Assessing language comprehension in motor impaired children needing AAC: validity and reliability of the Norwegian version of the receptive language test C-BiLLT
Bibliographic record
Abstract
Children with severe motor impairments who need augmentative and alternative communication (AAC) comprise a heterogeneous group with wide variability in cognitive functioning. Assessment of language comprehension will help find the best possible communication solution for each child, but there is a lack of appropriate instruments. This study investigates the reliability and validity of the Norwegian version of the spoken language comprehension test C-BiLLT (computer-based instrument for low motor language testing) - the C-BiLLT-Nor - and whether response modality influences test results. The participants were 238 children with typical development aged 1;2 to 7;10 (years/months) who were assessed with the C-BiLLT-Nor and tests of language comprehension and non-verbal reasoning. There was excellent internal consistency and good test-retest reliability. Tests of language comprehension and non-verbal reasoning correlated significantly with the C-BiLLT-Nor, indicating good construct validity. Factor analysis yielded a two-factor solution, suggesting it as a measure of receptive vocabulary, grammar, and overall language comprehension. No difference in results could be related to response mode, implying that gaze pointing is a viable option for children who cannot point with a finger. The C-BiLLT-Nor, with norms from 1;6-7;6 is a reliable measure of language comprehension.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".