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Record W6902179215 · doi:10.6084/m9.figshare.21776124

A scoping review of the playground experiences of children with AAC needs*

2022· article· en· W6902179215 on OpenAlexaff

Bibliographic record

VenueFigshare · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsMcGill University
Fundersnot available
KeywordsAugmentative and alternative communicationQualitative researchData collectionAugmentativeInclusion (mineral)Assistive technologySpecial needs

Abstract

fetched live from OpenAlex

Unstructured play on playgrounds is beneficial to children’s development, but children with disabilities are often unable to use playgrounds in the same ways as their peers without disabilities. No research to date has focused exclusively on the playground experiences of children who use augmentative and alternative communication (AAC). Therefore, in this scoping review, information from 10 studies published between 1990 and 2020 that investigated the playground experiences of children with disabilities, including those with limited speech, is synthesized. Included studies used experimental or non-experimental designs and involved the collection of either quantitative or qualitative data. The findings indicate that children with limited speech have diverse playground experiences and can benefit in some of the same ways as children with typical development from playground play but that they encounter barriers to participation that go beyond a lack of physical access. Additional research focusing specifically on understanding the communication experiences of children who use AAC on playgrounds is essential to address the complex issues associated with playground participation, including access to aided AAC systems on the playground. To foster more inclusive playgrounds, accessibility standards must address the unique needs of children with limited speech to support participation and access to communication on the playground.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.853

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1480.000

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 head0.125
GPT teacher head0.436
Teacher spread0.311 · 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
Published2022
Admission routes1
Has abstractyes

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