A dynamic modelling analysis of the impact of tobacco control programs on population-level nicotine dependence
Bibliographic record
Abstract
According to the 'hardening hypothesis', average nicotine dependence will increase as less dependent smokers quit relatively easily in response to effective public health interventions, so that sustained progress in reducing smoking prevalence will depend on shifting the emphasis of tobacco control programs towards intensive treatment of heavily dependent smokers (who comprise an increasing fraction of continuing smokers). We used a system dynamics model of smoking behaviour to explore the potential for hardening in a population of smokers exposed to effective tobacco control measures over an extended period. Policy-induced increases in the per capita cessation rate are shown to lead inevitably to a decline in the proportion of smokers who are heavily dependent, contrary to the hardening hypothesis. Changes in smoking behaviour in Australia over the period 2001‒2016 resulted in substantial decreases in current smoking prevalence (from 23.1% in 2001 to 14.6% in 2016) and the proportion of heavily dependent smokers in the smoking population (from 52.1% to 36.9%). Public health interventions that have proved particularly effective in reducing smoking prevalence (tobacco tax increases, smoke-free environment legislation, antismoking mass media campaigns) are expected to also contribute to a decline in population-level nicotine dependence.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".