Youth aging out of care and contact with the criminal justice system: the role of educational transitions in early adulthood
Notice bibliographique
Résumé
The process of transitioning of placement is marked by difficult transitions and is complicated by a history of placement experiences. Studies show that most young people who age out of placement care are enrolled in school just before they leave, but this proportion drops drastically soon after they leave care. This transition has the potential to increase the risk of being involved in the justice system. Conversely, staying in school after placement has the potential to prevent contact with the justice system. To examine whether educational transitions during the process of leaving placement care in early adulthood influence the risk of justice system involvement. More precisely, the study focuses on whether leaving school increases this risk among youth aging out of care. It also uses moderation analyses to assess whether this association varies based on placement experiences, such as placement instability and group placement. We used a subsample of the EDJeP study from Québec, Canada, consisting of youths who participated in the third wave and who were in school before leaving placement (n = 413). Administrative data from youth protection services and data from the three waves of questionnaires were analyzed. We used maximum likelihood logistic regression models to predict justice system involvement during early adulthood as a function of leaving school. Interaction terms were used to determine whether moderation effects were present. The results show that young people who leave school when they age out of placement are at greater risk of being involved in the justice system during early adulthood (OR = 4.55, p < 0.001). Conversely, young people who stayed in school after aging out of care were less likely to be involved in the justice system during early adulthood. However, there were no significant moderation effects (p > 0.05) with the placement experiences. • Among youth enrolled in school before leaving care, our results suggest that a significant proportion (64 %) will leave school when they age out of care. • Compared with young people who stayed in school after aging out of care, those who left school were almost five times more likely to report being involved with the adult justice system in wave 3. • Our results highlight the need for policies and programs that support education among care leavers.
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 | 0,001 | 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 ».