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Record W4400467414 · doi:10.31983/jrg.v10i1.9318

PERBEDAAN PENGETAHUAN DAN PERILAKU KONSUMSI SAYUR BUAH SETELAH PEMBERIAN EDUKASI GIZI DENGAN VIDEO ANIMASI DAN LEAFLET PADA ANAK SD

2022· article· en· W4400467414 on OpenAlexaff
Fajar Salekah

Bibliographic record

VenueJURNAL RISET GIZI · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsIntertek (Canada)WiLAN (Canada)
Fundersnot available
KeywordsAnimationSignificant differenceQuasi-experimentPsychologyMedicineMathematicsComputer scienceEnvironmental healthPopulationStatisticsComputer graphics (images)

Abstract

fetched live from OpenAlex

Background: Lack of knowledge can affect the behavior of consumption of vegetables and fruit in children. One of the ways to increase knowledge and behavior in consuming vegetables and fruit is by providing education, in educating researchers using animated videos and leaflets.Subject : The purpose of this study was to analyze differences in knowledge and behavior of fruit vegetable consumption by using video animation and leaflet methods in elementary school children in the Cibinong area.Methods: This type of research is a quasi-experimental research design with a controlled group pre-post design. Analysis of the data in this study using the Paired Sample T-test, Independent T-test and the Mann-Whitney test.Results: The results showed that there was a difference in knowledge before and after being given an intervention in the form of nutrition education with the animated video method (p = 0.000, = 29) and leaflets (p = 0.034, = 9). There was a significant difference in the consumption behavior of vegetables and fruit before and after the nutrition education intervention using animated videos (p = 0.000, = 27) and leaflets (p = 0.037, = 5).Conclusions: Based on the results of these data, it is concluded that animated videos are more effective for teaching and learning compared to leaflets.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.001
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 head0.028
GPT teacher head0.298
Teacher spread0.270 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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