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Desarrollo de un cuestionario didáctico para conocer la ingestión de verduras y frutas de niños en el receso escolar: estudio piloto

2012· article· es· W1975795098 on OpenAlexaff
Catalina Medina, Montserrat Villanueva-Borbolla, Sı́món Barquera

Bibliographic record

VenueSalud Pública de México · 2012
Typearticle
Languagees
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsUniversity of LethbridgeQueen's University
Fundersnot available
KeywordsConsumption (sociology)Food consumptionFood intakeFood frequency questionnaireHealthy foodPsychologyEnvironmental healthHumanitiesMedicineFood scienceBiologyArt

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop an open, self-reported questionnaire, focused in understanding intake and intention to increase vegetable and fruit consumption during the Mexican children's lunchtime. MATERIALS AND METHODS: After assessing pilot tests, the self-reported questionnaire was applied as a classroom exercise to measure children's consumptions of fruit and vegetables. RESULTS: A total of 486 children completed the questionnaire (49.8% males and 50.2% females). The reported food consumption indicates that 24.5% of children do not consume vegetables or fruits, and among consumers the most common portion size is one. Girls consume more vegetables and fruits and they can recognize more benefits. A total of 9.7% of the participants that do not consume VF, expressed less intention of including vegetables or fruits in their lunch. These results were similar to those reported by other authors. CONCLUSION: This study is useful to identify strengths and limitations during the design and implementation of this type of instrument to understand children's food habits during school recess.

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)0.009
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0010.001

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 head0.047
GPT teacher head0.412
Teacher spread0.365 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2012
Admission routes1
Has abstractyes

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