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Record W2102387438 · doi:10.1177/1090198105277855

Opportunities for Student Physical Activity in Elementary Schools: A Cross-Sectional Survey of Frequency and Correlates

2006· article· en· W2102387438 on OpenAlexaffabout
Tracie A. Barnett, Jennifer O’Loughlin, Lise Gauvin, Gilles Paradis, Jim Hanley

Bibliographic record

VenueHealth Education & Behavior · 2006
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMcGill UniversityInstitut National de Santé Publique du QuébecUniversité de Montréal
Fundersnot available
KeywordsCross-sectional studyPhysical activityPsychologyPhysical educationEnvironmental healthMedicineMathematics educationPhysical therapy

Abstract

fetched live from OpenAlex

The objectives of this study were to describe opportunities for student physical activity (PA) in elementary schools and to identify factors in the school environment associated with higher PA opportunity. Self-report questionnaires were completed by school principals and physical education teachers in 277 schools (88% response) in metropolitan Montreal. Correlates of opportunity were identified using ordinal logistic regression. There was substantial variation in PA opportunities between schools. Higher opportunity was associated with role modeling of PA by school principals, their interest in increasing PA through links to the municipality, adequate financial and human resources, access to school sports facilities, adequate space for storing student sports equipment, and suburban location. There is both the need and the potential for intervention to increase PA opportunities in elementary schools. Addressing barriers related to resources and access to sports facilities may help reduce disparities between schools in opportunities for students to engage in PA.

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)0.002
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 score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.122
GPT teacher head0.439
Teacher spread0.317 · 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

Citations73
Published2006
Admission routes2
Has abstractyes

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