Exploring the Need for Speech and Language Therapy Services in Turkish Schools: A Comparative Analysis
Bibliographic record
Abstract
Speech and language therapy (SLT) services are vital in assisting children with speech, language, or communication disorders. However, various barriers limit the provision of this service in an equal and adequate manner. This study examines these barriers in the UK, the USA, Canada, Australia, and Türkiye and explores how school-based speech and language therapies contribute to removing these barriers in these countries. Key barriers, including workforce shortages, equity, accessibility, geographical and financial constraints, and lengthy waiting lists, impact the quality and provision of SLT services. Embedding SLT services within the school may provide timely, targeted, and sustainable interventions, thereby improving accessibility and availability. A qualitative comparative method was employed to analyze service provision in the five countries and identify differences and similarities in barriers to accessing SLT services. The findings indicate that while improvements are needed in all countries, Türkiye faces unique challenges that require comprehensive reforms, as there are relatively few speech and language therapists. The service is not accessible for many children, which perpetuates these barriers. Establishing school-based SLT services and integrating relevant training into teacher education curricula should be prioritized, as this will equip future educators with the knowledge and competencies for their active support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".