Nonspeech Oral Motor Exercises: Use and Knowledge of Speech-Language Pathologists Working with People with Speech Sound Disorders
Bibliographic record
Abstract
Previous studies, conducted in different countries (e.g. Australia, Canada, India, Republic of Ireland, USA, UK), have shown that speech-language pathologists (SLPs) use nonspeech oral motor exercises (NSOMEs) to treat speech sound disorders (SSDs), bringing attention to the substantial debate regarding the clinical effectiveness of NSOMEs. The aim of the present study was to investigate and characterize the use of NSOMEs by Portuguese SLPs in the intervention of SSDs, and to analyze the evidence that supports it. To do so, SLPs who provide therapy to children with SSDs were invited to complete an online questionnaire, based on a previous survey conducted in India by Thomas and Kaipa (2015). A total of 184 participants responded to the survey; 93.5% reported knowing about NSOMEs, 78.5% used NSOMEs in their intervention for SSDs, and 80.2% considered them effective in treating SSDs (89% indicated that their knowledge about NSOMEs was acquired through graduate and post-graduate courses; 98.5% reported that they used NSOMEs to improve the motor function of the articulators). This study offers an overview of Portuguese speech-language pathologists’ reported use of NSOMEs as part of the intervention of speech sound disorders in children. Many of the participants in this study reported that they did use NSOMEs in SSD treatments, regardless of the lack of evidence to support their use in this context. Furthermore, the results show that the percentage of SLPs in Portugal using NSOMEs is similar to those found in the USA, UK, Canada, and India, but different from those in Australia and Ireland.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".