Internet gambling, substance use, and delinquent behavior: An adolescent deviant behavior involvement pattern.
Bibliographic record
Abstract
Internet gambling among adolescents is a growing phenomenon that has received little attention to date. This study examines associations between Internet gambling and the severity of gambling, substance use (SU), and delinquent behavior among 1,870 Quebec students aged 14 to 18. The results show a higher proportion of Internet-gambling (IG) students reporting problematic substance use and delinquency, compared with nongamblers (NG) and non-Internet gamblers (NIG). Furthermore, a higher proportion of at-risk and probable pathological gamblers are found among IG compared with NIG. A moderating effect (Baron & Kenny, 1986) of the gambler categories (NIG, IG) was found in the relationship between the associated problems and the severity of gambling. Among IG, the severity of delinquency and of substance use contributes to explaining gambling severity whereas, among NIG, the severity of delinquency is the only factor that significantly contributes to such an explanation. Discussion of the results is based on Jessor, Donovan, and Costa's (1991) general deviance syndrome theory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".