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Record W2163192410 · doi:10.21149/spm.v55s3.5137

Promoting healthful diet and physical activity in the Mexican school system for the prevention of obesity in children

2013· article· es· W2163192410 on OpenAlexaff
Margarita Safdie, Lucie Lévesque, Inés González-Casanova

Bibliographic record

VenueSalud Pública de México · 2013
Typearticle
Languagees
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsQueen's University
Fundersnot available
KeywordsChristian ministryHumanitiesMedicinePolitical scienceArt

Abstract

fetched live from OpenAlex

Objetivo. El presente trabajo describe el protocolo, objetivo, el diseño y los métodos de un ensayo controlado aleatorio de dos años realizados para evaluar la efectividad de una intervención ambiental en 27 escuelas primarias de la Ciudad de México. Material y métodos. El protocolo consta de dos unidades de análisis: el nivel escolar donde se evaluaron los cambios en el entorno escolar de escuelas primarias publicas de medio tiempo y el nivel individual que evaluó los cambios de comportamiento en alumnos de 9 a 11 años de edad. Se implementaros dos tipos de intervenciones: nutrición y actividad física apoyadas de una estrategia de educación/comunicación. Las intervenciones tuvieron dos intensidades: básica y plus. La evaluación de la efectividad se llevó a cabo durante los ciclos escolares 2006-2007 y 2007-2008. Resultados. Los resultados iniciales reportan los métodos de evaluación de conducta individual así como la prevalencia de sobrepeso y obesidad. La evaluación ambiental reporta el protocolo de evaluación del entorno escolar. Conclusiones. Este es el primer proyecto de la de intervención escolar con un diseño multinivel, multifactorial, basado en literatura científica disponible y en investigación formativa para prevenir la obesidad infantil en las escuelas de la Ciudad de México. * * Supported by International Life Science Institute (ILSI), Pan American Health Organization (PAHO), the Mexican Council for Science and Technology (Conacyt), and Mexican Ministry of Health (SSa).

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)0.002
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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

Citations34
Published2013
Admission routes1
Has abstractyes

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