Stability of Cigarette Consumption Over Time Among Continuing Smokers: A Latent Growth Curve Analysis
Bibliographic record
Abstract
OBJECTIVES: This paper examined the stability over time of daily cigarette consumption of continuing smokers and explored factors that might account for the patterns of change in consumption using a latent growth curve (LGC) analytic approach. METHODS: Data come from the first 5 waves of the International Tobacco Control Four-Country Survey, conducted in Canada, the United States, the United Kingdom, and Australia where a cohort of over 2,000 smokers from each country were recruited and followed up annually with replenishment. RESULTS: Raw data revealed that continuing smokers showed a marked steep decline in cigarettes per day during the first 2 waves followed by a gentler linear decline in consumption over the remaining waves of the study period. This pattern of change in cigarette consumption was best modelled using a piecewise linear LGC model. Baseline consumption level was highest in Australia and lowest in the United Kingdom, although the rate of decline was similar across the 4 countries. Being older than 55 years and having made at least 1 quit attempt were related to greater rate of decline in consumption. CONCLUSIONS: Continuing smokers who are unwilling or unable to quit smoking can and do attempt to reduce their daily cigarette consumption over time. Factors such as making a quit attempt even if unsuccessful and experiencing smoking bans at work and at homes can contribute to reduced smoking among this group, which suggests that interventions focusing in on these factors, along with providing cessation help, may greatly improve their chances of quitting smoking altogether.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".