Discomfort to Comfort, Coconut oil can Reduce Menstrual Pain!
Bibliographic record
Abstract
Background: Menstrual pain usually begins several hours before or just after the onset of menstruation. Women commonly experience pain in the lower abdomen and in some, it radiates to lumbar region, it affects their performance of their daily activities. Coconut oil has many benefits like it is anti-inflammatory and anti-toxin and fights pain directly and also it is cheap as well as it is easily available in home. Aims: The aim of the study was to find the effectiveness of applying coconut oil over lower abdomen in reducing menstrual pain among young women residing in a selected hostel. Materials and Methods: A pre-experimental one group pre-test and post-test study design, using a quantitative approach and non-probability purposive sampling technique on 30 hostlers, participated on the basis of their severity of menstrual pain. The tools deployed include sociodemographic variables, universal pain assessment scale, and modified McGill questionnaire. On the day of the menstrual pain, a selfprepared pre-test questionnaire was administered and after 1 h of intervention the post-test was administered. Both descriptive and inferential statistics were used for the analysis of data. Results: Pre- and post-test and paired-t test were analyzed. The mean ± standard deviations of pre-test were 2.03 ± 1.03 and the post-test was 0.76 ± 0.97. The pain reduced with 1.27 mean differences. The obtained t-value was13.32 and P-value significantly improved at P < 0.00. Conclusion: The study revealed that applying coconut oil over lower abdomen of menstruating women showed improvement in bringing down the level of menstrual pain. This indicates that application of coconut oil effectively reduced the menstrual pain.
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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) | 0.017 | 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".