The effect of infant feeding planning education on nutrition and breastfeeding knowledge, mother’s attitude, and husband’s support to expectant mother
Bibliographic record
Abstract
Background: The issue of malnutrition that leads to food security is not just a technical issue, but also a matter of individual habit to meet nutrients needed, including nutrients for fetus. The Government agrees to address the issue of food security in the first 1,000 days of life. This study aims at determining the influence of Infant Feeding Planning (Intention) Education on Nutrition and Breastfeeding Knowledge, Mother’s Attitude, and Husband’s Support to gravid Mother in Samarinda.Methods: This study is a quasi-experimental research design with pre and post control group. The sample size was 30 in the intervention group and 30 in the control group. The independent variable in this study was intention to breastfeed, while the dependent variables were breastfeeding and nutrition knowledge, mother’s attitude and husband’s support. The data were analyzed using paired t test, Wilcoxon test, ANOVA and MANOVA.Results: The study found the differences between intervention group and control group in nutrition knowledge, breastfeeding knowledge, and attitude. There was no difference between the groups in intention. The variables that influenced knowledge were education and employment. The variables influenced by the intention to breastfeed were breastfeeding knowledge, nutrition knowledge, and attitude. Intention had the greatest impact on the attitude, with the power observed at 0.689 which means that the intention affected the attitude of breastfeeding by 68.9%.Conclusions: Infant feeding planning education influences breastfeeding and nutrition knowledge as well as breastfeeding attitude.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".