A Case Study on Vocal Loudness With a Young Adult With Autism Spectrum Disorder and Developmental Delay
Bibliographic record
Abstract
PURPOSE: This clinical focus article describes an exploratory case study addressing reduction of vocal loudness in a young adult with a history of autism spectrum disorder and developmental delay. The need for a short-term pullout individual intervention arose from his participation in the Advancing Language and Literacy group, a program that provides support for enhancement of speech, language, and literacy skills for young adults with developmental disabilities. METHOD: The participant attended individual treatment sessions weekly for 9 sessions, all of which were digitally audio-recorded. Client awareness of different loudness levels was first established using nonspeech sounds and speech samples. Loudness in spontaneous speech was monitored with support of verbal and visual feedback in the individual sessions and tracked in individual sessions as well as sessions of the Advancing Language and Literacy group, which served as a generalization context. RESULTS: In the individual sessions, the participant's spontaneous production of acceptable volume improved from 42% to 92% of utterances. Observation of group participation indicated generalization, with the majority of utterances produced posttreatment at an acceptable volume. CONCLUSIONS: This clinical focus article emphasizes suprasegmental aspects as a dimension of communicative competence and highlights the need for continued intervention research and services for young adults with developmental disabilities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".