Variations in Daily Cigarette Consumption on Work Days Compared With Nonwork Days and Associations With Quitting: Findings From the International Tobacco Control Four-Country Survey
Bibliographic record
Abstract
INTRODUCTION: We explore whether reported daily cigarette consumption differs between work days and nonwork days and whether variation in consumption between work days and nonwork days influences quitting and abstinence from smoking. We also explore whether effects are independent of measures of addiction and smoking restrictions at work and home. METHODS: Data were from 5,732 respondents from the first five waves of the International Tobacco Control Four-Country Survey, occurring between 2002 and 2006. Respondents were current smokers employed outside the home. Variation in daily cigarette consumption on work days compared with nonwork days at one wave was used to predict the likelihood of making an attempt and the likelihood of maintaining a quit attempt for at least a month at the next wave. Generalized estimating equations were used to combine data for multiple waves. RESULTS: Just under half reported smoking more on a nonwork day, a little over a third reported no difference, and around one fifth reported smoking more on a work day. Controlling for possible confounding factors, smoking more on a work day was associated with making quit attempts. Among people who made a quit attempt, variation in consumption did not consistently predict one month's abstinence, being positive in Australia, but negative in the United Kingdom. CONCLUSION: Those who smoke more on work days try to quit more. Country differences for success may be related to the extent of bans on smoking, with those smoking more on work days more likely to succeed where bans in workplaces and public places were more prevalent, such as Australia at the time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".