Leaders’ Visions of Rehabilitation Services for Children in Ontario’s Schools: Effective Collaboration between Education and Health Sectors
Bibliographic record
Abstract
Leaders in Ontario’s district school boards (DSBs) and children’s treatment centers (CTC’s) share responsibility for rehabilitation therapy services in inclusive schools. Children with or at risk of disability rely on these services to enable their participation in learning and social environments. The aim of this study was to explore how leaders in DSBs, CTCs and the community envision effective collaboration in rehabilitation therapy services to advance collaboration in service of children with or at risk of disability and their families. Seven 90-minute online semi-structured focus groups were conducted involving a total of 36 education, community and health leaders and data were analyzed thematically. An eighth focus group comprised of representatives from the seven previous groups was conducted to validate the findings and develop recommendations. Three themes were established: collaboration is a relational and intentional process, forging a path forward to serve children with rehabilitation therapy needs, and leaders’ attributes needed to effect change. The participants recommended the following next steps: clarifying provincial standards for services including roles of all partners, knowledge building within schools, and utilizing existing evidence-based tools. A shared vision of rehabilitation therapy services is needed for effective collaboration between health and education sectors. Future research should involve leaders from health and education sectors, with parents, children and other partners in co-designing, implementing, and evaluating rehabilitation services in schools.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".