The Effect of Progressive Muscle Relaxation on Post Cesarean Section Pain, Quality of Sleep and Physical Activities Limitation
Bibliographic record
Abstract
Background: Pain, sleep disturbances, and physical activity limitation are the most tiresome complains of the women post caesarian section (Cs). Progressive muscle relaxation is a promising intervention for these complains. This study aimed to determine the effect of progressive muscle relaxation technique on post-cesarean section pain, quality of sleep and physical activities limitation. Research design: Randomized controlled clinical trial. Setting: post-partum unit at Damanhour National Medical Institute. Sample: A purposive sample of 80 women undergoing Cs was recruited. Randomization block was done to randomly assign 40 women for the study group and 40 for the control group. Tools: Four tools were used for data collection: structured interview schedule, short-form McGill Pain Questionnaire, Physical activities limitation Questionnaire and Groningen Sleep Quality Scale. Results: After the intervention, PMR significantly decreased pain severity among study group in Pain Rating Index scale, Visual analogue pain scale, and Present Pain Intensity scale compared to the control group. The severe physical activities limitation significantly absent from the entire study group, while it was significantly present among 70% of the control group. About two-thirds (62.5%) of the study group had a good quality of sleep compared to 5% of the control group. Conclusion: PMR significantly decreased pain, improved physical activities and quality of sleep among women after Cs. Recommendation: PMR should be incorporated in the nursing intervention protocols post-Cs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".