Socioeconomic variations in nicotine dependence, self-efficacy, and intention to quit across four countries: findings from the International Tobacco Control (ITC) Four Country Survey
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of socioeconomic status (SES) on nicotine dependence, self-efficacy, and intention to quit. DESIGN SETTING AND PARTICIPANTS: Data were from the first wave (2002) of the International Tobacco Control (ITC) Four Country Survey (ITC-4), a panel study of over 2000 adult smokers from each of four countries: the United States, Canada, the United Kingdom, and Australia. Data were collected via telephone interviews. MAIN OUTCOME MEASURES: Nicotine dependence, intention to quit, and self-efficacy to quit smoking were the main outcome measures used in this study. RESULTS: Lower levels of education were associated with higher nicotine dependence. The effect of lower income on higher heaviness of smoking index (HIS) scores was significant in Canada, the UK, and Australia. Respondents with low education had 35% larger odds of low self-efficacy than those with high education. Respondents with low education had 40% larger odds of having no intention to quit than those with high education. Respondents with low income had 23% larger odds of having no intention to quit than those with high income. Country was not a moderator of the association of SES with self-efficacy and intention to quit. CONCLUSION: To the extent that lower SES smokers are more addicted, they are likely to need more intensive support if they are to be successful in their attempts to quit. Given their lower incomes, this places a special responsibility on government to provide or subsidise such services. This should include access to the widest possible range of effective pharmacotherapies complemented with evidence based counselling and support.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".