Efektifitas Terapi Jahe Hangat Dalam Mengatasi Emesis Gravidarum Pada Ibu Hamil
Bibliographic record
Abstract
According to the World Health Organization (WHO), hyperemesis gravidarum occurs worldwide, including in the United States, with an incidence of 0.5-2%, Sweden 0.3%, California 0.5%, Canada 0.8%, China 10.8%, Norway 0.9%, Pakistan 2.2% and Turkey 1.9%. Currently, the incidence of hyperemesis gravidarum in Indonesia is 1-3% of all pregnancies. The aim of this research is to determine the effectiveness of administration of warm ginger on the incidence of gestational vomiting in pregnant woman in early pregnancy. It was a quantitative study with a pre-experimental study design using one group pretest-posttest design. The study population consisted only of pregnant women in the first trimester who suffered from morning sickness and whose pregnancies were assessed with the BPM Happy Purnama. Sampling for this study used a global sampling technique with a sample of 16 individuals. Data were collected using the Vomiting Pregnancy-Specific Quantification Questionnaire. The statistical results of the study showed a p-value of 0.002. This means less than a significant value that is less than 0.05. Therefore, it can be concluded that the administration of hot ginger effectively reduced the frequency of pregnancy vomiting. This study is also intended to be an alternative non-pharmacological treatment for vomiting in pregnant women in the first trimester.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".