Transition from playing with simulated gambling games to gambling with real money: a longitudinal study in adolescence
Bibliographic record
Abstract
Digital technology advances have supported an expansion of gambling activities, which is notable via the advent of simulated gambling games. Simulated gambling reproduces ‘real’ gambling activities, which enables the users to gamble without investing money. According to research evidence, a certain number of adolescents are playing with these games, but until now little has been known about how they could facilitate the migration to gambling with real money. Using a longitudinal design with a one-year interval period, the goal of this study was to assess the potential transition between playing with simulated gambling and the initiation to gambling with real money. The final sample was constituted of 1220 adolescents (age range = 14 to 18 y.o.) who had never played with real money at the first measurement time. At the second measurement time, 28.8% of the participants had gambled for the first time with real money. Logistic regressions revealed that the predictive association between simulated gambling and gambling with real money only holds for adolescents who transitioned from simulated poker to poker with real money. These findings highlight the need for regulation and monitoring on Internet gambling poker sites, as well as further research to assess the mechanisms at work.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".