Comparison the effects of aromatherapy with rose extract andlavender on the pain of the active phase of labor in primipara women
Bibliographic record
Abstract
Background and Aim: Pain is a common phenomenon of the labor process and use of non-pharmacological and complementary therapies to reduce labor pain has been on the rise. The aim of this study was to compare the effect of aromatherapy with rose extract and lavender on the pain in the active phase of labor in primipara women. Material and Methods: This clinical trial included 60 primiparous women referring to the maternity hospital of Amiral-Mochr('39')menin in 1395. The women were divided into three groups. Aroma therapy with lavender and rose extracts was started at cervical dilatations of 5 to 4 cm, and repeated every 15 minutes. We used distilled water for the control group. The severity of pain was measured at cervical dilatations of 4-5, 6-7, 8-10 cm. We used an individual and a midwifery questionnaire, a check list of examination and a McGill pain questionnaire. Using spss 21 software, data were analyzed by descriptive and analytical statistics (ANOVA with repeated observations, one-way ANOVA, covariance, chi-square, Kruskal-Wallis test). Results: There were no significant differences among the three groups in relation to the mean pain intensity before the intervention (P = 0.603). The severity of pain decreased significantly in the lavender and rose groups (P = 0.001) compared to that in the control group after the intervention (P = 0.001), and pain reduction in the lavender group was more than that in the rose group. Conclusion: Aromatherapy with lavender resulted in a more significant reduction in labor pain compared to that with rose essential oil.
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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) | 0.001 | 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".