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Record W4200188297 · doi:10.1186/s12889-021-12301-7

Translation and validation of the Canadian assessment of physical literacy-2 in a Danish sample

2021· article· en· W4200188297 on OpenAlexaffabout
Peter Elsborg, Paulina Sander Melby, Mette Kurtzhals, Mark S. Tremblay, Glen Nielsen, Peter Bentsen

Bibliographic record

VenueBMC Public Health · 2021
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersDanish Foundation TrygFondenTrygFondenSteno Diabetes Center Copenhagen
KeywordsBiostatisticsDanishMedicinePublic healthEpidemiologySample (material)Environmental healthLiteracyPathologyLinguistics

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to translate the Canadian Assessment of Physical Literacy, second edition (CAPL-2) into Danish language, adapt it to Danish context and to test the measurement properties on a sample of Danish school children. METHODS: The CAPL-2 measurement tool was translated into Danish language and adapted for the Danish context. This Danish version of the CAPL-2 was then tested on 891 Danish school children from 50 classes in 12 different schools. RESULTS: Confirmatory factor analysis using the four-factor model, as suggested by the CAPL-2 original developers, showed an acceptable model fit for the Danish version (CFI = .973; TLI = .957; RMSEA = 0.040 (90% CI 0.033-0.054); SRMR = 0.040). Positive significant correlations between the domains were found. The domains as well as the total CAPL-2 score were found to be positively associated with physical education teachers' assessment of their pupil's in four central aspects of PL (i.e. enjoyment, confidence, motor skills, and diversity) indicating predictive validity. High internal consistency of the instrument used to measure motivation and confidence domain were found. CONCLUSION: The translated and context-adapted Danish version of CAPL-2 is a valid and reliable measurement tool ready to use in Danish research studies.

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 score0.298
Threshold uncertainty score0.942

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 head0.057
GPT teacher head0.365
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

Citations50
Published2021
Admission routes2
Has abstractyes

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