The impact of social context on cigarette self-administration in nondependent smokers
Bibliographic record
Abstract
Tobacco use in nondependent smokers (i.e. chippers) is believed to be largely determined by situational factors including social context. However, little empirical research has examined how different social contexts impact chippers' smoking behaviour. Twenty-eight (16 men) chippers completed two laboratory sessions where they were offered an opportunity to self-administer puffs of their preferred tobacco brand using a progressive ratio task. During an individual session, participants self-administered cigarettes alone and during a paired session, they self-administered cigarettes with a coparticipant who was also smoking. The strongest predictors for number of self-administered puffs and breakpoint during the paired session were coparticipants' number of puffs and breakpoint, respectively (P<0.001), followed by puffs taken and breakpoint during the individual session (P<0.01). Current smoking frequency (cigarettes/week) did not significantly predict puffs taken or breakpoint during the paired session. Latency to cigarette self-administration during the paired session was correlated positively with coparticipants' latency (P<0.05), but not with latency during the individual session or cigarettes per week. The findings suggest that the presence of another smoker exerts an important influence on the quantity of chippers' smoking behaviour, such that chippers match their smoking behaviour to that of other smokers in their proximate environment.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".