Changes in sports gambling behavior during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
Sports betting is one of the most popular forms of gambling in Canada; recent prevalence estimates indicate that 7.9% of Canadian adults endorsed gambling on sports in the past year. The ongoing COVID-19 pandemic led to the temporary closure of most major sports leagues worldwide beginning in March of 2020. These sudden closures created a dramatic decrease in the availability of sports betting opportunities in the early stages of the pandemic, followed by a subsequent increase in availability as most sport leagues returned during the summer of 2020. Using a retrospective self-report measure of gambling participation, the present study investigated how the gambling behaviors of N = 85 past-year sports gamblers changed over the course of the pandemic. It was hypothesized that sports gamblers would report an initial decrease in gambling behaviors from pre-pandemic baseline levels to the early stages of the pandemic in May of 2020 when the availability of sports gambling was heavily restricted, followed by an increase in gambling behaviors from May to August, in accordance with the re-emergence of live sporting events. The general pattern of results supported the hypotheses, though gambling behaviors did not completely return to baseline levels. Beyond quantifying the changes in gambling behaviors over the early stages of the pandemic in Canada, results may have implications regarding the utility of voluntary gambling exclusion programs as well as legislation concerning gambling access.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".