Effect of Aloe Vera Drink on Intensity of Dysmenorrhea in Students, Tangerang, Banten
Bibliographic record
Abstract
ABSTRACT Background: WHO reported that more than 50% of women suffered dysmenorrhea in each nation. Concerning to side effects of analgetic drugs, harmless herbal therapeutic need to be considered as alternative medicine. Besides wide use of Aloe vera as cosmetics, it was also reported as a potent analgesic. This study aimed to investigate the effect of aloe vera drink on the intensity of dysmenorrhea in students at the School of Health Sciences Yatsi, Tangerang. Subjects and Method: A quasi-experiment with one group pretest-posttest without a control group was conducted at School of Health Sciences Yatsi, Tangerang from February to March. A total of 30 female students aged 18-21 years was selected by incidental sampling, in which 5 study subjects’ unmet criteria inclusion were dropped out. The criteria inclusion were female students, unmarried, during pre-menstruation period, no recently pain reliever used, and consent to consume aloe vera drink regularly for six days. The dependent variable was the intensity of dysmenorrhea. The intensity of dysmenorrhea was measured by McGill Pain questionnaires conducted two times at three days before and day 3 of menstruation. The independent variable was aloe vera drink consumption. The data were analyzed by paired t-test. Results: The intensity of dysmenorrhea in study subjects was reduced after treatment with aloe vera drink (Mean= 1.56; SD= 0.71) than before treatment (Mean= 2.68; SD= 0.75), and it was statistically significant (p <0.001). Conclusion: Aloe vera drinks consumption reduce the intensity of dysmenorrhea. Keywords: aloe vera drink, dysmenorrhea, intensity, female adolescents Correspondence: Solihati. Nursing Program Study, School of Health Sciences Yatsi, Tangerang. Email: solyan8000@gmail.com. Mobile: 085691903637. DOI: https://doi.org/10.26911/the7thicph.05.37
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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 | 0.000 |
| 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) | 0.003 | 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".