Features of culturally and linguistically relevant speech‐language assessments for Indigenous children: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Indigenous children may be at higher risk of being misdiagnosed with speech-language difficulties due to Eurocentric practices in health care and education. The use of conventional speech pathology assessment practices contributes to inappropriate disorder identification, further stigmatising a vulnerable population. Few resources are available for speech pathologists, which examine the cultural and linguistic relevance of assessments for this population. OBJECTIVE: To provide important features for speech pathologists to account for when building assessment plans for Indigenous children. DESIGN: This comprehensive scoping literature review was completed using the Arksey and O'Malley 6-step methodological framework, including the optional consultation exercise, and reported using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. To be included, studies needed to have been published since 2000, discuss speech-language assessments and involve a significant proportion of Indigenous participants under 7 years old. FINDINGS: Three features were extracted from 32 studies that discussed First Nations, Métis, Inuit, Native American, Aboriginal and Torres Strait Islander communities: using a battery of resources including alternative approaches, ensuring authenticity and cultural relevance, and considering a child's linguistic characteristics. CONCLUSION: While there remains a need to adapt according to a specific child's reality, this study provides a guideline for all allied health clinicians when they are building their culturally and linguistically relevant assessment plans.
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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 | 0.045 | 0.183 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.029 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 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".