Bibliographic record
Abstract
This thesis conducts an intelligent model based analysis to evaluate the effectiveness of tobacco control policies. By using the International Tobacco Control Four Country Survey data, the impact of tobacco control policies on smokers’ quitting behaviour is examined in four developed countries: Australia, Canada, the United Kingdom and the United States. A set of intelligent models are developed for predicting smokers’ quitting behaviour. The performance of these intelligent models is evaluated in order to select the best intelligent model for analyses. An attribute-based analysis is further conducted to investigate the underlying patterns and identify the factors that have the greatest impact on smokers’ plans to quit and their attempts to quit. Four policy drivers identified from the existing motivational attributes include: personal concerns, cigarette price, environmental restrictions and health system encouragement. They can be used to represent tobacco control policies. Outliers in the data are removed to improve the performance of the intelligent models. Results show that the derived policy drivers can fully represent the original attributes based on the performance of intelligent models using these two groups of input attributes. To evaluate the relative degrees of impact of tobacco control policies, hypothetical policy impacted populations are created to examine the variations of the quit attempt rate of smokers. Comparative studies are conducted for offering insightful analyses of impact degrees of tobacco control policies on different groups of smokers across the four countries. Results show that smokers’ health concerns and professional advice for quitting are two important factors to encourage quitting behaviour. Smoke-free policies may have a certain impact on increasing the quit attempt rate. In comparison with other tobacco control policies, the effectiveness of increasing cigarette price to reduce tobacco use is weak. Overall, this research establishes a methodological framework for modelling the complex planning process of tobacco control policies. In particular the framework can be used to measure the impact of specific tobacco control policies on smokers’ quitting behaviour across the four countries.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".