EFEKTIVITAS PENGGUNAAN LIDAH BUAYA (ALOEVERA) TERHADAP PENYEMBUHAN LUKA PERINEUM PADA IBU NIFAS DIWILAYAH KERJA PUSKEMAS KARANG ANYAR LAMPUNG SELATAN TAHUN 2021
Bibliographic record
Abstract
Perineal rupture is one of the causes of postpartum maternal bleeding. Postpartum hemorrhage is themain cause of 40% of maternal deaths in Indonesia. One way of natural treatment is by giving aloe vera whichcan accelerate the wound healing process. The purpose of this study was to determine the effectiveness of theuse of aloe vera (Aloevera) on the healing of perineal wounds in postpartum mothers in the Karang Anyar HealthCenter, South Lampung.The Study employed a quantitative research with quasi-experimental approach. The subjects of thisstudy were mothers who experienced perineal rupture of degree I and degree II in the Work Area of the KarangAnyar Health Center, South Lampung, 104 mothers, a sample of 51 data. Data obtained from questionnaires.Analysis using univariate and bivariate analysis using independent t test.The results of this study found that the average value of perineal wound healing given aloe vera(aloevera) was 4.72, standard deviation 1.815. The average value of perineal wound healing that was not givenaloe vera was 7.35, the deviation was 2.545. There is an effectiveness of using aloe vera (Aloevera) on thehealing of perineal wounds in postpartum mothers in the Karang Anyar Health Center, South Lampung(Pvalue=0.026,).Kesimpulan dalam penelitian ini Terdapat efektivitas penggunaan lidah buaya (Aloevera)terhadap penyembuhan luka perineum pada ibu nifas di Wilayah Kerja Puskesmas Karang Anyar LampungSelatan saran penelitian ibu yang mengalami luka perineum untuk merawat luka setalah melahirkan danmengkonsumsi makanan bergizi khususnya makananan yang mengandung protein tinggi agar lukanya cepatsembuh.From the results of the study, researchers suggest mothers who experience perineal wounds to treatwounds after childbirth and consume nutritious foods, especially foods that contain high protein in order toaccelerate the wound healing process
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".