The Prevalence and Correlates of Gambling in Australian Secondary School Students
Notice bibliographique
Résumé
Youth gambling is associated with a range of harms. This study aimed to examine, among Australian adolescents, the prevalence of gambling (ever, in the last month, at-risk and problem), the most frequent gambling types and modalities, and to explore the student characteristics associated with gambling in the last month and with at-risk or problem gambling. Students aged 12-17 years from Victoria and Queensland answered gambling questions as part of the Australian Secondary School Alcohol and Drug (ASSAD) Survey in 2017. The ASSAD also included a series of questions about smoking, alcohol and other drug use, and mental health. A total of 6377 students from 93 schools were included in analysis. The prevalence of ever gambling and gambling in the last month was 31% and 6% respectively. Of students who had gambled in the last month, 34% were classified as at-risk and 15% were classified as problem gamblers. The most frequent types of gambling in the last month were horse or dog race and sports betting. Students who gambled in the last month did so most frequently via a parent or guardian purchasing or playing for them, at home or at a friends' house, and online or using an app. Regression analysis indicated that male gender, having money available to spend on self, alcohol consumption in the last seven days, the number of types of advertisements seen in the last month, and the number of peer or family members who gambled in the last month, were significantly associated with the likelihood of students gambling in the last month. Male gender, some age categories, and exposure to more types of gambling advertising were also significant predictors of being classified as an at-risk or problem gambler. This large study of youth gambling provides data on gambling behaviours and related variables from a large sample of Australian secondary school students. Student characteristics, including male gender and exposure to more types of gambling advertising, were associated with an increased likelihood of gambling in the last month and of being classified as an at-risk or problem gambler. Further implications of the study findings are discussed.
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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».