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Record W1984331758 · doi:10.3109/07434618.2013.849754

Exploring the Impact of Cognition on Young Children's Ability to Navigate a Speech-Generating Device

2013· article· en· W1984331758 on OpenAlexafffund
Manon Robillard, Chantal Mayer-Crittenden, Annie Roy‐Charland, Michèle Minor-Corriveau, Roxanne Bélanger

Bibliographic record

VenueAugmentative and Alternative Communication · 2013
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsLaurentian University
FundersHealth Canada
KeywordsCategorizationCognitionSet (abstract data type)PsychologyFlexibility (engineering)Cognitive flexibilityVocabularyCognitive psychologyCognitive skillDevelopmental psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This study examined the impact of cognition on young children's ability to navigate a speech-generating device (SGD) with dynamic paging. Knowledge of which cognitive factors impact navigational skills could help clinicians select the most appropriate SGD for children who have complex communication needs. A total of 65 typically developing children aged 48-77 months were assessed using the Leiter International Performance Scale-Revised (Leiter-R) and the Automated Working Memory Assessment (AWMA). Although significant correlations were found between the ability to navigate an SGD (using a taxonomic organization) and all cognitive factors except for cognitive flexibility, a stepwise linear regression revealed that sustained attention, categorization, and fluid reasoning were the most pragmatic set of factors to predict navigational skills. Future studies are needed to further understand the factors that impact children's navigational skills.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.204
GPT teacher head0.472
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2013
Admission routes2
Has abstractyes

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