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Record W3182408614 · doi:10.1080/1091367x.2021.1946541

Psychometric Properties of a French-Canadian Version of the Test of Gross Motor Development – Third Edition (TGMD-3): A Bifactor Structural Equation Modeling Approach

2021· article· en· W3182408614 on OpenAlexaffabout
Christophe Maïano, Alexandre J. S. Morin, Johanne April, E. Kipling Webster, Olivier Hüe, Claude Dugas, Dale A. Ulrich

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2021
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversité du Québec à Trois-RivièresConcordia UniversityUniversité du Québec en Outaouais
Fundersnot available
KeywordsStructural equation modelingGross motor skillPsychologyExploratory factor analysisMotor skillTest (biology)PsychometricsDevelopmental psychologyPhysical therapyClinical psychologyMedicineStatistics

Abstract

fetched live from OpenAlex

The objective was to assess the psychometric properties of a French-Canadian version of the third edition of the Test of Gross Motor Development (TGMD-3). Participants were 127 French-speaking Canadian children. Results supported the validity-reliability of a bifactor exploratory structural equation modeling representation of the TGMD-3. Additionally, results supported a lack of differential item functioning as a function of age, body mass index (BMI), physical activity/sport practice (PA/SP), and sex. Finally, latent mean differences showed that: (a) older children score lower on specific skills and higher on the global motor skills factor than younger children; (b) children with a higher BMI score lower on locomotor skills than children with a lower BMI; (c) children with higher weekly frequency of PA/SP score higher on the global motor skills factor than children with a lower weekly frequency of PA/SP; and (d) boys score higher on ball skills and lower locomotor skills than girls.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.055
GPT teacher head0.269
Teacher spread0.214 · 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

Citations31
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueMeasurement in Physical Education and Exercise ScienceSame topicChildren's Physical and Motor DevelopmentFrench-language works237,207