What the public thinks about the tobacco industry and its products
Bibliographic record
Abstract
OBJECTIVES: To assess public attitudes toward the tobacco industry and its products, and to identify predictors of attitudes supportive of tobacco industry denormalisation. DESIGN: Population based, cross sectional survey. SETTING: Ontario, Canada. SUBJECTS: Adult population (n = 1607). MAIN OUTCOME MEASURES: Eight different facets of tobacco industry denormalisation were assessed. A denormalisation scale was developed to examine predictors of attitudes supportive of tobacco industry denormalisation, using bivariate and multivariate analyses. RESULTS: Attitudes to the eight facets of tobacco industry denormalisation varied widely. More than half of the respondents supported regulating tobacco as a hazardous product, fining the tobacco industry for earnings from underage smoking, and suing tobacco companies for health care costs caused by tobacco. Majorities also thought that the tobacco industry is dishonest and that cigarettes are too dangerous to be sold at all. Fewer than half of the respondents thought that the tobacco industry is mostly or completely responsible for the health problems smokers have because of smoking and that tobacco companies should be sued for taxes lost from smuggling. In particular, less than a quarter thought that the tobacco industry is most responsible for young people starting to smoke. Non-smoking, knowledge about health effects caused by tobacco, and support for the role of government in health promotion were independent predictors of support for tobacco industry denormalisation. CONCLUSIONS: Although Ontarians are ambivalent toward tobacco industry denormalisation, they are supportive of some measures. Mass media programmes aimed at increasing support for tobacco industry denormalisation and continued monitoring of public attitudes toward this strategy are needed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".