Intention to quit among Indian tobacco users: Findings from International Tobacco Control Policy evaluation India pilot survey
Bibliographic record
Abstract
INTRODUCTION: Tobacco users face barriers not just in quitting, but also in thinking about quitting. The aim of this study was to understand factors encouraging intention to quit from the 2006 International Tobacco Control Policy (TCP) Evaluation India Pilot Study Survey. MATERIALS AND METHODS: A total of 764 adult respondents from urban and rural areas of Maharashtra and Bihar were surveyed through face-to-face individual interviews, with a house-to-house approach. Dependent variable was "intention to quit tobacco." Independent variables were demographic variables, peer influence, damage perception, receiving advice to quit, and referral to cessation services by healthcare professionals and exposure to anti-tobacco messages. Logistic regression model was used with odds ratio adjusted for location, age, gender, and marital status for statistical analysis. RESULTS: Of 493 tobacco users, 32.5% intended to quit. More numbers of users who were unaware about their friends' tobacco use intended to quit compared to those who were aware (adjusted OR = 8.06, 95% CI = 4.58-14.19). Higher numbers of users who felt tobacco has damaged their health intended to quit compared to those who did not feel that way (adjusted OR = 5.62, 95% CI = 3.53-8.96). More numbers of users exposed to anti-tobacco messages in newspapers/magazines (adjusted OR = 1.76, 95% CI = 1.02-3.03), restaurants (adjusted OR = 2.47, 95% CI = 1.37-4.46), radio (adjusted OR=4.84, 95% CI = 3.01-7.78), cinema halls (adjusted OR = 9.22, 95% CI = 5.31-15.75), and public transportation (adjusted OR = 10.58, 95% = 5.90-18.98) intended to quit compared to unexposed users. CONCLUSION: Anti-tobacco messages have positive influence on user's intentions to quit.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".