Implementation of the C-BiLLT, an accessible instrument to assess language comprehension in children with limited motor and speech function: an international clinician survey
Bibliographic record
Abstract
This study assessed implementation of the Computer-based Instrument for Low-motor Language Testing (C-BiLLT). The C-BiLLT is an accessible language comprehension assessment tool originally developed for children with cerebral palsy and complex communication needs. The purpose of the current study was to understand the clinical contexts in which the C-BiLLT is used in the Netherlands, Belgium, and Norway and assess barriers and facilitators to implementation. An online survey was distributed to rehabilitation clinicians working in the Netherlands, Dutch-speaking parts of Belgium, and Norway. A total of 90 clinicians reported their training in and use of the C-BiLLT; assessed its acceptability, appropriateness, and feasibility; and commented on perceived barriers as well as advantages of the tool. Acceptability, appropriateness, and feasibility were all rated highly. The C-BiLLT was used with various populations and age groups but most often with children who were younger than 12 years of age, and those with cerebral palsy. The main implementation facilitator was clinicians' motivation; the main barriers were related to resources and complexity of cases. Findings suggest implementation of new assessment tools is an ongoing process that should be monitored following initial training, in order to understand clinical contexts in which the tools are being used.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".