Health Promotion Model for Improvement of the Nutritional Status of Children Under Five Years
Bibliographic record
Abstract
OBJECTIVE: Nutrition problems in Indonesia are multidimensional problems that are influenced by several factors including economic, education, social culture, agriculture, and health. Nutrition problems reflect economic, political, and social crises as the root causes of malnutrition. This study formulates a health promotion model to improve the nutritional status of children under five years old. METHOD: This type of research is quantitative with survey design and cross-sectional approach. RESULT: This study produced a risk of children under five yearsexperiencing poor nutritional status with a history of illness.The risk of children under five yearsexperiencing undernourished nutritional status with strong health workers-cadre-family partnerships and strong family support. The risk of children under five years experiencing wasting nutritional status increases with a history of diarrheal disease. The risk of children under five yearsexperiencing wasting nutritional status decreases with strong health workers-cadre-family partnerships and strong family support. The risk of a child under five yearsexperiencing a stunting nutritional status increases with a history of diarrheal disease. The risk of children under five years old experiencing stunting nutritional status decreases with strong health workers-cadre-family partnerships and strong family support. CONCLUSION: Nutritional status of children under five years (malnutrition, wasting and stunting) is affected directly and indirectly through the variables of family income, mother's knowledge, attitudes towards nutrition problems, environmental sanitation, social capital, health workers-cadre-family partnerships, family support, history of diarrhea disease and mother'seducation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".