Australian smokers increasingly use help to quit, but number of attempts remains stable: findings from the International Tobacco Control Study 2002–09
Bibliographic record
Abstract
OBJECTIVE: To assess interest in quitting smoking and quitting activity, and the use of pharmacotherapy and behavioural cessation support, among Australian smokers between 2002 and 2009. METHODS: Data were taken from 3303 daily smokers taking part in a minimum of two consecutive waves of the International Tobacco Control Four Country Survey. Using weighted data to control for sampling and attrition, we explored any effects due to age, sex, whether living in a metropolitan or regional area, and nicotine dependence. RESULTS: Around 40% of smokers reported trying to quit and, of these, about 23% remained abstinent for at least one month when surveyed. Low socioeconomic smokers were less likely to be interested in quitting and less likely to make a quit attempt. Reported use of prescription medication to quit smoking rose sharply at the last wave with the addition of varenicline to the pharmaceutical benefits scheme. Among those who tried, use of help rose gradually from 37% in 2002 to almost 59% in 2009 (including 52% using pharmacotherapy and 15% using behavioural forms of support). IMPLICATIONS: Use of help to quit is now the norm, especially among more dependent smokers. This may reflect a realization among smokers that quitting unassisted is more likely to fail than quitting with help, as well as the cumulative effect of promoting the use of help. Given the continuing high levels of failed quit attempts, services need to be able to expand to meet this increasing demand.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".