International Survey of Speech-Language Pathologists’ Practices in Working with Children with Autism Spectrum Disorder
Bibliographic record
Abstract
OBJECTIVE: Autism spectrum disorder (ASD) is a complex neurodevelopmental impairment. To better understand the role of speech-language pathologists (SLPs) in different countries in supporting children with ASD, the International Association of Logopedics and Phoniatrics (IALP) Child Language Committee developed a survey for SLPs working with children or adolescents with ASD. Method and Participants: The survey comprised 58 questions about background information of respondents, characteristics of children with ASD, and the role of SLPs in diagnosis, assessment, and intervention practices. The survey was available in English, French, Russian, and Portuguese, and distributed online. RESULTS: This paper provides a descriptive summary of the main findings from the quantitative data from the 1,114 SLPs (representing 35 countries) who were supporting children with ASD. Most of the respondents (91%) were experienced in working with children with ASD, and the majority (75%) worked in schools or early childhood settings. SLPs reported that the children's typical age at diagnosis of ASD on their caseload was 3-4 years, completed mostly by a professional team. CONCLUSIONS: The results support positive global trends for SLPs using effective practices in assessment and intervention for children with ASD. Two areas where SLPs may need further support are involving parents in assessment practices, and supporting literacy development in children with ASD.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".