Effect of Applying Crushed Ice Gel Pads on Episiotomy Pain and Wound Healing Among Postpartum Primiparous Women
Bibliographic record
Abstract
Background: Episiotomy pain is the most common complaint among postpartum women. One of the simple nursing interventions to decrease it and improve the healing process is applying the crushed ice gel pads. The present study aimed to evaluate the effect of applying crushed ice gel pads on episiotomy pain and wound healing among postpartum primiparous women. A quasi-experimental study design was used. A total of 200 postnatal women (intervention and control group) were selected from the postpartum ward and outpatient clinic at El- Manail maternity Hospital, Egypt. Four Tools for data collection were used: “women structured interviewing sheet, Numeric Rating Scale, short-form McGill pain questionnaire, and the standardized REEDA Scale”. The Results of the study revealed that the mean age of the sample was 26.8± 5.8 years old. There was a statistically significant reduction in the level of episiotomy pain at first day, 3 days, and seven days postpartum between the two groups. Also, the study group REEDA score decreased to 2.72 ± 4.6 and 1.1 ± 3.8 during the posttest. Added to that, most of the intervention group (92.0%) had good episiotomy wound healing while more than half of the control group (57.0%) still has poor and mild healing. Conclusion: Women who applied the crushed ice gel pads at postpartum period experienced a lower level of episiotomy pain, had good episiotomy wound healing, and had better ability to perform daily living activities during the posttest than the control group. Recommendations: The nurses should be aware of the importance of crushed ice gel pads for postpartum primiparous women, and they should encourage them to apply the crushed ice gel pads at the hospital as well as home to improve their episiotomy wound healing and maintain comfort.
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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) | 0.002 | 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".