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Record W2048154027 · doi:10.3104/reports.298

Gross motor skill acquisition in adolescents with Down syndrome

2006· article· en· W2048154027 on OpenAlexafffund
Sarah Meegan, Brian K. V. Maraj, Daniel J. Weeks, Romeo Chua

Bibliographic record

VenueDown Syndrome Research and Practice · 2006
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyGross motor skillMotor skillNonverbal communicationMultiple baseline designAudiologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to assess whether verbal-motor performances deficits exhibited by individuals with Down syndrome limited their ability to acquire gross motor skills when given visual and verbal instruction together and then transferred to either a visual or verbal instructional mode to reproduce the movement. Nine individuals with Down syndrome (6 males, 3 females) performed 3 gross motor skills. Both visual and verbal instructional guidance was given to the participants over a 4-day period. Twenty-four hours later, the participants were video recorded as they produced the movements (used as baseline measures). On Day 6, they were randomly assigned into verbal and visual groups and required to reproduce the skills while the experimenter provided either visual demonstration or verbal instructions depending on the group. Based on skill performance scores, participants in the verbal-motor performance group demonstrated a lower level of proficiency and an increased number of performance errors when compared to participants in the visual-motor performance group. Moreover, while the visual group demonstrated an increase in performance levels compared to baseline measures, the opposite effect was seen for the verbal group.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.347
Teacher spread0.320 · 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.

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

Citations31
Published2006
Admission routes2
Has abstractyes

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