Incorporating Therapy into the Regular Curriculum: Working Together with Occupational Therapists
Bibliographic record
Abstract
Abstract Over the past three decades, there has been considerable change in the education of children with disabilities in Canada. Children with developmental disabilities attend inclusive classrooms and are educated alongside their non-disabled peers, receiving services and supports to optimize their participation (Hutchinson, 2014; Slee, R. (2001). Social justice and the changing directions in educational research: The case of inclusive education. International Journal of Inclusive Education, 5(2), 167–177). In Canada, occupational therapists have provided services in schools for over three decades with the aim of supporting participation of children with disabilities (Graham, D. R., Kennedy, D., Phibbs, C., & Stewart, D. (1990). Position paper on occupational therapy in schools. Canadian Journal of Occupational Therapy, 57(4), 1–6; Reid, Chiu, Sinclair, Wehrmann, & Naseerl, 2006). This chapter presents examples from case study research conducted in Ontario, Canada, on the delivery of school-based occupational therapy (SBOT) for two young children (focal participants) with developmental disabilities. Case study research was used to describe the nature of SBOT service delivery from multiple stakeholder perspectives. Data were gathered over the duration of one school year using a combination of observation, document analysis, and interviews involving participants directly involved in the delivery of SBOT with each focal participant. Common characteristics in these two cases enabled cross-case analysis to identify features of collaborative working that facilitated educational programming and outcomes for students with developmental disability.
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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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".