Online gambling during the COVID-19 pandemic in Canada: A mixed-method study
Bibliographic record
Abstract
ABSTRACT\nThe gambling industry has been severely impacted by the health crisis. In several countries, lockdowns have resulted in a migration to online gambling, the changing of game offering and player habits. From a public health perspective, one of the issues related to online gambling is the harms associated with them. Research questions. This study aims to provide a portray of online gambling habits in the context of the COVID-19 pandemic, namely the changes in gambling patterns and the factors contributing to gambling problems. Method. The study included two subsamples of adults aged 18 and over residing in the province of Québec (Canada): 1) a telephone survey conducted with a random sample (N=1,300) using a two-stage proportional stratified sampling design (households, individuals), 2) a sample recruited through a web panel (N=3,200), and 3) qualitative interview data conducted with 98 online gamblers. Results. The analysis revealed an increase in gambling participation and initiation during the pandemic. The various profiles of online gamblers are contrasted on harm, mental health and the perceived impact of the pandemic on gambling patterns and problems.\nIMPLICATION STATEMENTS\nThis study will provide one of the first population portray of gambling habits among a representative sample of online gamblers as well as the lived experience of gamblers during the first year of the COVID-19 pandemic. The findings will inform key stakeholders and raise awareness about the harm associated with online gambling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".