The use of non-speech oral-motor exercises among Indian speech-language pathologists to treat speech disorders: An online survey
Bibliographic record
Abstract
OBJECTIVE: Previous surveys in the United States of America (USA), the United Kingdom (UK), and Canada have indicated that most of the speech-language pathologists (SLPs) tend to use non-speech oral-motor exercises (NSOMEs) on a regular basis to treat speech disorders.At present, there is considerable debate regarding the clinical effectiveness of NSOMEs. The current study aimed to investigate the pattern and extent of usage of NSOMEs among Indian SLPs. METHOD: An online survey intended to elicit information regarding the use of NSOMEs was sent to 505 members of the Indian Speech and Hearing Association. The questionnaire consisted of three sections. The first section solicited demographic information, the second and third sections solicited information from participants who did and did not prefer to use NSOMEs, respectively. Descriptive statistics were employed to analyse the responses that were clinically relevant. RESULTS: A total of 127 participants responded to the survey. Ninety-one percent of the participants who responded to the survey indicated that they used NSOMEs. CONCLUSION: The results suggested that the percentage of SLPs preferring to use NSOMEsis similar to the findings of surveys conducted in the USA, the UK, and Canada. The Indian SLPs continue to use NSOMEs based on a multitude of beliefs. It is important for SLPs to incorporate the principles of evidence-based practice while using NSOMEs to provide high quality clinical care.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".