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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".