The comorbidity of tobacco smoking and gambling: A review of the literature
Bibliographic record
Abstract
ISSUES: Evidence suggests that tobacco smoking and gambling frequently co-occur. Although high rates of comorbid smoking and gambling have been documented in studies with clinical populations of pathological gamblers in treatment, in studies using samples drawn from the community, and in large-epidemiological surveys, little empirical attention has been directed towards investigating the exact nature of this relationship. APPROACH: In this review, we stress the literature that has examined the epidemiology, aetiology and environmental factors implicated in comorbid smoking and gambling. Publications included in the review were identified through PsycInfo, PubMed and Medline searches. KEY FINDINGS: Although conclusive evidence is lacking, a growing body of literature suggests that smoking and gambling might share similar neurobiological, genetic and/or common environmental influences. IMPLICATIONS: Comorbid tobacco smoking and gambling are highly prevalent at the event and syndrome levels. However, research investigating how smoking might affect gambling or vice versa is currently lacking. CONCLUSION: More studies that examine the impact of this comorbidity on rates of tobacco dependence and problem gambling, as well as implications for treatment outcomes, are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".