Autism, Stigma, and South Asian Immigrant Families in Canada
Bibliographic record
Abstract
Considerable empirical evidence suggests early recognition of autism and access to support result in long-term positive outcomes for children and youth on the spectrum and their families. However, children of racialized families are often diagnosed at later ages, are more likely to be misdiagnosed, and experience many barriers to service access. There is also a paucity of research exploring the experiences of parents from specific immigrant groups caring for their children on the spectrum in Canada, many of whom identify as members of racialized communities. As such, the main aim of the study was to examine how South Asian immigrant parents in Canada are experiencing available care programs and support. Another aim was to examine their perceptions of social stigma associated with autism. We conducted an inductive thematic analysis of qualitative data from nine interviews with South Asian parents living in Ontario, Canada. Findings confirmed barriers to an autism diagnosis and to service access. Additionally, parents reported pronounced autism stigma, which enacted impediments to timely diagnosis, service access, and health-promoting behaviors. Findings also revealed that parents experience considerable caregiver stress and psychological distress. The generated evidence is anticipated to inform equitable policy, programming, and practices that better support the needs of children on the spectrum and their immigrant families.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".