40 Disparities in Diagnosis of Neurodevelopmental Disabilities in Canadian Indigenous and Racialized Children
Bibliographic record
Abstract
Abstract Background A core racial injustice faced by Indigenous and racialized people in North America lies in access to healthcare services. It is especially important for those with neurodevelopmental disabilities (NDDs) to access appropriate diagnostic services so that they can receive appropriate and timely healthcare resources. While there is a significant amount of literature from the U.S. documenting later diagnoses of NDDs in certain racialized groups, it is unclear whether such trends have been documented in Canada. Objectives Our objective was to review the research on the diagnosis of NDDs in Indigenous and racialized children in Canada. Design/Methods We reviewed OVID, CINAHL, and Web of Sciences with a thorough two-step screening process for English papers from 1991 to 2021. We used the following search term categories: 1) diagnosis, 2) NDDs including autism spectrum disorder (ASD), fetal alcohol spectrum disorder (FASD), cerebral palsy (CP), and intellectual disabilities (ID), 3) Indigenous and racialized populations. Racialized populations included those such as immigrants, Black, Hispanic, Asian, and refugees living in Canada. Results In total, we found twelve papers. Nine were on ASD, one on CP, and two on NDDs in general. As well, out of the twelve papers, six were on immigrants, five on Indigenous children, and one on both Indigenous and immigrant children. Some of the studies on immigrant children distinguished the world regions they were from, one distinguished refugees from immigrants, while others did not specify. Studies discussed disparities in rates of diagnosis, explored possible explanations for these disparities, as well as potential solutions for Indigenous and racialized children to receive their NDD diagnosis in a timely manner. Conclusion There was a disproportionately large number of studies on FASD in Indigenous children, which may reflect a lack of appropriate representation of neurodiverse racialized and Indigenous children in Canadian NDD literature. Ultimately, these were excluded during screening, as they were not focused on FASD diagnosis but rather on other aspects such as prevention. The remaining studies do not fully reflect the wide variety of NDDs of Indigenous and racialized children. It is our hope that more research be done to represent all children in an equitable fashion and in doing so, shape policies and interventions to improve access to healthcare.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.029 | 0.042 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".