Socioeconomic position and abrupt versus gradual method of quitting smoking: Findings from the International Tobacco Control Four-Country Survey
Bibliographic record
Abstract
INTRODUCTION: Our aim was to investigate the association between socioeconomic position (income and education) and abrupt versus gradual method of smoking cessation. METHODS: The analysis used data (n = 5,629) from Waves 1 through 6 (2002-2008) of the International Tobacco Control Four-Country Survey, a prospective study of a cohort of smokers in the United States, Canada, the United Kingdom, and Australia. RESULTS: Logistic regression analyses using generalized estimating equations showed that higher income (p < .001) and higher education (p = .011) were associated with a higher probability of abrupt versus gradual quitting. The odds of adopting abrupt versus gradual quitting were about 40% higher among respondents with high income ($60,000 and more in the United States/Canada/Australia and £30,000 and more in the United Kingdom) compared with those with low income (less than $30,000 in the United States/Canada/Australia; £15,000 and less in the United Kingdom). Similarly, the odds of abrupt versus gradual quitting were about 30% higher among respondents with a high level of education (university degree) compared with those with a low level of education (high school diploma or lower). DISCUSSION: Higher socioeconomic position is associated with a higher probability of quitting abruptly rather than gradually reducing smoking before quitting.
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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 | 0.003 |
| 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 | 0.001 |
| 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 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".