Effect of Dance Labor on the Management of Active Phase Labor Pain & Clients’ Satisfaction: A Randomized Controlled Trial Study
Bibliographic record
Abstract
BACKGROUND: There are a wide variety of non- pharmacologic pain relief techniques for labor which include pelvic movement, upright position, back massage and partner support during the first stage of labor. The effectiveness of dance labor- which is a combination of these techniques- has not been evaluated. AIM: This study aimed to evaluate the effectiveness of dance labor in pain reduction and woman's satisfaction during the first stage of labor. METHODS: 60 primiparous women aged 18-35 years old were randomly assigned to dance labor and control groups. In the dance labor group, women were instructed to do standing upright with pelvic tilt and rock their hips back and forth or around in a circle while their partner massaged their back and sacrum for a minimum of 30 minutes. In the control group, the participants received usual care during physiologic labor. Pain and satisfaction scores were measured by Visual Analogue Scale. Data were analyzed by using the t. test and Chi-square. FINDINGS: Mean pain score in the dance labor group was significantly lower than the control group (P < 0.05). The mean satisfaction score in the dance labor group was significantly higher than in the control group (P < 0.05). CONCLUSION: Dance labor which is a complementary treatment with low risk can reduce the intensity of pain and increase mothers, satisfaction with care during the active phase of labor.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".