The Effect of Pregnancy Gymnastics on Reducing Pain on The Backs of Pregnant Women in the Practice of Independent Midwives Sri Ratu Alam Batam City in 2022
Bibliographic record
Abstract
Pregnancy brings great changes in the female body, including the back. Since entering the second tricemester period, some pregnant women began to experience back pain due to the increasingly large baby. 50-80% of women have experienced back and hip pain during pregnancy, chiropractic doctor Ronald J. Tyszkowski said. Back pain is a side effect of a normal pregnancy. The prevalence of spinal pain in pregnant women occurs more than 50% in the United States, Canada, Iceland, Turkey, Korea, and Israel. Meanwhile, in northern America, Africa, the Middle East, Norway, Hong Kong, the prevalence is higher which ranges from 21% to 89.9%. The results of research on pregnant women in various regions in Indonesia reached 60-80% of pregnant women experiencing back pain in their pregnancies. In East Java, around 65% of all pregnant women experience back pain. The method used with the Crossectiona design was a study sample of pregnant women in the II and III trimesters with a total of 35 people. Based on the results of research conducted at BPM Sri Ratu Alam Batam City on, using the Statistics Produck Service Solution (SPSS) program and from the analysis using the Chi-Square test, a P Value value = 0.001 < 0.05 was obtained, this shows that there is an influence of pregnant gymnastics on reducing pain in the back of pregnant women at BPM Sri Ratu Alam Batam City
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".