Effect of Foot and Hand Massage on Relieving Post-Cesarean Section Incisional Pain and Improving Patient' Satisfaction
Bibliographic record
Abstract
Back ground: Pain is one of the most common problems after cesarean section. Pain can interfere with awoman's ability to care for herself and her baby, so this study aimed to evaluate the effect of foot andhand massage for relieving post-Cesarean section incisional pain and improving patient’ satisfaction.Subjects and methods: Setting: This study was conducted in the department of obstetrics and gynecology(postnatal ward) of El-Manzala General Hospital at Aldakahlia Governorate. Design: Quasi-experimentalresearch design was used to achieve the aim of the current study. Sampling Type: Convenience sampling.Sample: The sample size was estimated to be 159 subjects divided into 3 groups (53 mothers for eachgroup). Tools: Four data collection tools, An interviewing structured questionnaire, Numerical ratingscale; Modified McGill pain questionnaire short form (SF-MPQ), and Likert Scale. Results: There wassignificant relieving of pain level among the three intervention groups (foot, hand, foot & hand massagegroups) at the third session of massage where (P <0.01). Foot and hand massage group had significantlylower pain scores than the other two groups (P <0.05). Conclusion: Foot and hand massage has a positiveeffect in reducing the mean score of pain level post-cesarean section. Also, the majority of patients postcesarean section were satisfied with the application of foot and hand massage for reliving post-cesareansection incisional pain. Recommendations: using of foot and hand massage to be one of the routine careto women post-cesarean Also, A distribution of a booklet about pain management post cesarean sectionand distributed among post-cesarean section mother.
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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) | 0.000 | 0.001 |
| 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".