A Quasi Experimental Study to Assess the Effectiveness of Ginger Powder on Dysmenorrhea among Nursing Students in Selected Nursing Colleges, Hoshiarpur, Punjab
Bibliographic record
Abstract
Background: primary dysmenorrhea is one of the most common gynecologic disorders affecting more than half of menstruating women that interferes with daily activities. Some studies have found alternative methods such as acupuncture, acupressure, stimulation, massage, aromatherapy and ginger to be fairly effective for treatment of dysmenorrhea. Ginger is a spice that has traditionally been treated as medicine. So, ginger powder was used to assess its effect on dysmenorrhea among nursing students. Material & Methods: sample of 60 nursing students from selected nursing colleges, 30 each in experimental group and control group were selected by non-probability purposive sampling technique. Subjective and objective assessment of level of dysmenorrhea wer done by using modified mcgill pain questionnaire and standardized wong bakers faces pain rating scale respectively. Analysis was done by using both descriptive and inferential statistics. Findings: findings showed that according to subjective assessment in experimental group, 100% nursing students had mild level of pain, whereas in control group, 46.67% had mild level of pain, On the other hand, according to objective assessment in experimental group 66.7% had mild level of pain whereas in control group 56.7% had moderate pain. Results were found statistically significant at p < 0.01 level in experimental group on both subjective and objective assessment. Conclusion: study reveals that there was impact of ginger powder on dysmenorrhea among nursing students in experimental group.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".