Cigarette smoking practice and attitudes, and proposed effective smoking cessation measures among college student smokers in China
Notice bibliographique
Résumé
Purpose This paper aims to investigate the average daily consumption of cigarettes and its correlates, attitudes toward smoking, and suggestions for anti‐smoking measures in a sample of Chinese college student smokers. Design/methodology/approach A sample of 150 college student cigarette smokers in Baoding, a city near Beijing, filled out a questionnaire asking about their average daily consumption of cigarettes, attitude toward smoking and their opinions on how they might control their smoking behavior. Findings In total, 85.3 percent of the smoking students were males and 14.7 percent females, and males had started smoking earlier. However, and surprisingly, the average daily consumption among females was greater than among males (9.6 vs 5.6 cigarettes a day , p <0.01). Average daily cigarette consumption was significantly associated with perceived health condition (students who thought themselves in poorer health consumed more), perceived risk of cigarette smoking (but with those who thought it harmful actually consuming more than those who did not), frequency of offering cigarettes as gifts, and perceived enjoyment from smoking. The three most commonly reported measures which curbed smoking were friends' or classmates' suggestions, the urging of a boyfriend/girlfriend, and the urging of parents. Practical implications Findings in this paper underscore the importance of developing effective smoking cessation programs through gender‐specific approaches at post‐secondary educational institutions as well as coordinating anti‐smoking efforts at multiple levels of educational administration in China. Originality/value This paper adds to the few studies on Chinese college students' cigarette smoking practices and student smokers' attitudes toward cigarette smoking. It also reports for the first time possible effective anti‐smoking measures as suggested by the students themselves. The value of this study lies in the finding that cigarette smoking among highly educated individuals in China is on the rise and vigorous research on the smoking behavior of this group should be a high priority.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».