Smoking Prevalence, Willingness to Quit and Factors Influencing Smoking Cessation among University Students in a Western Nigerian State
Notice bibliographique
Résumé
Background: In order to increase the proportion of successful attempts to quit smoking, it is important to understand the characteristics of smokers who successfully quit smoking. This study seeks to find out the smoking prevalence, the level of willingness to quit and factors influencing smoking cessation among university students in a western Nigerian state.Methodology: This study was a descriptive cross-sectional study carried out among young adults in tertiary institutions. A sample size of 300 was determined using Fishers formula while multi stage sampling technique was used to select respondents. The questionnaire was semi-structured, pretested and self administered. Analysis was done using Epi-Info version 3.4.1. Frequency tables and cross-tabulations were generated with a statistical significance p-value pre-determined at less than 0.05. Results: The number of respondents that ever smoked was 66 (22% of the total number of respondents) out of which 25 (38%) have ceased smoking while 41 (62%) currently smoke. Those willing to quit out of the 41 that currently smoke are 16 (39%) while 25 (61.0%) were not willing to quit. Of the respondents that ever smoked, the main location of smoking was parties/clubs (50%), while friends (53%) were found to be the main influence to smoke. Willingness to quit smoking was expressed by 16 (39.0%) of current smokers. Among respondents that ever smoked, 55(83.3%) attempted to quit out of which 41(74.5%) did as a result of health problems. Factors that positively affected smoking cessation were older age group of 26-30 (100%), belief that smoking can lead to premature death (47.1%) and never being asked to quit smoking (68.4%) with statistically significant p values. Conclusion: Influence of friends and going to parties/clubs are major factors contributing to smoking habit. Diagnosis of health problems play a major role in attempts to quit smoking while belief that smoking can lead to premature death is a major factor influencing smoking cessation. Being asked to quit smoking without a good understanding of the attendant health hazards does not contribute positively to successful smoking cessation. Peer education in schools emphasizing knowledge of the health implications of smoking as well as early diagnosis of smoking related health problems will go a long way in encouraging smoking cessation.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».