The Effect of Hypnobreastfeeding on Increased Milk Production in Breastfeeding Mothers of Perlis Village, Tangkahan Durian District, of North Sumatera, Indonesia
Bibliographic record
Abstract
BACKGROUND/AIM: To ascertain Hypnobreastfeeding on increased milk production in Breastfeeding Mothers of Perlis Village, Tangkahan Durian District, in Nort Sumatera of Indonesia. METHODS: A Quasy experiment study with non-equivalen control group approach with purposive sampling. A total of 24 samples randomly selected become as many as 12 people were given hypnobreastfeeding treatment and 12 people were not treated with the inclusion criteria age of infants were10 days - 1 month, mother’s Hb ≥ 12 g% and infants were not given formula milk during the study. Data entry was done manually but the analysis was done with the SPSS version 12 programme. Results were presented as distribution frequencies. RESULTS: Hypnobreastfeeding was significantly increase milk production in breastfeeding mothers (p value =0.001). The mean of initial breast milk in the hypnobreastfeeding group was 78.92 ml SD 2.15 and after treatment became 93.94 ml (SD=5.23). The findings of the study were compared with previous studies and the researchers showed how the relevant theories that guided the study were used to explain the findings. CONCLUSION: Hypnobreastfeeding treatment could increased milk production of breastfeeding mothers. So, the counseling of Hypnobreastfeeding treatment was necessary to breastfeeding mothers even to mothers during pregnancy.
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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 | 0.000 | 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) | 0.001 | 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".