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Record W2944397239 · doi:10.1111/jar.12607

Effects of intervention intensity on skill acquisition and task persistence in children with Down syndrome

2019· article· en· W2944397239 on OpenAlexaff
Nicole Neil, Emily A. Jones

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2019
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsWestern University
FundersProfessional Staff Congress and City University of New York
KeywordsIntervention (counseling)Task (project management)Persistence (discontinuity)Session (web analytics)Intensity (physics)PsychologyDevelopmental psychologyAffect (linguistics)AudiologyMedicineComputer scienceCommunicationPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Modifying intensity is one approach to tailoring intervention to meet the needs of learners with developmental disabilities. This study examined the effects of varying intensity levels of a behaviour analytic intervention on the efficiency of acquisition and task persistence in young children with Down syndrome. METHODS: Using adapted alternating treatment designs, three children were taught expressive language targets when three aspects of the dose of intervention intensity varied: number of opportunities, spacing of opportunities and session duration. RESULTS: Children acquired targets faster in conditions in which the spacing of opportunities was shorter than conditions in which the spacing was longer. Two children showed greater expression of positive affect in moderate levels of intensity. Children showed idiosyncratic differences in off-task behaviour. DISCUSSION: This research suggests that pacing of opportunities may be an important for understanding acquisition outcomes in a behaviour analytic approach to intervention for communication among young children with Down syndrome.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.202
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.097
GPT teacher head0.349
Teacher spread0.252 · 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 teacher head, 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

Citations10
Published2019
Admission routes1
Has abstractyes

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