Predictors of smoking in cars with nonsmokers: Findings from the 2007 Wave of the International Tobacco Control Four Country Survey
Bibliographic record
Abstract
OBJECTIVE: This study examines the proportion and characteristics of smokers who smoke in cars with nonsmokers across four countries and the potentially modifiable correlates of this behavior. METHODS: Respondents included a total of 6,786 current adult smokers from Wave 6 (September 2007-February 2008) of the International Tobacco Control Four Country Survey, a random digit-dial telephone survey of nationally representative samples of adult smokers in Australia, the United Kingdom, Canada, and the United States. RESULTS: Reports of smoking in cars with nonsmokers ranged from a low of 29% in Australia and the United Kingdom, to 34% in Canada, and to a high of 44% in the United States. Daily smokers who were from the United States, male, and younger were the most likely to smoke in cars with nonsmokers. Several potentially modifiable factors were also found to be related to this behavior, including smoke-free homes and beliefs about the dangers of cigarette smoke exposure to nonsmokers. CONCLUSIONS: A considerable proportion of smokers continue to smoke in cars with nonsmokers across the four countries, particularly in the United States. Public health campaigns should educate smokers about the hazards of cigarette smoke exposure and promote the need for smoke-free cars. These findings provide a foundation of evidence relevant for jurisdictions that are considering banning smoking in cars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".