Economic impacts of caring for autistic children in Ontario, Canada: report from a pilot study
Bibliographic record
Abstract
Introduction: Although research on the economic costs of autism is growing, relatively few studies have examined these costs incurred by families of autistic children in Canada. Methods: This study designed and piloted a survey to capture the broader economic impact of caring for autistic children, including direct and indirect costs. It also sought to gather preliminary data to inform a future full-scale survey and enhance understanding of autism's economic impact in the Canadian context. The pilot survey was developed through a systematic and iterative process involving a literature review, workshops, and focus group discussions. It was then distributed to families with autistic children in Ontario, Canada's most populous province. Results and discussion: A mixed-method analysis of survey responses revealed that financial challenges for these families often begin during the diagnostic process and continue with high out-of-pocket medical and therapy costs. Caregivers also face challenges accessing funding and appropriate support services, contributing to indirect costs such as increased living expenses, childcare, education, and training. Caregivers of autistic children in Ontario experience substantial and multifaceted challenges that are compounded by inadequate public support. Understanding the nature and extent of caregiver expenditures can inform more targeted and efficient policy responses in financial, informational, and practical autism-related support.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".