The Pattern of Carbohydrate Intake among University Students from Eastern Indonesia Tends to be Diverse
Bibliographic record
Abstract
Background: Along with the shift in consumption patterns of carbohydrate staple foods in Indonesia, many communities are abandoning local traditional foods such as tubers and sago and turning to white rice. Objective: Considering the rise of modern living patterns that are loved by teenagers and young adults, the researchers aimed to identify the basic food consumption patterns of adolescents and young people in Indonesia and the factors that influence them. Methods: The subjects involved in this study were 108 Universitas Gadjah Mada students representing various regions and tribes in Indonesia. The proportion of each part of Indonesia was determined by the proportionate stratified random sampling technique, while the research subjects were selected by purposive sampling. Subjects were interviewed using the semi-quantitative food frequency questionnaire (SQ-FFQ). Data on knowledge factors related to nutrition and perceptions were obtained using a separate questionnaire. Results: The most consumed staple carbohydrate foods are cereals, especially white rice (82%), tubers (10.6%), noodles, pasta and bread (12.3%) and sago (1.8%). Several factors that influence the consumption pattern significantly (p<0.05) are sex, place of origin, and nutritional status. Conclusions: There is a high dependency on white rice among young adults in Yogyakarta, Indonesia. The culture and place of origin have an important role in shaping the pattern of carbohydrate consumption. Strategic interventions are needed to increase youth food diversification.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".