Sociodemographic and psychosocial correlates of smoking-induced deprivation and its effect on quitting: findings from the International Tobacco Control Policy Evaluation Survey
Bibliographic record
Abstract
AIMS: To determine the prevalence and characteristics of smokers who experience smoking-induced deprivation (SID), and to examine its effect on quit attempts, relapse and cessation. METHODS: Waves 2 and 3 (2003-5) of the International Tobacco Control Policy Evaluation Survey were used, which is a prospective study of a cohort of smokers in the US, Canada, UK and Australia. SID was measured with the question "In the last six months, have you spent money on cigarettes that you knew would be better spent on household essentials like food?" A total of 7802 smokers participated in the survey in wave 2, of whom 5408 were also interviewed in wave 3. FINDINGS: The proportion of smokers who reported SID was highest in Australia (33%) and lowest in the UK (20%). Younger age, minority status and low income were associated with a higher probability of SID. Some of the other factors related to a higher probability of SID were higher level of nicotine dependence, having an intention to quit, and smoking to help one socialise or control weight. The relationship between SID and quit attempt was mediated by having an intention to quit and worrying that smoking would damage health and reduce the quality of life. The relationship between SID and relapse was mediated by perceived stress. SID was not associated with successful cessation. CONCLUSIONS: Many smokers experience deprivation that is the result of their smoking. Strategies to reduce the prevalence of smoking probably effect a general improvement in standards of living and reduction in deprivation.
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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 | 0.000 |
| 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 | 0.001 |
| 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".