Educational differences in associations of noticing anti-tobacco information with smoking-related attitudes and quit intentions: findings from the International Tobacco Control Europe Surveys
Bibliographic record
Abstract
This study examined educational differences in associations of noticing anti-tobacco information with smoking-related attitudes and quit intentions among adult smokers. Longitudinal data (N = 7571) from two waves of six countries of the International Tobacco Control (ITC) Europe Surveys were included. Generalized estimating equation analyses and multiple linear and logistic regression analyses were conducted. Higher educated smokers noticed anti-tobacco information slightly more often than lower educated smokers (F(2) = 25.78, P < 0.001). Noticing anti-tobacco information was associated with more negative smoking-related attitudes (β = 0.05, P < 0.001) and more quit intentions (OR = 1.08, P < 0.001). Among smokers without a quit intention at baseline, a positive association was found for noticing anti-tobacco information at baseline with follow-up quit intention (OR = 1.14, P = 0.003). No other longitudinal associations were found. No educational differences were found in the association of noticing anti-tobacco information with smoking-related attitudes but associations with quit intentions were found only among low (OR = 1.12, P = 0.001) and high educated respondents (OR = 1.11, P < 0.001) and not among moderate educated respondents (OR = 1.02, P = 0.43). Noticing anti-tobacco information may positively influence quit intentions and possibly smoking-related attitudes. Lower educated smokers were as likely to be influenced by anti-tobacco information as higher educated smokers but noticed anti-tobacco information less often; increasing reach of anti-tobacco information may increase impact in this group.
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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 | 0.007 |
| 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".