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Record W164592590

Using an iPad-presented social story to increase on-task behaviours of a young child with Autism

2012· article· en· W164592590 on OpenAlexaff
Julianne Michelle Vandermeer, Todd Milford, Wendi Beamish, Wayne Terrence Lang

Bibliographic record

VenueGriffith Research Online (Griffith University, Queensland, Australia) · 2012
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAutismIntervention (counseling)GirlPsychologyTask (project management)Developmental psychologyAffect (linguistics)Subject (documents)Social skillsComputer scienceCommunicationWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

Social stories have been widely used to help children with autism understand how to act in a particular situation. A single subject design was used to investigate the effectiveness of presenting a social story on an iPad to increase the on-task behaviours of a high functioning five-year-old girl with autism. The intervention was undertaken in a one-on-one situation within an autism-specific preparatory classroom. Analysis of video data collected over six weeks indicated that this intervention was successful in increasing the subject's rate of attention to teacher and materials. Improvements in affect around time-with-teacher were also noted. This study adds to the efficacy of using social stories with young children with autism. Additional research is warranted to explore the viability of the iPad as an intervention tool to promote early learning for young children with and without exceptionalities.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.210
GPT teacher head0.411
Teacher spread0.201 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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