The association between race-based bullying and nicotine vaping in adolescents
Notice bibliographique
Résumé
Racialized youth face a higher risk of bullying victimization due to discriminatory bias which can lead to adverse health conditions and increased substance use. This study aimed to examine whether bullying victimization is associated with nicotine vaping, and whether race-based bullying was associated with greater odds of nicotine vaping than other forms of bullying. Cross-sectional survey data were used from the COMPASS study collected during the 2022–2023 school year from 14,480 students attending secondary schools in Ontario, Canada. Associations between any bullying victimization (in the last 30 days) and nicotine vaping (≥2 times in the last 30 days), and then among bullied students, between race-based bullying and vaping, were explored using random intercept logistic regression models. One third (33.4 %) of students who reported race-based bullying engaged in vaping, in comparison to 29.4 % of students who were bullied for other reasons and 15.6 % of nonbullied students. Students who experienced bullying had higher odds (AOR 2.25, 95 % CI [2.03–2.50]) of vaping relative to nonbullied students. Among students who experienced bullying, there was no statistical difference in the odds of vaping between those who reported being bullied due to racial or cultural reasons and their peers who reported being bullied for reasons other than their race or culture (1.16, 95 % CI [0.81–1.67]). Results suggest that while bullying is strongly associated with vaping among adolescents, being bullied for reasons such as race, culture, or ethnicity does not significantly alter the likelihood of vaping behaviour relative to other forms of bullying. • This is the first Canadian study to examine nicotine vaping in relation to reported experiences of race-based bullying. • Any bullying victimization was associated with higher odds of nicotine vaping in adolescents. • Students that experienced race-based bullying had the highest frequency of nicotine vape use. • Multiethnic and another ethnicity adolescents were more likely to vape and be bullied. • Vaping odds did not differ based on race-based bullying relative to other bullying.
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 | 0,000 |
| 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 ».