Environmental factors selectively impact co‐occurrence of problem/pathological gambling with specific drug‐use disorders in male twins
Bibliographic record
Abstract
AIMS: Multiple forms of drug abuse/dependence frequently co-occur with problem/pathological gambling (PPG). The current study examines the extent to which genetic and environmental factors contribute to their co-occurrence. DESIGN: Bivariate models investigated the magnitude and correlation of genetic and environmental contributions to problem/pathological gambling and its co-occurrence with nicotine dependence, cannabis abuse/dependence and stimulant abuse/dependence. SETTING: Computer-assisted telephone interviews in the community. PARTICIPANTS: Participants were 7869 male twins in the Vietnam Era Twin Registry, a USA-based national twin registry. MEASUREMENTS: Life-time DSM-III-R diagnoses for problem/pathological gambling, nicotine dependence, cannabis abuse/dependence and stimulant abuse/dependence were determined using the Diagnostic Interview Schedule. FINDINGS: All drug-use disorders displayed additive genetic and non-shared environmental contributions, with cannabis abuse/dependence also displaying shared environmental contributions. Both genetic [genetic correlation rA = 0.22; 95% confidence interval (CI) = 0.10-0.34] and non-shared environmental components (environmental correlation rE = 0.24; 95% CI = 0.10-0.37) contributed to the co-occurrence of problem/pathological gambling and nicotine dependence. This pattern was shared by cannabis abuse/dependence (rA = 0.32; 95% CI = 0.05-1.0; rE = 0.36; 95% CI = 0.16-0.55) but not stimulant abuse/dependence (SAD), which showed only genetic contributions to the co-occurrence with problem/pathological gambling (rA = 0.58; 95% CI = 0.45-0.73). CONCLUSIONS: Strong links between gambling and stimulant-use disorders may relate to the neurochemical properties of stimulants or the illicit nature of using 'hard' drugs such as cocaine. The greater contribution of environmental factors to the co-occurrence between problem/pathological gambling and 'softer' forms of drug abuse/dependence (cannabis, tobacco) suggest that environmental interventions (perhaps relating to availability and legality) may help to diminish the relationship between problem/pathological gambling and tobacco- and cannabis-use disorders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".