Notice bibliographique
Résumé
No one can doubt the importance of smoking cessation as a fundamental preventive initiative (1,2). The dramatic reductions in morbidity and mortality that follow cessation argue that cessation interventions should be an integral part of clinical care. But despite a substantial reduction in smoking rates in recent decades, there is now disconcerting, if not dispiriting, news regarding a decline in cessation rates in the United States. In this issue of the Journal, Leventhal and colleagues (3) report that declines in smoking have slowed following decades of reductions, particularly among minority, disadvantaged, and rural groups. Their conclusions are based on a careful analysis of successive iterations of the Tobacco Use Supplement of the Current Population Survey, affording a robust examination of population smoking behaviors and a nuanced understanding of cessation activities among groups typically not captured in standard cessation research. Their findings reveal that there was virtually no change in cessation activities from 2014 to 2019 and that sociodemographic disparities in cessation behaviors were prominent. Overall interest in cessation remained constant: 77.1% of smokers voiced a desire to quit, but sustained cessation rates (7.5%) were unchanged (3). More disquieting, rates of smoking cessation have declined among the most vulnerable segments of the population, where rates of smoking and smoking-related disease are high. Equally concerning, only a minority of smokers (34.4%) employ smoking cessation treatments when attempting to quit, and predictable discrepancies in the use of cessation supports exist among racialized and disadvantaged communities. Many of the conventional approaches intended to support cessation—quit lines, subsidized nicotine-replacement therapy, and digital treatment applications—attracted only modest use. The findings identify real challenges for clinicians and public-health organizations. How can we address our failure to provide supported cessation opportunities to those who have interest in cessation but receive little assistance? In clinical settings, inadequate approaches to addressing tobacco addiction are, sadly, commonplace. The introduction of integrated cessation programs in hospital settings has been recommended for decades (4-6). Evidence of their clinical significance and their ability to reduce cost and subsequent use of health-care resources continues to accumulate (7-10). Smoking cessation at the time of cancer diagnosis can enhance quality of life, reduce treatment complications, and prolong survival (11). Recommendations for the incorporation of smoking-cessation services as a standard of cancer care abound and should be heeded (12-14). Barriers remain but can be overcome; the time to ensure the systematic integration of smoking-cessation services in clinical settings is long overdue (15-17). Notwithstanding our ability to increase the likelihood of smoking cessation success, the challenge of smoking cessation is substantial. How can we combat the activities of an industry unrivalled in its ability to evade regulation and to cause enormous human loss? Although efforts to assist smokers should be expanded and systematized, it is equally important that as clinicians we advocate for much more robust and far-reaching regulation of the tobacco industry—an industry unparalleled in its record of duplicity and destruction. None. Role of the funder: Not applicable. Disclosures: The author has no conflicts of interest to disclose. Author contributions: Writing, original draft, revisions—AP. No new data are presented in this editorial.
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 | 0,011 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,010 |
| Communication savante | 0,008 | 0,011 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,050 | 0,066 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,003 |
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 ».