693 Violent behavior and its relationship with other health risk behaviors among Romanian adolescents in the digital era
Notice bibliographique
Résumé
<h3>Background</h3> The prevalence of different forms of peer violence among adolescents and the associated factors vary between different countries and regions and suffer variations during time. <h3>Objectives</h3> This study focuses on Romanian high school students and aims to assess the prevalence of different forms of interpersonal peer violence and self directed violence as well as their associations with different health risk behaviors. <h3>Methods</h3> A cross sectional study by means of anonymous questionnaires was performed in 2019 among 781 high-school students aged 15–19 from rural and urban areas situated in North-East Romania. <h3>Results</h3> Around one quarter of the high school students was a victim of a physical fight with a peer in the last three months, while 15% recognized they perpetrated physical fight among peers during the last 3 months. More than half of the participants declared that in the last 3 months they were aggressed verbally by peers at least once, while around one third of the participants said they have done so. In the same period of time one third of the high school students were excluded by peers at school and 43% excluded others. With regard to exposure to cyber-violence in the last 3 months, almost one third were victim of it and one out of 5 students were perpetrators of this form of violence. A percentage of 17% of the students declared that they had serious thought about suicide at least once during lifetime. There were several associations between different forms of violence as well as with other health risk behaviors such as tobacco and alcohol use and problematic use of social media. <h3>Conclusions</h3> Violence prevention and reduction efforts are needed for Romanian adolescents, including their integration in school based health education.
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 | 0,000 | 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 ».