Voices from the field: exploring service providers’ insights into service delivery and AAC use in Canada
Bibliographic record
Abstract
Use of augmentative and alternative communication (AAC) often relies on the involvement of AAC service providers; however little is known about how AAC services are delivered across Canada. This study aimed to explore AAC service provision and factors influencing use of AAC from the perspectives of service providers across Canada who are involved in providing and/or supporting use of AAC systems. The 22 participants from nine (of the 10) provinces participated in online focus groups. Participants were speech-language pathologists, occupational therapists, communicative disorders assistants, and a teacher. Transcripts of the audio recordings were analyzed using reflexive thematic analysis. Four themes were generated that reflect service-related factors contributing to the use of AAC in Canada: Support of Organizational Structures, Concordant Relationships and Goals, Making the Right Decisions, and Influence of Knowledge and Attitudes. These themes highlight how government systems, key stakeholders, assessment practices, and knowledge of AAC influence service provision and use of AAC. Voices from across Canada highlighted shared experiences of services providers as well as revealed variability in service delivery processes. The findings bring to attention a need for further research and development of service provision guidelines to support consistency, quality in practice, and equity in AAC services.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".