Tutorial: Speech Assessment for Multilingual Children Who Do Not Speak the Same Language(s) as the Speech-Language Pathologist
Bibliographic record
Abstract
PURPOSE: The aim of this tutorial is to support speech-language pathologists (SLPs) undertaking assessments of multilingual children with suspected speech sound disorders, particularly children who speak languages that are not shared with their SLP. METHOD: The tutorial was written by the International Expert Panel on Multilingual Children's Speech, which comprises 46 researchers (SLPs, linguists, phoneticians, and speech scientists) who have worked in 43 countries and used 27 languages in professional practice. Seventeen panel members met for a 1-day workshop to identify key points for inclusion in the tutorial, 26 panel members contributed to writing this tutorial, and 34 members contributed to revising this tutorial online (some members contributed to more than 1 task). RESULTS: This tutorial draws on international research evidence and professional expertise to provide a comprehensive overview of working with multilingual children with suspected speech sound disorders. This overview addresses referral, case history, assessment, analysis, diagnosis, and goal setting and the SLP's cultural competence and preparation for working with interpreters and multicultural support workers and dealing with organizational and government barriers to and facilitators of culturally competent practice. CONCLUSION: The issues raised in this tutorial are applied in a hypothetical case study of an English-speaking SLP's assessment of a multilingual Cantonese- and English-speaking 4-year-old boy. Resources are listed throughout the tutorial.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".