Perspectives of Indian Speech-Language Pathologists on Implementing Augmentative and Alternative Communication Systems for Individuals with Nonverbal Autism Spectrum Disorder
Bibliographic record
Abstract
The implementation of Augmentative and Alternative Communication (AAC) systems for individuals with nonverbal Autism Spectrum Disorder (nvASD) remains highly debated, especially in a multilingual and multicultural country like India. Existing AAC guidelines are largely Western-based and may not be fully applicable in the Indian context. This study explored the perspectives of Indian Speech-Language Pathologists (SLPs) on AAC implementation in individuals with nvASD. A cross-sectional study was conducted in two phases. Phase 1 involved developing and validating a questionnaire assessing SLP's practices and attitudes towards AAC. In phase 2, the validated questionnaire was administered online to 93 Indian SLPs (29.23 years of mean age, 71% female) represented diverse clinical, educational, and academic settings. The survey explored key parameters, including AAC candidacy, cultural and linguistic adaptation, AAC selection and customization, interdisciplinary collaboration and caregiver involvement, outcome based evaluation practices, speech-AAC integration, and barriers to AAC implementation. Most SLPs emphasized working on communication prerequisites before introducing AAC. Vocabulary selection was prioritized over partner training. AAC was primarily used for both intervention and communication, employing goal-oriented approaches alongside other speech and language strategies. Challenges included lack of resources, inadequate training, and limited interdisciplinary collaboration. Parental involvement and caregiver feedback were identified as critical to success. SLPs in India broadly recognize AAC as a valuable tool for individuals with nvASD but report facing multiple systemic and practical challenges to its implementation. The findings underscore the need for culturally and contextually relevant AAC guidelines, targeted clinician training, and supportive policy measures to improve access and long-term communication outcomes in Indian settings.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".