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Record W4385476420 · doi:10.1080/17483107.2023.2241515

Canadian manufacturer and technician perspectives on the design and use of augmentative and alternative communication technology

2023· article· en· W4385476420 on OpenAlexaffabout
Sonja Bonar, Seamus P. L. Burnham, Jillian T. Henderson, Beata Batorowicz, Shane D. Pinder, Tracy A. Shepherd, T. Claire Davies

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsQueen's University
Fundersnot available
KeywordsAugmentative and alternative communicationThematic analysisFlexibility (engineering)CandidacyProcess managementGovernment (linguistics)TechnicianFocus groupKnowledge managementBusinessComputer sciencePsychologyEngineeringQualitative researchMarketingManagementPolitical scienceSociology

Abstract

fetched live from OpenAlex

PURPOSE: Device manufacturers and technicians (MaTs) of augmentative and alternative communication (AAC) systems play key roles in the design and successful uptake of communication devices. This study aims to investigate MaT perspectives on AAC device design and effective use. MATERIALS AND METHODS: To investigate their perspectives, a focus group of MaTs within Canada was conducted. Reflexive thematic analysis was used to analyze data. FINDINGS: Three major themes resulted from analysis, which reflect MaT's views: AAC hardware and software flexibility, AAC knowledge and implementation, and social good versus financial resources. CONCLUSIONS: This study provides insights into the complexities faced by MaTs in balancing technical support of system end-users and the financial resources necessary for that support. These insights indicate a need for increased financial resources and the expansion of individuals who qualify for AAC system candidacy. MaTs suggest that an increase in resources and candidacy could lead to more successful AAC implementation and a greater understanding of AAC for all stakeholders.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.009
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.392
Teacher spread0.331 · 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 designObservational
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

Citations4
Published2023
Admission routes2
Has abstractyes

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