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Record W2742782026 · doi:10.4236/ape.2017.73023

Adolescents’ Relationship between Physical Education and Longitudinal Physical Activity Trends

2017· article· en· W2742782026 on OpenAlexafffund
Alexandra C. Wiseman, Patricia L. Weir

Bibliographic record

VenueAdvances in Physical Education · 2017
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsPhysical activityLongitudinal studyAdolescent healthPsychologyPerspective (graphical)Physical educationGerontologyHealth behaviorPhysical healthMedicineDevelopmental psychologyMental healthPhysical therapyEnvironmental healthNursingPedagogy

Abstract

fetched live from OpenAlex

With physical education (PE) being an avenue to be physically active and learn about health and wellbeing, it is important to understand enrollment trends and physical activity (PA) behaviors among adolescents. The purpose of this study is to examine adolescents’ health profiles and gain an understanding of adolescents’ perspective of PE. The current study used mixed methodology to examine adolescents’ health profiles and gain understanding of their perspectives of PE. Part 1 identified relationships over a two-year period between: PE rating, physical activity (PA), and health variables using data from the National Longitudinal Survey of Children and Youth (NLSCY). Part 2 examined adolescents’ perspectives of PE through four focus groups.Overall, PE was preferred over other subjects by 78% of participants; and preferring PE predicted higher frequencies of PA, lower BMI, and higher self-esteem. Enrollment in high school PE was influenced by the environment, gender, course conflicts, and teacher influence. In summary, the majority of adolescents prefer PE; it has an influence on health, and is an avenue for PA. Continued efforts need to be made to increase PE enrollment and participation to ensure the health of young people.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.402
Teacher spread0.366 · 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.

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

Citations4
Published2017
Admission routes2
Has abstractyes

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