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Record W4414259789 · doi:10.1080/1091367x.2025.2558521

Physical Literacy in Children Questionnaire: Adaptation, Validity, and Reliability Evidence for 4–12-Year-Old Greek Children

2025· article· en· W4414259789 on OpenAlexaboutno aff
Vasiliki Kaioglou, Manolis Adamakis, Irene Kossyva, Komanthi Kouloutbani, Anastasia-Evangelia Afthentopoulou, Emiliano Mazzoli, Lisa M. Barnett, Fotini Venetsanou

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2025
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsReliability (semiconductor)Physical educationLiteracyNumeracyPhysical activityDevelopmentally Appropriate Practice

Abstract

fetched live from OpenAlex

This study adapted the Physical literacy in children questionnaire (PL-C Quest) for Greek children (4–12 years; N = 552, Mage = 9.2 ± 2.2 years) and conducted comprehensive psychometric analyses: (1) Confirmatory factor analysis (CFA) tested a four-domain model (physical, psychological, social, cognitive) with higher-order factor (physical literacy); (2) Multi-group CFA examined measurement invariance across sexes; (3) convergent validity was assessed through Average Variance Extracted (AVE) and Composite Reliability, with additional Pearson’s correlations against the Canadian Assessment of Physical literacy-2 scores (n = 90); (4) discriminant validity was evaluated via AVE comparisons; (5) Cronbach’s α was used to evaluate internal consistency; and (6) test–retest reliability was determined through Interclass Correlation Coefficient in a subsample (n = 120). Results supported the model’s adequate fit, measurement invariance, and moderate convergent validity. While discriminant validity was limited, reliability metrics were strong. The PL-C Quest (Greek) demonstrates adequate psychometric properties for assessing perceived physical literacy, facilitating monitoring and program evaluation.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.202
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.041
GPT teacher head0.350
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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