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Record W2068307898 · doi:10.1177/1088357614532497

Transition to Kindergarten for Children With Autism Spectrum Disorder

2014· article· en· W2068307898 on OpenAlexafffund
Elizabeth Starr, Tanya S. Martini, Ben C. H. Kuo

Bibliographic record

VenueFocus on Autism and Other Developmental Disabilities · 2014
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsBrock UniversityUniversity of Windsor
FundersUniversity of Windsor
KeywordsEthnically diversePsychologyAutism spectrum disorderDevelopmental psychologyAutismContext (archaeology)Intervention (counseling)Transition (genetics)Cultural diversityFocus groupEthnic group

Abstract

fetched live from OpenAlex

Despite the stated importance of a successful kindergarten transition (TTK) for future school success, no research has addressed this transition for culturally/ethnically diverse families having children with autism spectrum disorders (ASD). To address this gap, six focus groups (three with ethnically diverse parents, one with kindergarten teachers, and one each with early childhood resource teachers and early intervention providers) were conducted to elicit the experiences of these stakeholders regarding TTK for children with ASD generally, and the TTK experience for ethnically diverse families specifically. Four major themes relating to TTK emerged from the focus groups: Relationship Building, Communication, Knowledge, and Support. While these themes were relevant for all groups, parents who were relatively recent immigrants and for whom English was not a first language identified unique difficulties. Results are discussed within the context of Bronfenbrenner’s Ecological Systems Theory. Recommendations to improve the experience for ethnically diverse families are explored.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.277
Teacher spread0.258 · 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

Citations59
Published2014
Admission routes2
Has abstractyes

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