Effectiveness of massage therapy in reducing labor pain compared with other alternative therapies among pregnant women: A systematic review
Bibliographic record
Abstract
Background: Childbirth is one of the most painful events that a woman experiences in her life, the pain is so severe that it exceeds chronic disease conditions. Even though, delivery is a natural process, the pain which the laboring women undergoes is considered severe in most of the cases. To alleviate labor pain, several alternative therapies are emerging and this study focuses on the effectiveness of massage therapy. Method: Systematic review method was used in this study. The main electronic databases used were DISCOVER, MEDLINE, SCIENCE DIRECT, CINAHL PLUS, Psych INFO, PubMed, Web of science, PubMed, EMBASE and the Cochrane Library for the systematic review. The first search produced 2,227 results. On rigorous analysis and based on inclusion and exclusion criterion 17 studies were found to be adequate. In addition, CASP tool was applied to check the validity and reliability of the studies. Finally, 8 studies were chosen for systematic review. Results: The main themes identified in this systemic review were majority of the pregnant women in the studies need pain relief during labor irrespective of caste, creed, race and is directly related/proportional to their satisfaction in childbirth experience. The socio-demographic and biologic variables which includes age, parity, race, ethnicity and psychological factors are directly related to variations in pain perception among pregnant women. The massage therapy is effective in reducing labor pain among pregnant women especially stressed and anxious women. Massage therapy compared with other complementary / alternative therapies such as acupressure, music therapy and aromatherapy are effective in reducing labor pain and massage therapy is very effective among depressed women.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".