PERBEDAAN PENGETAHUAN DAN PERILAKU KONSUMSI SAYUR BUAH SETELAH PEMBERIAN EDUKASI GIZI DENGAN VIDEO ANIMASI DAN LEAFLET PADA ANAK SD
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".