The effect of Aromatherapy Massage on pain levels in females suffering from Dysmenorrhea
Bibliographic record
Abstract
Background: \nPrevious studies reported a prevalence of dysmenorrhea in women age 18-25 of 84%. There are few published examples examining the effect of self-applied aromatherapy massage with clary sage and sweet marjoram essential oils on pain levels in participants suffering from dysmenorrhea. \nResearch Question: \nWhat is the effect of self-applied aromatherapy massage on pain intensity and quality of pain in females suffering from dysmenorrhea who manage their condition without medication? \nMethods: An experimental study with 4 participants primarily using quantitative data supported by qualitative. Participants applied oil with essential oil on their abdomen at baseline (period 1) and intervention (period 2). The short form of the McGill Pain questionnaire (SF-MPQ) including the Visual Analogue Scale (VAS) and the Present Pain Index (PPI) were used to investigate the quality and intensity of pain in participants at baseline and before/after intervention. A short questionnaire at follow up was used to gather further qualitative data. \nResults: \nThere was a general trend amongst the group in pain intensity findings towards a reduction in pain. General the pain showed a slight decline in heaviness, aching, sharpness and sickening character in the SF-MPQ scores. None of the participants experienced adverse effects from using the oil. \nConclusion: \nThis was a limited study, but findings suggest that the intervention could reduce period pain. However due to sample size and methodological limitations no conclusions about efficacy of aromatherapy massage can be made. Further research with a larger sample size and a control group is necessary to confirm findings of this study and eliminate bias.
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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 | 0.001 | 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) | 0.006 | 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".