Exploring Assessment of Situational Intelligibility in Children with and without Speech-Language Disorders
Bibliographic record
Abstract
Parents of young children typically respond to their child’s communications with language that is syntactically and semantically appropriate to what the child said. However, parents of children with speech-language disorders (SLD) may not always understand their child and so may not respond to the child’s communication attempts in a language-promoting manner. The goal of the present study is to explore the use of a new questionnaire about children’s situational intelligibility, the Caregiver/Parent Understanding-the-Child Questionnaire (CPUCQ). The CPUCQ asks parents to respond to 31 common communication situations, ranging from saying “Thank you” to telling a story. Parents indicate the child’s mode of communication in each situation (Speech only, Speech plus gesture, Gesture only, or Child does not do this), and they also rate how well they understand the child. The CPUCQ and various language measures were administered to 54 typically developing (TD) children and 34 children with SLD in the age range 2;0 – 6;8. Both groups showed developmental change over the age range in the mode of communication and in the understandability ratings. Further study of the CPUCQ as an assessment instrument is warranted, particularly as it may provide valuable information that can be used clinically to determine treatment goals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".