Clinical Impressions of How Young Children Use AAC at Home and in Child Care Settings: A Canadian Perspective
Bibliographic record
Abstract
Abstract Speech-language pathologists, working in a multicultural, community-based environment for young children with special needs in Vancouver, Canada, collected information on 84 clients using AAC from a chart review. The speech-language pathologists collected additional usage information and attended a group interview to discuss barriers and facilitators of AAC. Thirty-one percent of the children were using AAC. Children aged between 16 and 72 months typically relied on multiple modes of communication, including sign, communication boards and binders, and low- and high-tech communication devices. All of the children used at least one type of unaided mode. Fifty-five percent used pictures or communication boards/displays, and 29% used technology with speech output. Similarities in usage of AAC were noted in home and child-care settings with increased use of unaided in homes and a slightly increased use of aided communication in child care settings. Speech-language pathologists reported that the time needed for AAC intervention as well as limited funding for high-tech devices continue to be major barriers. Additional research is needed to describe current AAC practices with young children particularly from minority linguistic and cultural backgrounds. Stakeholder input is needed to explore perceptions of children's usage of AAC in daily life with familiar and unfamiliar communication partners.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".