Cross-lagged links among gambling, substance use, and delinquency from midadolescence to young adulthood: Additive and moderating effects of common risk factors.
Bibliographic record
Abstract
The authors examined cross-lagged links among gambling, substance use, theft, and violence from midadolescence to young adulthood and whether behavioral disinhibition, deviant peers, and parental supervision as common risk factors explain or moderate those links. In 2 community samples, male Caucasians were assessed for gambling participation and problems with the South Oaks Gambling Screen-Revised for Adolescents (K. C. Winters, R. Stinchfield, & J. Fulkerson, 1993) at age 16 years and the South Oaks Gambling Screen (H. R. Lesieur & S. B. Blume, 1987) at age 23. Other problem behaviors were also assessed both times. Risk factors were measured at age 16. Adolescent substance use was related to subsequent theft and violence but not gambling. Gambling problems were linked to subsequent gambling participation. For adolescents with deviant peers, gambling problems were linked to subsequent theft; this was not the case for adolescents without deviant peers. Only for individuals high on disinhibition did stability of gambling problems resemble moderate stabilities of other problem behaviors. Each risk factor was related to each problem behavior (exception: parenting unrelated to gambling). These risk factors partly explained the cross-lagged links among behaviors and thus may be useful targets of prevention. (PsycINFO Database Record (c) 2009 APA, all rights reserved).
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".