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Record W2118727003 · doi:10.1352/1934-9556-51.6.458

Effectiveness of Responsive Teaching With Children With Down Syndrome

2013· article· en· W2118727003 on OpenAlexaff
Özcan Karaaslan, Gerald Mahoney

Bibliographic record

VenueIntellectual and developmental disabilities · 2013
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsDown syndromePsychologyDevelopmental psychologyRandomized controlled trialTurkishCurriculumIntelligence quotientChild developmentTreatment and control groupsAffect (linguistics)Clinical psychologyMedicinePsychiatryCognition

Abstract

fetched live from OpenAlex

A randomized control study was conducted to evaluate Responsive Teaching (RT) with a sample of 15 Turkish preschool-aged children with Down syndrome (DS) and their mothers over a six-month period of time. RT is an early intervention curriculum that attempts to promote children's development by encouraging parents to engage in highly responsive interactions with them. Subjects were randomly assigned to treatment conditions: the control group consisted of standard preschool classroom services; the RT group received bi-weekly RT parent-child sessions in addition to standard services. RT mothers made significantly greater increases in their Responsiveness and Affect as wellas decreases in Directiveness than control group mothers. There were also significant group differences in children's interactive engagement and development. Children in the RT group improved their developmental quotient scores by an average of 47% compared to 7% for children in the control group. Results are described in terms of the effects of parental responsive interaction on the developmental functioning of children with DS.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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
Published2013
Admission routes1
Has abstractyes

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