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Record W1917627399 · doi:10.1186/s12889-015-2106-6

The Canadian Assessment of Physical Literacy: methods for children in grades 4 to 6 (8 to 12 years)

2015· article· en· W1917627399 on OpenAlexafffundabout
Patricia E. Longmuir, Charles P. Boyer, Meghann Lloyd, Yan Yang, Elena Boiarskaia, Weimo Zhu, Mark S. Tremblay

Bibliographic record

VenueBMC Public Health · 2015
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsOntario Tech UniversityChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsBiostatisticsConfirmatory factor analysisCompetence (human resources)Raw scoreStructural equation modelingDevelopmental psychologyHealth literacyPhysical educationMedicineLiteracyPhysical fitnessPsychologyClinical psychologyRaw dataPublic healthPhysical therapySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Physical literacy is described as the motivation, confidence, physical competence, knowledge and understanding to value and engage in a physically active lifestyle. As such, it is expected that those who have greater physical literacy would be more likely to obtain the health benefits offered by habitual physical activity. A theoretical model and assessment battery, the Canadian Assessment of Physical Literacy (CAPL), for the assessment of childhood physical literacy had been proposed in theory but validity data were lacking. The purpose of this study was to explore validity evidence for the CAPL among children in grades 4 to 6. METHODS: CAPL validity was evaluated through three analyses that utilized cross-sectional data obtained through local schools in Eastern Ontario, Canada. A confirmatory factor analysis compared the data to the theoretical model. Patterns of association between self-reported age and gender and the CAPL total and domain scores were examined using regression models. Teacher ratings of participants' knowledge, attitude and physical activity competence were compared to assessment results. RESULTS: The CAPL was completed by 963 children (55 % female) in grades 4, 5 and 6. Children were 8 to 12 years of age (mean 10.1 years), with 85 % of children approached agreeing to participate. A confirmatory factor analysis using data from 489 children with complete raw scores supported a model with four domains: engagement in physical activity (active and sedentary), physical competence (fitness and motor skill), motivation and confidence, and knowledge and understanding. Raw domain scores followed expected patterns for age and gender, providing evidence for their validity. Interpretive categories, developed from age and gender adjusted normative data, were not associated with age indicating that the CAPL is suitable for use across this age range. Children's gender was associated with the physical competence, motivation and engagement in physical activity domain scores, indicating that further research is required regarding the gender adjustment of the raw CAPL scores. CAPL domain and total scores were statistically significantly associated with teacher ratings of the child's motivation, attitudes, fitness, skill and overall physical activity. CONCLUSIONS: CAPL offers a comprehensive assessment of engagement in physical activity, physical competence, motivation and confidence, and knowledge and understanding as components of childhood (grades 4 to 6, 8 to 12 years) physical literacy. Monitoring of these measures enhances our understanding of children's physical literacy, and assists with the identification of areas where additional supports are required.

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)0.004
metaresearch head score (Gemma)0.010
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: Methods · Consensus signal: none
Teacher disagreement score0.194
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.005
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.085
GPT teacher head0.462
Teacher spread0.377 · 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
GenreMethods

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

Citations249
Published2015
Admission routes3
Has abstractyes

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