Comparing the autism service needs and priorities of Indigenous and newcomer families in Canada: Qualitative insights
Bibliographic record
Abstract
Background Indigenous Peoples and newcomers are two of the largest and fastest growing populations in Canada (Statistics Canada, 2022; Zimonjic, 2022). Yet despite this, little is known about their experiences navigating and accessing autism services for their children. Method To address this gap, the current study sought to explore the autism service needs and priorities of Indigenous and newcomer families in Canada. A total of 19 participants (9 Indigenous and 10 newcomer caregivers) were selected using purposive sampling to participate in a semi-structured interview. Results The findings revealed that Indigenous families prioritized the need for services in rural and remote areas, tailored information to their needs, and support preserving their cultural heritage. On the other hand, newcomer families emphasized the importance of peer support, quality standards for services and therapies, and support during transition periods. Similarities across both groups indicated the need for addressing the lengthy waitlists for services, which have also been reported in the general population in Canada, having services and professionals place a greater emphasis on the child’s strengths, and culturally safe services and practice. Conclusion The present findings have important implications for the design and implementation of services and supports which reflect the needs and priorities of underserved communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".