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Record W2133328819 · doi:10.1002/bin.216

Sensitivity of passive approach during preference and reinforcer assessments for children with severe and profound intellectual disabilities and minimal movement

2006· article· en· W2133328819 on OpenAlexafffund
Sara Spevack, C. T. Yu, May S. Lee, Garry L. Martin

Bibliographic record

VenueBehavioral Interventions · 2006
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of ManitobaSt.Amant
FundersCanadian Institutes of Health ResearchU.S. Public Health ServiceResearch Manitoba
KeywordsPsychologyPreferenceReinforcementDevelopmental psychologySensitivity (control systems)Movement (music)Cognitive psychologyAudiologySocial psychologyStatisticsMedicine

Abstract

fetched live from OpenAlex

We evaluated the use of passive approach to assess preferences of two children, with severe and profound intellectual disabilities. Both children had physical challenges and exhibited minimal physical movement. We also compared the relative reinforcing effects of the identified high and low preference stimuli for a switch pressing response, and for a more passive looking response. High and low preference stimuli were identified for both children. Moreover, the high preference stimulus maintained higher rates of responding than the low preference stimulus for both children for the passive looking response, but not for switch pressing. The study extended the use of passive approach to assess preferences and identified the choice of target response as a potential limiting factor during reinforcer tests for these children.

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)0.005
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.226
GPT teacher head0.385
Teacher spread0.159 · 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

Citations12
Published2006
Admission routes2
Has abstractyes

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