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Record W6939691100 · doi:10.6084/m9.figshare.27901707

Voices from the field: exploring service providers’ insights into service delivery and AAC use in Canada

2024· article· en· W6939691100 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
Fundersnot available
KeywordsAugmentative and alternative communicationService delivery frameworkService providerThematic analysisFocus groupService (business)Service design

Abstract

fetched live from OpenAlex

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 <i>Right</i> 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.189
GPT teacher head0.371
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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