Type 1 Diabetes in Ontario Schools: Policy and Practice
Bibliographic record
Abstract
OBJECTIVES: Type 1 diabetes (T1D) is a challenging chronic condition. Young children with T1D require daily support to manage their condition while at school. In 2018, Ontario established a provincial policy to ensure safe and equitable school participation for children with diabetes. Despite this, children and parents describe very different school experiences. In this qualitative study we describe the interpretation and implementation of school board policy related to the care of children with T1D from the perspective of school educators (principals/teachers). METHODS: Policy documents were reviewed employing a qualitative descriptive research design using directed qualitative content analysis. Semistructured interviews were conducted with 13 teachers and principals from 10 publicly funded elementary schools across the Hamilton and Toronto District School Boards in 2021. RESULTS: There are major differences in how policies regarding T1D are being implemented in schools. This includes how school staff are educated about T1D, and how they interpret and act on blood glucose information. Although educators often play an active role in supporting children, many face barriers, including competing priorities, fear, lack of information, and lack of support. Facilitators include effective communication/collaboration, actionable information, time, and a diabetes "champion." In some instances, access to nursing support could help to resolve barriers or create care gaps. CONCLUSIONS: School board policy provides high-level guidance on how to support children with T1D in school, but gaps remain. We provide specific recommendations regarding policy, staff education/training, roles and responsibilities, and future research.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".