Pengaruh Teknik Akupressure Terhadap Nyeri Punggung Pada Ibu Hamil Di Wilayah Puskesmas Jogonalan I Klaten
Bibliographic record
Abstract
Abstract: Acupressure, Back Pain, Pregnancy. Acupressure is effective to relieve back pain in Meridian point. Acupressure technique is done to help pregnant women in relieving complaints in pregnancy such as nausea and vomiting. In labour process, this technique can be an induction of labor, and can reduce anxiety. The purpose is to know the influence of acupressure technique to relieve back pain for pregnant women in Puskesmas Jogonalan I area of Klaten. Research is pre experimental design with one group pretest posttest approach. The population is all pregnant women in Puskesmas Jogonalan I area of Klaten. The population target is all third trimester of pregnant women in Puskesmas Jogonalan I area of Klaten. Technique sampling is purposive sampling with 30 peopole, ang using t-test data analysis. The characteristics of respondents showed that most of them are 20-35 years old, their gestational age are 3137 weeks, their occupation are housewives, and most of them have 2-3 children. Degree of back pain in pregnant women before acupressure as many as 21 people (70%) are in severe pain. Degree of back pain in pregnant women after given acupressure as many as 24 people (80%) are in mild pain. There is influence of acupressure technique to relieve back pain for pregnant women in Puskesmas Jogonalan I area of Klaten (t =9,893; p=0,001<0,05).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".