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Record W2170675236 · doi:10.1177/1090198110367877

Exploring the Influence of a Social Ecological Model on School-Based Physical Activity

2010· article· en· W2170675236 on OpenAlexaffabout
Jessie-Lee D. Langille, Wendy M. Rodgers

Bibliographic record

VenueHealth Education & Behavior · 2010
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of AlbertaDalhousie University
Fundersnot available
KeywordsSocial ecological modelHealth promotionPromotion (chess)Government (linguistics)Public healthOverweightSocial influencePsychologyAcademic achievementGerontologyEnvironmental healthMathematics educationObesityEcologyPolitical scienceMedicineSocial psychologyNursing

Abstract

fetched live from OpenAlex

Among rising rates of overweight and obesity, schools have become essential settings to promote health behaviors, such as physical activity (PA). As schools exist within a broader environment, the social ecological model (SEM) provided a framework to consider how different levels interact and influence PA. The purpose of this study was to provide insight on school-based PA promotion by investigating the integration between different levels of Emmons's SEM within one public school board in a large Canadian city. Interviews were conducted with participants from the government (n = 4), the public school board (n = 3), principals (n = 3), and teachers (n = 4) and analyzed to explore perspectives on the various levels of the model. The results suggested that higher level policies "trickled down" into the organizational level of the SEM but there was pivotal responsibility for schools to determine how to implement PA strategies. Furthermore, schools have difficulty implementing PA because of the continued priority of academic achievement.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.239
Threshold uncertainty score0.475

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0040.011
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.001
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 head0.329
GPT teacher head0.529
Teacher spread0.200 · 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

Citations170
Published2010
Admission routes2
Has abstractyes

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