Effectiveness of ‘Mois sans tabac 2016’: A French socialmarketing campaign against smoking
Bibliographic record
Abstract
of the FCTC, which calls for the use of all available communication tools to promote and strengthen public awareness of tobacco-control issues.In general, massmedia campaigns to encourage smoking cessation are effective as part of comprehensive tobacco control programmes [3][4][5][6][7][8] .Another tool proposed in Article 14 of the FCTC is the design and implementation of effective programs aimed at promoting the cessation ABSTRACT INTRODUCTIONIn October 2016, the first edition of Mois sans tabac (Tobacco-Free Month) was launched, a campaign which had invited French smokers to challenge themselves to quit smoking for the whole month of November.We aimed to study the effectiveness of this social marketing intervention on quit attempts (QA) in the general French population, and to study possible differences according to sociodemographic characteristics.METHODS This study used data from the 2017 Health Barometer survey, a random survey conducted by telephone on 25319 individuals.It included 6341 respondents who reported that they were daily smokers when the Mois sans tabac campaign was launched in 2016.The association between self-declared exposure to the campaign and making a QA has been studied using multivariate logistic regressions.RESULTS Exposure to the 2016 Mois sans tabac campaign is associated with a QA lasting at least 24 hours in the final quarter of 2016 (AOR=1.32;95% CI: 1.07-1.63,p<0.01), with a QA lasting at least 30 days (AOR=1.95;95% CI: 1.31-2.91,p<0.001), and being abstinent at the time of the interview in 2017 (AOR=2.39;95% CI: 1.37-4.15,p<0.01).A dose-effect relationship is observed between the frequency of exposure to the campaign and QA, which is mostly explained by the number of sources of exposure (television, radio, posters, the press, the internet and social networks).Although certain priority groups (e.g.manual workers, the unemployed) had poorer recall of the campaign than other groups, the impact of self-reported exposure to the campaign on QA in unemployed people or those with less than high school educational level appears to have been greater.CONCLUSIONS These analyses suggest the effectiveness of the 2016 Mois sans tabac intervention, in a context of strengthening public tobacco control policies in France, which may have contributed to the drop in smoking observed between 2016 and 2019.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".