Terapi jahe hangat pada ibu hamil yang mengalami hiperemesis gravidarum
Bibliographic record
Abstract
Background: Between 60% and 70% of primigravida pregnant women experience emesis gravidarum in the first trimester, and between 40% and 60% of multigravida pregnant women experience it. Approximately 25% of pregnant women experience early vomiting problems that require time off from work. The World Health Organization (WHO) estimates that 12.5% of all pregnancies worldwide are affected by HG, with varying incidence rates ranging from 10.8% in China, 2.2% in Pakistan, 1.9% in Turkey, 0.9% in Norway, 0.8% in Canada, 0.5% in California, and 0.3% in Sweden. Meanwhile, the incidence of HG in Indonesia ranges from 1-3% of all pregnancies. Non-pharmacological measures often recommended by health workers include advising pregnant women to consume warm ginger drinks. Purpose: To provide nursing care for mothers with hyperemesis gravidarum who have nausea using warm ginger. Method: Descriptive research design with a case study design. The subjects were two people who experienced nausea nursing problems. Ginger drinks were administered in the morning and evening for four days. Nausea and vomiting were measured before and after non-pharmacological therapy was administered, and during the study, the researcher used SPO. Results: Management of nausea with the application of warm ginger nursing interventions that the author performed for 4 days on patients with hyperemesis gravidarum, where the nausea problems of both clients were completely resolved, as evidenced by Mrs. S no longer experiencing nausea and vomiting and Mrs. A's nausea and vomiting had decreased to only 2 episodes from morning to night, and both clients were cooperative during the intervention. Conclusion: There is an effect of applying warm ginger to reduce nausea in patients with hyperemesis gravidarum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 | 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) | 0.001 | 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 teacher head, 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".