Awareness of pro-tobacco advertising and promotion and beliefs about tobacco use: Findings from the Tobacco Control Policy (TCP) India Pilot Survey
Bibliographic record
Abstract
Tobacco companies are utilizing similar strategies to advertise and promote their products in developing countries as they have used successfully for over 50 years in developed countries. The present study describes how adult smokers, smokeless tobacco users, and non-users of tobacco from the Tobacco Control Project (TCP) India Pilot Survey, conducted in 2006, responded to questions regarding their perceptions and observations of pro-tobacco advertising and promotion and beliefs about tobacco use. Analyses found that 74% (n=562) of respondents reported seeing some form of pro-tobacco advertising in the last six months, with no differences observed between smokers (74%), smokeless tobacco users (74%), and nonsmokers (73%). More than half of respondents reported seeing pro-tobacco advertising on store windows or inside shops. Overall, this study found that a significant percentage of tobacco users and non-users in India report seeing some form of pro-tobacco advertising and promotion messages. Additional analyses found that smokers were more likely to perceive tobacco use as harmful to their health compared with smokeless tobacco users and non-users (p<0.01). The findings from this study reiterate the need for stronger legislation and strict enforcement of bans on direct and indirect advertising and promotion of tobacco products in India.
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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 | 0.003 |
| 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) | 0.001 | 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".