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Record W2907209218 · doi:10.1111/josh.12722

Comparing Physical Activity Behavior of Children During School Between Balanced and Traditional School Day Schedules

2019· article· en· W2907209218 on OpenAlexafffundabout
Andrew Clark, Piotr Wilk, Jason Gilliland

Bibliographic record

VenueJournal of School Health · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsChildren’s Health Research InstituteWestern University
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsSchedulePhysical activityMedicinePsychologyPhysical therapyDemographyGerontologyDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Some Canadian schools have modified their daily schedules from the traditional school day (TSD) schedule (two 15-minute breaks and one 60-minute break) to a balanced school day (BSD) schedule (two 40-minute breaks). While this change increases daily planning and instructional time, it also changes the amount of time available for moderate-vigorous physical activity (MVPA). METHODS: This study uses a case-control design to examine differences in objectively measured MVPA between children in 3 schools using a BSD schedule and 3 schools using a TSD schedule. Study participants (aged 10-12 years) were recruited from schools in Ontario, Canada. RESULTS: Regardless of schedule type, girls had lower MVPA than boys, and as both boys and girls got older their MVPA significantly decreased. The findings indicate there was no statistically significant difference in the total minutes of in-school MVPA between children from BSD schools and children from TSD schools. MVPA was significantly higher for older girls attending BSD schools than older girls attending TSD schools, suggesting that implementing a BSD may help curb declining MVPA as girls enter adolescence. CONCLUSION: Despite encouraging findings, more rigorous studies (ie, pre-post experiments with control) are needed to better understand how changing schedules impacts children's health.

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)0.000
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 score0.004
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.044
GPT teacher head0.325
Teacher spread0.281 · 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

Citations12
Published2019
Admission routes3
Has abstractyes

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