Socioeconomic and country variations in knowledge of health risks of tobacco smoking and toxic constituents of smoke: results from the 2002 International Tobacco Control (ITC) Four Country Survey
Bibliographic record
Abstract
BACKGROUND: Socioeconomic status is strongly associated with smoking prevalence and social class differences contribute substantially to social inequalities in mortality. This research investigated socioeconomic and country variations in smokers' knowledge that smoking causes heart disease, stroke, impotence and lung cancer, that smoke contains cyanide, mercury, arsenic and carbon monoxide, and whether nicotine causes most of the cancer. METHODS: Data were from the International Tobacco Control (ITC) Four Country Survey, a cohort survey of over 9000 adult smokers from four countries: the United States, Canada, the United Kingdom, and Australia. Data were collected via telephone interviews in 2002. RESULTS: Higher education and income were associated with higher awareness. For example, the odds of knowing that smoking causes heart disease, stroke and lung cancer were respectively 71%, 34% and 83% larger for respondents with high versus low income. The odds of knowing that smoke contains cyanide, mercury, arsenic and carbon monoxide were respectively 66%, 26%, 44% and 108% larger for respondents with a university degree than those with a high school diploma or lower level of education. Results also revealed that awareness of harms of smoking was generally the highest in Canada and the lowest in the UK. CONCLUSIONS: Lower socioeconomic status was associated with lower awareness of the harms of smoking and misunderstanding around nicotine. There is a need to improve knowledge of the dangers of smoking among the disadvantaged segments of the population.
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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 | 0.000 | 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".