Effectiveness of Nursing Post-discharge Guideline on Life Quality among Menopausal Women Undergoing Uterine Prolapse Surgery
Bibliographic record
Abstract
Background: The term "uterine surgery" refers to any surgery on a woman's reproductive system. The quality of life of menopausal women might be impacted by uterine prolapse, which can also increase maternal morbidity and mortality. The symptoms and severity of uterine prolapse determine the course of treatment. Aim of this Study: To assess the Effectiveness of Nursing Post-discharge Guideline on Life Quality among Menopausal Women Undergoing Uterine prolapse Surgery. Research Design: A quasi-experimental design was used. Research Setting: The study was carried out at gynecological outpatients' clinics affiliated with Ain Shams University hospitals. Sampling: 210 menopausal women who were scheduled for surgery and had uterine prolapse in the second, third, or fourth degree made up the purposive sample. The pre-test interviews were conducted in outpatient clinics, and the post-surgery interviews were conducted three months following discharge during the hospital's outpatient department follow-up. Tools: The study variables were measured using three different instruments. First tool: a systematic questionnaire for personal and health information. A questionnaire to assess knowledge of uterine prolapse surgery is the second tool. Tool (3). To assess the quality of life in the bio-psychosocial and environmental domains, the World Health Organization developed the WHOQOL-BREF. Results: Indicated a knowledge difference between the pre-and post-test that was statistically significant. Additionally, following the discharge guideline, there was a statistically significant association between certain elements of the physical quality of life domain. Conclusion: The nursing post-discharge approach led to improvements in many areas of life quality. Recommendations: For menopausal women undergoing uterine prolapse surgery, a continuous periodic nursing care routine should be administered.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".