Sports betting and eSports betting: Same risk factors?
Bibliographic record
Abstract
Various events in recent years have probably had a significant influence on gambling habits. From the pandemic with its many confinements to the amendment of the Sports Betting Act (S.C. 2021, c.20) which came into effect in 2021, the gambling environment in Canada has changed. While early studies point to an increase in gambling patterns during the COVID, what about more specific and lesser known groups such as those betting on eSports and those betting on sports? This study is part of a longitudinal e-Gaming study in which nearly 4,000 participants from a web panel completed an online questionnaire. Among the 550 sports bettors, 35.1% increased their gambling frequency during the pandemic, 37.4% increased their spending and 41.1% increased the time spent on sports betting. For their part, among the 303 eSports bettors, 49.3% increased their spending and 51.1% increased the time and frequency of these bets during the pandemic. In addition, 51.5% of eSports bettors have an PGSI score that classifies them as problem gamblers (8+) compared to 30.7% of sports bettors. This presentation will further explore the risk factors for these two groups in order to inform preventive interventions for these relatively unknown groups.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".