MétaCan
Menu
Back to cohort
Record W2949994953 · doi:10.1177/0022466919852340

Training Car Wash Skills to Chinese Adolescents With Intellectual Disability and Autism Spectrum Disorder in the Community

2019· article· en· W2949994953 on OpenAlexaff
Gabrielle T. Lee, Yunhuan Pu, Sheng Xu, Michelle W. Lee, Hua Feng

Bibliographic record

VenueThe Journal of Special Education · 2019
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyVideo modelingAutismAutism spectrum disorderTask (project management)Intellectual disabilityVocational educationTraining (meteorology)Social skillsDevelopmental psychologyApplied psychologyTeaching methodModellingPedagogyPsychiatry

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the effects of video modeling and visual task analysis on the acquisition, maintenance, and engagement of washing cars for three Chinese adolescents with intellectual disability and autism spectrum disorder. Video-based training was conducted in the conference room of a university-affiliated autism research center in China, and the hands-on training using visual task analysis took place in a local car wash. Three male adolescents (16–19 years old) participated in this study. A multiple probe across four tasks design was used. Results indicated that the training was effective in increasing independent and accurate responses of car wash tasks for all participants, and two of the three participants had a relatively high level of task engagement after the training. The acquired skills and improved task engagement were maintained for up to 6 months without practice. Implications in vocational skills training for Chinese adolescents in the community are discussed.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.023
GPT teacher head0.317
Teacher spread0.294 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Special EducationSame topicAutism Spectrum Disorder ResearchFrench-language works237,207