EFFECT OF FOOT AND HAND MASSAGE ON RELIVING POST CESAREAN SECTION AFTER PAIN
Bibliographic record
Abstract
BACKGROUND: post cesarean section pain is the most complication affected mother. Hand and foot massages are very effective in decreasing pain and its consequence. The aim: of this study was to assess the effect of foot and hand massage on post cesarean section after pain. Methodology: Design: A quasi- experimental design was utilizing in this study. Setting: The study was conducted at obstetrics and gynecology hospitals (postnatal ward) of Al-Amery General Hospital Port-Said& Obstetrics and Gynecology Specialized Hospital Port-Said. Subjects: Mothers during early post cesarean section period during the second 6 hours (n=120) divided into two groups. "study group" and “control group”. Four tools were used for data collection: Structured interviewsheet for the woman’s it includes two parts: general characteristics, &obstetrical history of women as previous cesarean section. , Numerical rating scale. , Modified McGill pain & Likert Scale. Results. The result revealed that there was no significant difference between the two groups concerning their levels of pain and anxiety before the massage (P>0.05). However, the levels of pain significantly decreased in the intervention group, immediately, 6hand 8 hours after the intervention (P<0.001). Conclusion: According to our results, Hand and foot massage was effective in lowering the level of post-cesarean pain. nurses can relieve the post CS pain by applying available massage technique as it is simple & non-invasive Recommended Further researches are advised where replication of the current study on a larger post CS women size and different settings for the purpose of better generalization.
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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 | 0.002 |
| 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) | 0.005 | 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".