Translational Science: Basic Science to Public Policy and Back Again
Notice bibliographique
Résumé
In this month’s issue of Nicotine & Tobacco Research, the overarching theme is translational science in tobacco control. To set the stage there is an excellent review from the SRNT Basic Science Network1 on the ways in which findings from basic science research has informed and shaped public policy and how bidirectional communication between basic scientists and policy makers can further improve the translation of knowledge into policy. Given the current landscape of an ever-increasing variety of tobacco and nicotine products such as heat-not-burn, very low nicotine cigarettes (VLNCs) and other alternative nicotine delivery systems, there is a need for both tobacco research and policy to be nimble in order to adapt and implement relevant evidence-based policies. Next there are several papers assessing the effectiveness of cigarette package warnings as a public health policy. Lazard et al.2 present findings on believability of cigarette warnings regarding the addictive potential of nicotine in cigarettes and menthol as a contributor to the addictive potential of cigarettes. They found that the majority of adolescents and adults believe that both cigarettes and nicotine are addictive but are less likely to believe that menthol cigarettes are more addictive than regular cigarettes. This supports the policy of adding warning labels regarding addictiveness. Four studies assessing Graphic Warning Labels (GWLs) report similar findings. In a study by Cochran et al.3 neural processing of GWLs and how this affects attentional bias towards smoking cues was assessed using EEG. Their findings indicate that anxiety-provoking GWLs actually increase attentional bias to smoking cues while GWLs that elicit disgust has the opposite effect. They conclude that GWLs that are disgust-focused may be a better public health strategy for encouraging cessation attempts among current smokers. The second study also assessed attentional bias but researchers were interested in whether the size of the GWLs themselves altered visual attention and negative affect and intentions to quit.4 They found that GWLs that covered 50% of the package were more effective than GWLs covering 30% of the package at increase visual attention and negative affect. Intentions to quit were also higher among smokers exposed to the 50% GWL than those not exposed to a GWL, while those exposed to 30% GWL did not differ from controls on quit intention. The authors conclude that larger GWLs may be more effective, although courts in the United States have blocked this. The third study by Morgan et al.5 demonstrated that GWLs were more effective than text only warnings in sparking conversations among smokers social networks regarding the health effects of smoking and quitting than those exposed to text only warnings. A study comparing responses to GWLs over time in both Canada and Australia6 demonstrates that while attention to GWLs decreased over a 2-year period, cognitive responses increased especially in higher SES smokers, again demonstrating the overall effectiveness of the policy. These findings are confirmed in another study in North Carolina7 showing that while emotional and cognitive reactions to GWLs may wane over time, quit intentions actually increase. These studies taken together demonstrate that GWLs have the desired effect on smokers and as such are an effective public policy cessation intervention.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,097 | 0,169 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,002 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,007 | 0,062 |
| Communication savante | 0,024 | 0,046 |
| Science ouverte | 0,004 | 0,018 |
| Intégrité de la recherche | 0,052 | 0,049 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,027 | 0,006 |
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 source (Gemma direct ou Codex distillé), 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 ».