The Role of Cross-Cue Reactivity in Coexisting Smoking and Gambling Habits
Bibliographic record
Abstract
Cigarette smoking is the most common addictive behaviour co-occurring with problem gambling. Based on classical conditioning, smoking and gambling cues may acquire conditioned stimulus properties that elicit cravings for both behaviours. This study examined cross-cue reactivity in 75 men who were regular smokers, poker players or cigarette-smoking poker players. Participants were exposed to discrete cigarette, poker and neutral cues while skin conductance and psychological urges to smoke and gamble were measured. Results showed evidence of cross-cue reactivity based on skin conductance, and subjective response to smoking cues; subjective response to gambling cues was less clear. Smoking gamblers showed greater skin conductance reactivity to cues, and stronger subjective urges to smoke to smoking and gambling cues, compared to individuals who only smoked or only gambled. This study demonstrates evidence for cross-cue reactivity between a substance and a behavioural addiction, and the results encourage further research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".