Perception de l'efficacité des paquets de cigarettes standardisés
Bibliographic record
Abstract
Introduction - As the tobacco industry has been stripped of most of the marketing mix, the cigarette pack has become an increasingly important marketing tool. It is possible however to reduce the attractiveness of the pack through plain packaging, which involves removing the pack design elements and leaving only the health warning and brand name in standardised font and size. Method - To investigate this tool, a quantitative study based on face to face interviews was conducted in France among a representative sample of 836 individuals (smokers and non smokers, aged 18 and above). A current cigarette pack and a plain pack of the French leading brand were compared. Results - Results indicated that plain packaging reduces the appeal of the pack (dull appearance, reduces the desire to buy). Compared to a current pack, the plain pack was associated with greater awareness of smoking danger and reported to facilitate intentions to reduce consumption, to quit, or not to start among non-smokers. Discussion - Our study highlights that the tobacco pack is an effective promotion tool. The plain packaging certainly has the potential to reduce the attractiveness of tobacco industry products and to help fighting against tobacco use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".