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Record W3147725722 · doi:10.1177/10731911211003967

Evaluating the Factor Structure and Criterion Validity of the Canadian Little DCDQ: Associations Between Motor Competence, Executive Functions, Early Numeracy Skills, and ADHD in Early Childhood

2021· article· en· W3147725722 on OpenAlexaboutno aff
Kesha Hudson, Michael T. Willoughby

Bibliographic record

VenueAssessment · 2021
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
FundersInstitute of Education Sciences
KeywordsPsychologyDevelopmental psychologyCompetence (human resources)Criterion validityPopulationMotor skillEarly childhoodExecutive functionsConstruct validityClinical psychologyPsychometricsCognitionPsychiatrySocial psychologyMedicine

Abstract

fetched live from OpenAlex

The Canadian Little Developmental Coordination Disorder Questionnaire (Little DCDQ-CA) is a parent-report screening instrument that identifies 3- to 4-year-old children who may be at risk for Developmental Coordination Disorder (DCD). We tested the factor structure and criterion validity of the Little DCDQ-CA in a sample of preschool-aged children in the United States ( N = 233). Factor analysis indicated that the DCDQ-CA was best represented by one factor. Using cutoff scores that were proposed by the developer, 45% of the sample was identified as at-risk for DCD. Although a much larger percentage of children was identified as at-risk than would be expected based on the prevalence of formal DCD diagnoses in the population, the Little DCDQ-CA demonstrated good criterion validity. Specifically, compared with their peers, children who exceeded the at-risk criterion demonstrated worse motor competence, executive functioning skills, and early numeracy skills and were rated as having greater ADHD behaviors by their teachers, all consistent with expectations for children who are at risk for DCD. Results are discussed as they relate to future use of the Little DCDQ-CA.

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)0.000
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 score0.015
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.344
Teacher spread0.309 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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