85 Implementation of the collaborative care pathway: A consultation and referral pathway between a paediatric hospital’s Early Relational Health program and a local school board
Notice bibliographique
Résumé
Abstract Background Children exhibiting challenging mental health (MH) and/or behavioural concerns at school can lead to higher stress among staff, students and their families, and sometimes resulting in suspension or expulsion. Evidence-based MH consultation models aim to help school staff and expert clinicians collaborate in managing children with MH and/or behavioural concerns and ensure these young children receive the right care at the right time. In 2022, MH providers from a paediatric hospital who specialize in early childhood MH and a local school board’s Early Years (EY) team established the Collaborative Care Pathway (CCP) - a case consultation model to help manage children under age six with challenging MH and/or behavioural concerns at schools. Objectives We conducted an evaluation of the CCP’s first two years of implementation to determine the CCP’s uptake, EY team satisfaction and explore the facilitators and barriers of the CCP's implementation. Design/Methods The CCP aimed to meet monthly during the first two years of implementation (2022 to 2024). With caregiver consent, the EY team presented cases of children (average 5.29 years) exhibiting MH/behavioural concerns at school to specialist clinicians who provided recommendations (e.g., class-based interventions, referral to MH services, etc.). Pathway uptake was determined by the number of children reviewed at each meeting, service recommendations and outcomes. A satisfaction survey and a focus group were completed in June 2023 to determine the CCP’s facilitators and barriers. Results The CCP completed 12 meetings whereby 28 total cases were reviewed with 14 referred to the EC team for further assessment. EY staff identified many benefits to the model, including improvements in family and staff’s ability to manage the child’s behaviours, perceived behavioural improvements in the child at school, and bridging a gap between home, school and services. Staff desired more frequent meetings and extending the age limit. Additionally, the EY team expressed interest in additional training/resources on attachment and trauma to support these families. To mitigate this gap, in 2024, CHEO clinicians provided school staff Teachability Factor Training which focuses on student-teacher relationships to address challenging MH and/or behavioural concerns in schools. Post-training participants expressed the new skills will help them understand their students better. Conclusion The CCP's implementation has resulted in good uptake and outcomes after two years. The clinicians’ ongoing support and expertise enhanced the school staff’s knowledge and skills for supporting these children in class and supporting their families. This consultation model mitigates a gap in service delivery for young children by effectively connecting them with resources when a concern is identified. This unique collaboration between a school board and MH service should be explored in other jurisdictions.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».