Efektifitas pemberian jahe, susu kedelai (soya) dan madu (jesoma) terhadap penurunan nyeri dismenore pada remaja putri
Bibliographic record
Abstract
Background: Dysmenorrhea is discomfort that can occur when experiencing menstruation which can have an impact on disrupting physical activities.Objectives: The focus of this research is to determine the effect of ginger, soy milk and honey (Jesoma) on reducing dysmenorrhea pain complaints.Methods: The research method used was Quasi-experimental with a one group pre-post test design approach. Sampling in this study used a purposive sampling technique with the sampling criteria being ready to be respondents, female students who were still active and experiencing dysmenorrhea and not experiencing secondary dysmenorrhea. The number of samples in this research was 32 female students at SMK 1 Ketapang. The pre-test in this research was carried out before the treatment and the post-test was carried out after the research by giving ginger, soy milk and honey (Jesoma).Result : Data analysis used the Wilcoxon test with an error rate of <0,05. The statistical results obtained a p-value of 0,000, so there was an effect of giving ginger, soy milk and honey (Jesoma) on reducing dysmenorrhea pain. Conclusion: In order to obtain the best intervention method in reducing dysmenorrhea, researchers can then carry out laboratory tests to determine the content of ginger, soy milk and honey (Jesoma) and combine it with several methods to obtain the best method in reducing dysmenorrhea.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".