Clinician perspectives on implementing the C-BiLLT-CAN for non-speaking children with cerebral palsy: a focus group study
Bibliographic record
Abstract
PURPOSE: Currently available methods may not reliably assess language comprehension in children with significant speech and motor limitations. The Computer-Based instrument for Low motor Language Testing (C-BiLLT) is a standardized assessment designed for children with cerebral palsy that allows them to participate using various alternative response methods. This study aimed to understand speech-language pathologists' and occupational therapists' perceived facilitators and barriers to implementing the Canadian C-BiLLT (C-BiLLT-CAN). MATERIALS AND METHODS: Six focus groups were conducted with 30 clinicians. Transcripts were analyzed using a semi-deductive thematic analysis. The Consolidated Framework for Implementation Research was used to guide the identification of clinicians' perceived facilitators and barriers. RESULTS: Clinicians unanimously reported interest in implementing the C-BiLLT-CAN. Facilitators and barriers were classified into five primary themes. Key facilitators related to the test's evidence-based design, standardized nature, and potential flexibility. Key barriers related to Internet connectivity, the need to expand customization and response options to meet a greater breadth of needs, privacy policies, lack of resources, and perceived costs associated with equipment, training, and time. CONCLUSIONS: Many perceived barriers aligned with previous European and Canadian C-BiLLT implementation research. However, findings elucidated unique considerations that will inform adaptations to the C-BiLLT-CAN and development of training/educational materials.
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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 | 0.030 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".