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Record W2914918762 · doi:10.1123/jtpe.2018-0270

Physical Literacy in Children and Youth—A Construct Validation Study

2019· article· en· W2914918762 on OpenAlexaff
John Cairney, Heather J. Clark, Dean Dudley, Dean Kriellaars

Bibliographic record

VenueJournal of Teaching in Physical Education · 2019
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of ManitobaUniversity of Toronto
Fundersnot available
KeywordsCompetence (human resources)PsychologyConstruct (python library)Physical educationConstruct validityLiteracyMultilevel modelDevelopmental psychologyMean squared errorMathematics educationApplied psychologySocial psychologyStatisticsMathematicsPedagogyPsychometricsComputer science

Abstract

fetched live from OpenAlex

Purpose: Physical literacy (PL) has been proposed as a key construct for understanding participation in physical activity. However, the lack of an agreed-upon definition and measure has hindered research on the topic. The current study proposed and analyzed the construct validity of a PL model comprised of motor competence, perceived competence, motivation, and enjoyment. Method: The authors tested three different models in two samples: Grade 5 (N = 1,448) and Grade 7 students (N = 698). Results: The PL construct was best represented as a hierarchical model in both the Grade 5, X2(295) = 791.90, p < .001; root mean square error of approximation = .035; and comparative-fit index = .97, and the Grade 7 samples, X2(295) = 557.21, p < .001; root mean square error of approximation = .036; and comparative-fit index = .98, samples. Discussion: Future work is needed to design and evaluate a PL measure consistent with our model. Such work will help generate further research and understanding of PL.

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)0.011
metaresearch head score (Gemma)0.018
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 score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
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 head0.009
GPT teacher head0.331
Teacher spread0.322 · 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

Citations76
Published2019
Admission routes1
Has abstractyes

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