Evaluation of Efforts to Support Students With Type 1 Diabetes in Ontario Schools: Survey of Parents
Bibliographic record
Abstract
OBJECTIVES: Children and youth with type 1 diabetes (T1D) must have support to manage their condition while at school. In 2017, Ontario students and their parents were surveyed to assess the level of school support and the extent to which that support met perceived needs. In 2018, a provincial policy was established, providing high-level guidance regarding children with T1D in school. We redistributed our survey in 2023 to determine whether support for children with T1D has improved and where gaps remain. METHODS: An online survey was circulated to patients and families through the 35 pediatric diabetes education centres in the Ontario Pediatric Diabetes Network in 2017 and 2023. Survey responses were collected via REDCap software. Results were analyzed using descriptive statistics and the Pearson chi-square test. The Mann-Whitney U test was used to compare satisfaction with school support. RESULTS: A total of 1,060 responses were received in 2017, and 437 responses in 2023. Between the 2 time points, respondents reported increased use of individual care plans, continuous glucose monitoring, and improved management of hypoglycemia at school. There was no improvement in support for blood glucose monitoring or insulin administration. Overall, there was no increase in satisfaction with school support. Importantly, 37% of caregivers stopped work related to diabetes care at school. CONCLUSIONS: School support for children with T1D has improved in specific domains. However, gaps remain, and many families remain adversely affected by lack of support in school. Our findings suggest a need for ongoing advocacy to address care gaps.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".