Tobacco use and impulsivity in people with mental illness: A systematic review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Impulsivity is a risk factor for the development and maintenance of tobacco use, especially among individuals with comorbid mental illness, but the nature of this relationship is poorly understood. We systematically examined evidence for the relationship between impulsivity and tobacco use in people with psychiatric disorders. METHODS: Following PRISMA guidelines, original peer-reviewed articles published from database inception to July 2024 were searched for using PubMed, Google Scholar, ProQuest, Ovid, and PsycINFO. RESULTS: Of 1192 articles identified, 16 met the inclusion criteria. There was consistent evidence of a positive relationship between tobacco use and impulsivity in schizophrenia (SZ), major depressive disorder (MDD) and bipolar disorder (BD). However, the majority of studies were cross-sectional studies, preventing causal inferences. DISCUSSION AND CONCLUSIONS: Tobacco smoking is robustly associated with impulsivity in people with SZ, MDD, and BD, although causal conclusions cannot be drawn due to methodological limitations of the current literature. Future prospective and experimental studies are needed to ascertain whether impulsivity is a risk factor for smoking, how tobacco use affects impulsivity, and whether impulsivity may be a treatment target for smoking cessation. SCIENTIFIC SIGNIFICANCE: To our knowledge, this article is the first comprehensive review of literature, specifically examining the relationship of tobacco smoking and impulsivity among individuals with psychiatric disorders. By highlighting this understudied intersection, our findings provide novel insights into the relationship between smoking behaviors and impulsivity in individuals with mental illness, contributing to more effective treatment strategies and emphasizing the need for tailored interventions to address these co-morbidities.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".