Effectiveness of Mois sans tabac 2016, a French social marketing campaign against smoking
Bibliographic record
Abstract
Abstract Background In October 2016, Santé publique France, the National Public Health Agency, launched a social marketing campaign aiming at triggering quit attempts among smokers: “Mois sans tabac”, inspired by the English ’Stoptober’. This campaign sets smokers the objective of being smokefree for one month, in November. It combines a national mass-media communication with provision of cessation help services (quitline, website, mobile application, self-help kit) and local actions, in connection with the health regional agencies. The study aims at evaluating the effectiveness of this intervention. Methods The effectiveness evaluation is based on a specific analysis of the 2017 Health Barometer, a random survey representative of the population living in metropolitan France conducted between January and July 2017 on a sample of 25,319 individuals aged 18-75 years-old. The participation rate was 48.5%. Multivariate logistic regressions allowed testing the association between recall of the campaign and quit attempts (QA) in the last quarter of 2016, adjusted for sociodemographic confounders (N = 6,341). Respondents who attempted to quit were contacted at one-year for a follow-up. Results Nearly one in six daily smokers (15.9% [14.9-17.1]) reported making a 24-hour QA in the last quarter of 2016, and 18.4% [15.5-21.3] of them reported that it was related to Mois sans tabac, which represents approximately 380,000 QA [310,000-440,000] related to the operation. Among smokers who made a QA, 31% have been abstinent for at least 30 days [27%-34%] and 18% [15%-21%] reported they did not smoke anymore in 2017. Recall of Mois sans tabac was associated with QA in the last quarter of 2016 (aOR=1.3 [1.1-1.6], p < 0.01) and with cessation in 2017 (aOR=2.4 [1.4-4.2], p < 0.01), and odds-ratios increased with frequency of exposure and number of sources of information. Conclusions These results show that the first edition of Mois sans tabac was successful in triggering quit attempts among smokers. Key messages Mass-media campaigns for smoking cessation, combined with provision of help services and local actions, can be effective for triggering quit attempts. Importing foreign social marketing programs can be effective.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".