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Record W3096522693 · doi:10.1007/s11125-020-09519-5

Increasing physical literacy in youth: A two-week Sport for Development program for children aged 6-10

2020· article· en· W3096522693 on OpenAlexaffabout
Marika Warner, Jackie Robinson, Bryan Heal, Jenny Lloyd, James Mandigo, Bess Lennox, Larkin Davenport Huyer

Bibliographic record

VenueProspects · 2020
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of the Fraser ValleyQueen's UniversityWilfrid Laurier University
Fundersnot available
KeywordsLiteracyPhysical educationPsychological interventionPhysical activityPsychologyPerceptionHealth literacyGerontologyPhysical therapyMedicinePedagogyPolitical scienceHealth care

Abstract

fetched live from OpenAlex

Abstract Regular physical activity significantly improves health outcomes, yet rates of childhood physical activity remain alarmingly low. Physical literacy has been identified as the foundation for quality physical education, suggesting that sport, education, and public health interventions should seek to increase physical literacy to promote physical activity. A two-week day camp program for children aged 6–10 facing barriers to positive development, was developed and delivered by a Sport for Development facility in Toronto, Canada. Utilizing fundamental movement skills (FMS) as a teaching tool and a pre- and post-assessment, the camp aimed to increase physical literacy and promote engagement in physical activity. Results indicate a significant increase in FMS (t (44) = 4.37, p < .001) as well as improved self-perceptions of physical literacy (t (40) = 14.96, p < .001). The largest FMS increases were found in running and balance and the most significant impacts were among low baseline performers.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.313
Teacher spread0.287 · 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

Citations34
Published2020
Admission routes2
Has abstractyes

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