Transition Readiness Among Youth Accessing Mental Health Services With Physical Health Co‐Morbidities
Notice bibliographique
Résumé
BACKGROUND: Transition readiness, or skills and preparation for navigating adult health care, is an important factor in the successful transition from child and adolescent mental health services (CAMHS) to adult care; however, predictors of transition readiness are not fully understood. One factor which may impact transition readiness among youth accessing CAMHS is the presence of a co-occurring physical health condition; however, this has not been previously examined. Within a cohort of youth receiving CAMHS, the objective of this study was to understand if there is an association between co-occurring physical health conditions and transition readiness and if this relationship is impacted by severity of mental health symptoms. METHODS: This study was a secondary analysis of baseline data from the Longitudinal Youth in Transition Study, including 237 16- to 18-year-old youth accessing outpatient CAMHS from four different clinical sites. Participants completed self-report measures on mental health symptoms, functioning, service use, transition readiness, and physical health conditions. Multiple linear regression models were used to measure the association between the presence of health conditions and transition readiness scores as well as determine if there was an interaction between mental health symptoms and physical health conditions to predict transition readiness. RESULTS: Co-occurring physical conditions were reported by 41% of youth and were associated with greater overall transition readiness. There was no interaction between mental health symptom severity and co-occurring physical conditions, though attention problems were independently associated with lower transition readiness scores. CONCLUSIONS: Youth accessing CAMHS who have a co-occurring physical condition have overall greater transition readiness than youth without a co-occurring condition. Further research should explore the role of frequency and types of healthcare encounters in transition readiness for transition age youth needing ongoing mental health care to better understand how to support self-management and care navigation skill development.
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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,004 | 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 ».