“My child is completely underestimated”: Canadian parents’ perspectives on implementing an accessible language comprehension assessment for non-speaking children with cerebral palsy
Bibliographic record
Abstract
PURPOSE: Reliable assessment of language comprehension is difficult for children with significant speech and motor limitations. The Computer-Based instrument for Low motor Language Testing (C-BiLLT) was designed for children with cerebral palsy (CP) and speech and motor limitations. A Canadian English version (C-BiLLT-CAN) has been validated. However, early investigation identified feasibility challenges necessitating further exploration. This study aimed to understand parents' perceived barriers and facilitators to implementing the C-BiLLT-CAN in the Canadian clinical context. MATERIALS AND METHODS: Seven focus groups were conducted synchronously online with 16 parents from five Canadian provinces/territories. Transcripts were analyzed using semi-deductive thematic analysis, framing results within the Consolidated Framework for Implementation Research (CFIR). RESULTS: Parents unanimously expressed interest in making the C-BiLLT-CAN clinically available. Facilitators and barriers were discussed under five themes. Key facilitators included the unique design, standardized nature, and potential flexibility of the C-BiLLT-CAN. Barriers involved the inability to accommodate all children, the potential for unintended assessment impacts, and clinics' readiness and willingness to prioritize implementation. CONCLUSIONS: This study contributes new knowledge surrounding the assessment needs of parents of children with CP and speech and motor limitations. Alongside findings from a parallel clinician study, results will inform adaptations to the C-BiLLT-CAN to facilitate implementation.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".