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Record W2899421441 · doi:10.1177/0198742918806925

Effects of PECS on the Emergence of Vocal Mands and the Reduction of Aggressive Behavior Across Settings for a Child With Autism

2018· article· en· W2899421441 on OpenAlexaff
Xiaoyi Hu, Gabrielle T. Lee

Bibliographic record

VenueBehavioral Disorders · 2018
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsWestern University
Fundersnot available
KeywordsAutismMultiple baseline designIntervention (counseling)PsychologyDevelopmental psychologyVocal communicationAggressionCommunicationPsychiatry

Abstract

fetched live from OpenAlex

Effective strategies to address communication and behavior challenges are critical in early intervention programs. The purpose of this study was to investigate the effects of the Picture Exchange Communication System (PECS) on vocal mands and aggressive behavior displayed by a child with autism in China. One 4-year-old boy with autism participated in this study. The experimental design was a multiple baseline across three settings. The PECS intervention involved the first three phases described in the PECS manual. The results indicated that PECS effectively increased vocal mands and decreased aggressive behavior maintained by access to preferred items in all three settings. The results also suggested that vocal mands were potentially controlled by pictures in the PECS book. One week following the completion of the intervention, the child maintained the PECS exchanges at a high level with increased vocal mands. His aggressive behavior remained at almost zero occurrences. Results of this study have important implications to early intervention educators working with children with autism.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.018
GPT teacher head0.323
Teacher spread0.305 · 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 designNon-randomized trial
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

Citations20
Published2018
Admission routes1
Has abstractyes

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