MétaCan
Menu
← Back to cohort
Record W4205107275 · doi:10.3390/ijerph19020688

Physical Activity in High School Classrooms: A Promising Avenue for Future Research

2022· review· en· W4205107275 on OpenAlexaff
Barbara Fenesi, Jeffrey D. Graham, Madeline Crichton, Michelle Ogrodnik, Jasmyn Skinner

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMcMaster UniversityOntario Tech UniversityWestern University
Fundersnot available
KeywordsPhysical activityPsychologyDevelopmental psychologyMental healthMedicinePhysical therapy

Abstract

fetched live from OpenAlex

Adolescence represents a sensitive period whereby lifestyle factors such as physical activity can have profound, long-lasting effects on development and later life habits. However, adolescence constitutes a period of frequent sedentary behaviour. Among children, integrating physical activity into elementary school classrooms has been shown to reduce sedentary behaviour and improve academic achievement and overall physical and mental health. However, this promising area of research has not extended to adolescents and high school classrooms. In this paper, we describe the benefits of conducting research on the impact of physically active high school classrooms, and highlight the challenges and potential misconceptions associated with research in this field. Specifically, we review research on the role of physical activity in adolescent development, the benefits of classroom-based physical activity for children, and discuss the factors that may have led researchers to focus on classroom-based physical activity primarily for children, despite the potentially similar benefits for adolescents.

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)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0100.002

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 head0.183
GPT teacher head0.487
Teacher spread0.304 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public Health→Same topicObesity, Physical Activity, Diet→French-language works237,207→