The Effect of Murottal Intervention in Prolactine Hormone Levels of Breastfeeding Mothers in Takalar Regency
Bibliographic record
Abstract
Breast milk is important for the infant’s growth and development early in life. Attention to lactating mothers in terms of increasing their breastmilk production is important. This study aimed to investigate the effect of giving murottal therapy on the levels of the hormone prolactin in lactating mothers in Takalar District. This was a true-experimental study in which 44 lactating mothers were divided into two groups, 22 mothers in intervention and 22 in control groups. The study was conducted in Takalar Regency, South Sulawesi Province, Indonesia. The characteristic of the participants showed that the age of participants in the intervention group is two years higher than in the control group. The number of mothers having more than three children is higher compared to the control (31.8% vs 9.1%). All characteristics of intervention and control were statistically not different. The result of this study showed that prolactin hormone levels were decreased in both groups. The prolactin hormone levels in the intervention group showed a lower decrease compared to control group (-89.84 ± 54.14 vs -103.54 ± 65.67), but not significantly different (p=0.453). The Qur’an therapy may be effective to replace music therapy to support lactation period and exclusive breastfeeding program, especially for those from Muslim communities. The District Health Office of Takalar can promote this therapy to improve lactation management program.
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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".