Interventions pour l’arrêt du tabac chez les fumeurs de faible niveau socio-économique : synthèse de la littérature
Bibliographic record
Abstract
INTRODUCTION: In most western countries, smoking appears to be highly differentiated according to socio-economic level. Two systematic reviews published in 2014 showed that most of the recommended interventions for smoking cessation, particularly individual interventions, tend to increase social inequalities in health. An analysis of the most recent literature was carried out in order to provide policy makers and stakeholders with a set of evidence on the modalities of interventions to encourage and help disadvantaged smokers quit smoking. METHODS: This review was based on articles published between January 2013 and April 2016. Only studies conducted in European countries or countries in stage 4 of the tobacco epidemic (USA, Canada, Australia, New Zealand) were included. Selected articles were double-screened. RESULTS: Twenty-three studies were identified, including evaluation of media campaigns, face-to-face behavioural support, phone- and web-based support or awareness of passive smoking among children. Some interventions adapted to precarious populations have been shown to be effective. CONCLUSIONS: Some characteristics would facilitate access and improve the support of disadvantaged groups, including a local intervention, a proactive approach and co-construction with targeted smokers.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".