Electronic cigarettes and subsequent use of cigarettes in young people: An evidence and gap map
Notice bibliographique
Résumé
BACKGROUND AND AIMS: The use of e-cigarettes may influence later smoking uptake in young people. Evidence and gap maps (EGMs) are interactive on-line tools that display the evidence and gaps in a specific area of policy or research. The aim of this study was to map clusters and gaps in evidence exploring the relationship between e-cigarette use or availability and subsequent combustible tobacco use in people aged < 30 years. METHODS: We conducted an EGM of primary studies and systematic reviews. A framework and an interactive EGM was developed in consultation with an expert advisory group. A systematic search of five databases retrieved 9057 records, from which 134 studies were included. Systematic reviews were appraised using AMSTAR-2, and all included studies were coded into the EGM framework resulting in the interactive web-based EGM. A descriptive analysis of key characteristics of the identified evidence clusters and gaps resulted in this report. RESULTS: Studies were completed between 2015 and 2023, with the first systematic reviews being published in 2017. Most studies were conducted in western high-income countries, predominantly the United States. Cohort studies were the most frequently used study design. The evidence is clustered on e-cigarette use as an exposure, with an absolute gap identified for evidence looking into the availability of e-cigarettes and subsequent cessation of cigarette smoking. We also found little evidence analysing equity factors, and little exploring characteristics of e-cigarette devices. CONCLUSIONS: This evidence and gap map (EGM) offers a tool to explore the available evidence regarding the e-cigarette use/availability and later cigarette smoking in people under the age of 30 years at the time of the search. The majority of the 134 reports is from high-income countries, with an uneven geographic distribution. Most of the systematic reviews are of lower quality, suggesting the need for higher-quality reviews. The evidence is clustered around e-cigarette use as an exposure and subsequent frequency/intensity of current combustible tobacco use. Gaps in evidence focusing on e-cigarette availability, as well as on the influence of equity factors may warrant further research. This EGM can support funders and researchers in identifying future research priorities, while guiding practitioners and policymakers to the current evidence base.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».