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Evaluation of an Afterschool Children’s Healthy Eating and Exercise Program

2014· article· en· W2003934137 on OpenAlexvenueno aff
Chia‐Liang Dai, Laura Nabors, Keith A. King, Rebecca A. Vidourek, Ching‐Chen Chen, Hoang Thi My Nhung, Katherine G. Mastro

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical therapyGerontology

Abstract

fetched live from OpenAlex

Background: The purpose of this study was to examine the feasibility of the Children’s Healthy Eating and Exercise Program (CHEE) in an afterschool program of an elementary school. Methods: Students in a low-income elementary school were recruited to participate in the program. Thirty-three children were in the intervention group. Twenty-four children in the comparison group were recruited from after school clubs in the same elementary school. The CHEE Program consisted of 18 sessions, featuring nutrition (20 min) and physical activity (40 min) lessons. Nutrition lessons were adapted from the Traffic Light Diet. Other lessons included MyPlate, my refrigerator, my lunchbox, and a healthy foods tasting activity. Multiple physical activities were utilized in the program including soccer, dance, relay races, tag, and other fun games. Data were collected at the beginning and end of the program. Results: Children in both groups reported eating more vegetables at the post-intervention measurement. Children in the intervention group indicated that they learned about healthy eating and new physical activities due to their participation in the program. Conclusions: Future studies are needed to discover barriers to behavior change as well as apply a more rigorous design to examine the impact of the CHEE Program.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.353
Teacher spread0.335 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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