Predictors of Illicit Drug Use Among University Students in Northern Ireland, Wales and England
Notice bibliographique
Résumé
INTRODUCTION: The use of illicit drug/s among university students is a public health concern. Nevertheless, many UK studies investigated a narrow spectrum of variables to explore their association/s with illicit drug/s use. METHODS: We assessed the associations between a wide range of socio-demographic, health and wellbeing variables (independent variables) and having used illicit drug/s regularly, occasionally or never in life (dependent variables). Data (3706 students) were collected from seven universities in England, Wales, and Northern Ireland, using a self-administered questionnaire. RESULTS: About 5% of the sample had regularly used illicit drug/s, 25% occasionally, and 70% never. Regular drug use (RDU) was significantly more likely among males aged 21-29 years, daily smokers, those with heavy episodic drinking or possible alcohol dependency (CAGE test), and those who perceived their academic performance better than their peers. RDU was less likely among students with high health awareness and those living with parents. The predictors of occasional drug use (ODU) were similar to those of RDU. However, in addition, students with higher perceived stress were less likely, and students who felt financial burden/s were more likely to report ODU, while no association with academic performance was found. Never use of illicit drug/s was inversely associated with most of the variables listed above, and was positively associated with religiosity. Illicit drug/s use goes along with other substance use (alcohol and smoking). The finding that illicit drug/s use was higher among students reporting good academic performance was surprising and raises the question of whether illicit drug/s may be used as performance enhancing drugs. CONCLUSION: The factors identified with illicit drug/s use in this study could be utilized to develop appropriate public health policies and preventive measures for the health of students. Multilevel, value based, comprehensive, and strategic long-term intervention plans are required. This could include social interventions aimed at generating recreations alternatives and opportunities for youth, and a critical review for current authorities' interventions and services. Suggestions for coping with problems of campus illicit drug use/abuse also need to be offered.
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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 | 0,000 |
| 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 ».