EPA-0134 – Video gaming and gambling: an exploratory study in an adolescent french population
Bibliographic record
Abstract
The notion of gambling fascinates the scientific community for almost thirty years to become today a major concern for health policy. It is a well-known fact that lack of control and excessive gambling can have deleterious effects on the individual. The aim of this study was to compare the use of video games and gambling in a population of 5568 adolescents and young adults in schools, colleges and universities of France. A majority of the sample was female (61.2% vs 38.8% male). We note that 43.4% of the sample is less than 18 years old. We evaluated personality dimensions (anxiety, depression, self-esteem, impulsivity), socio-cognitive variables (cognitive distorsions, life satisfaction) and habits (substance use). Our results showed that 6% of children and 3,5% of adults were found to be at risk of gambling according to the Canadian Pathological Gambling Index (CPGI). Correlations between scores on gambling and problematic video game use were 0.29 for children and 0.44 for adults (significant at p 0.05). According to regression analysis, the predictors of problematic use of video games were depression, incapacity to stop (facet of impulsivity) and the score on the CPGI. For pathological gambling, the predictors were: interpretive bias (dimension of cognitive distorsion), substance use (tobacco and cannabis), facets of impulsivity of the UPPS (incapacity to stop, positive urgency and lack of premeditation) and the total score for the problematic use of video game. These results are interesting in the adaptation of care for people with gambling problem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".