Behavioral economic tobacco demand in relation to cigarette consumption and nicotine dependence: a meta‐analysis of cross‐sectional relationships
Bibliographic record
Abstract
Abstract Background and Aims A cigarette purchase task (CPT) aims to characterize individual variation in the reinforcing value of tobacco. This meta‐analysis estimated the associations between cigarette demand, tobacco consumption and nicotine dependence using this task. Design A meta‐analysis of cross‐sectional studies identified by PubMed and PsycINFO databases was conducted. Fixed‐ and random‐effects models were used. The study also examined the model used to derive elasticity of demand (exponential or exponentiated) as a potential moderator. Publication bias was assessed using ‘fail‐safe N', Begg–Mazumdar test, Egger's test, Tweedie's trim‐and‐fill approach and meta‐regression of publication year with effect size. Setting Studies from any setting that reported coefficient correlations on the tested associations. Participants Daily cigarette users (i.e. 5 to 38 cigarettes per day; n = 7649). Measurements Cigarette consumption, nicotine dependence and five tobacco demand indicators: intensity (i.e. consumption at no cost), elasticity (i.e. sensitivity to rises in costs), Omax (maximum expenditure), Pmax (i.e. price at which consumption becomes elastic) and breakpoint (i.e. price at which consumption ceases). Findings Twenty‐three studies met inclusion criteria. All the CPT indices were significantly correlated with smoking behavior (rs = 0.044–0.572, Ps = 0.012–10−8). Medium‐to‐large effect size associations were present for intensity, Omax, and elasticity, whereas small effects were obtained for breakpoint and Pmax. Evidence of a moderating effect of the different elasticity modeling approaches was not present. There was limited evidence of publication bias. Conclusions All five demand indices derived from the cigarette purchase task by (CPT) were robustly associated with cigarette consumption and tobacco dependence. Of the demand indices, maximum expenditure, intensity and elasticity exhibited the largest magnitude associations.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.034 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".