Access to autism spectrum disorder services for marginalized families in Canada: a scoping review protocol
Bibliographic record
Abstract
ABSTRACT OBJECTIVE: The objective of this scoping review is to assess the extent of the literature on racially or ethnically marginalized families’ access to, utilization, and experiences with ASD services for their child in Canada. Our review will summarize key findings, compare the reported experiences of different marginalized populations to identify similarities and differences, and highlight gaps in the present literature. This review will enable us to better understand facilitators and barriers to ASD service access/use, and thus provide evidence-based recommendations to improve Canadian service provision, policy, and research. INTRODUCTION: Navigating support services for a child with ASD can be challenging for any family, especially for families who belong to historically marginalized communities. Previous systematic reviews have identified common barriers immigrant and underserved parents face when accessing ASD services for their child – such as language barriers, limited health literacy, and unfamiliarity with the healthcare system. However, the experiences of racially or ethnically marginalized families with a child with ASD in Canada have not been comprehensively reviewed. INCLUSION CRITERIA: Qualitative literature describing the experiences of marginalized families (i.e., under-represented racial/ethnic groups, immigrant, refugee) in Canada with accessing and/or using ASD services for their child under 18 years of age will be included. METHODS: A comprehensive literature search in Ovid MEDLINE, Ovid EMBASE, CINAHL, APA PsycINFO, Scopus, and Web of Science will be conducted by a research librarian in August 2023. Studies will not be limited by publication date. English and French-language studies will be included. Following the search, title and abstract screening, then full-text screening will be conducted independently and in duplicate to assess eligibility of search results against the inclusion criteria. Disagreements will be resolved by consensus or adjudicated by a third reviewer. Finally, data will be extracted from included papers independently and in duplicate using a data extraction tool developed a priori. Included studies will also be critically appraised for methodological quality, independently and in duplicate, using the CASP qualitative checklist. All results will subsequently be presented in the final scoping review.
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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 | 0.063 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.005 |
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".