Are Online Gamblers More At Risk Than Offline Gamblers?
Bibliographic record
Abstract
OBJECTIVES: To characterize and compare sociodemographic profiles, game-play patterns, and level of addictive behaviors among adults who gamble online and those who do not, and to examine if, at the population level, online gambling is associated with more risky behaviors than offline gambling. METHODS: Respondents were 8,456 offline gamblers and 111 online gamblers who participated in a population-based survey conducted in the province of Québec, in 2009. The study sample is representative of adult general population. RESULTS: There is an unequal distribution of online gambling in the population. A disproportionate number of men, young people, and students say they participate in online gambling. Poker players are overrepresented among online gamblers and gambling behaviors tend to be more excessive on the Internet. Compared with offline gamblers, online gamblers report more co-occurring risky behaviors, namely alcohol and cannabis use. CONCLUSION: Those who gamble online appear to be more at risk for gambling-related problems, but the present findings alone cannot be used as evidence for that conclusion. Future research designs could combine longitudinal data collection and multilevel analyses to provide more insight into the causal mechanisms associated with online gambling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".