimated at 15.6-20.3% of Québec's population in 2021, among which 5.6% gambled online for the first time during the pandemic, which represented a substantial addition to the 14.7% of people who gambled online both before and during the pandemic. Only 1.4% of people quit online gambling during the pandemic. The impact of the pandemic was similar for frequency, expenditure, and time spent on various online gambling activities, with day trading having increased most during the pandemic. Seeking to earn money was one of several motivations endorsed by participants who had begun or increased online gambling practices during the first year of the pandemic. CONCLUSION: The COVID-19 pandemic clearly revealed a significant increase in online gambling practices when changes in the gambling landscape and in daily life occurred due to the health crisis. This calls for a greater attention to the need for comprehensive regulatory measures and a support system for online gambling in a context of a steadily increasing lucrative market.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".