Pain Intensity among Women with Post-Caesarean Section: A Descriptive Study
Bibliographic record
Abstract
Background: The frequency of Caesarean section increased from 5% to 15% across the world. According to statistics, it is highest in the U.S. or around 24%, and then in Canada about 20%, in Denmark about 13%, 10% in England, and it is lowest in Japan 7%. Post-cesarean section women experience pain due to operative trauma. Individual variability of postoperative pain is influenced by multiple factors, including sensitivity to pain, psychological factors, age, and genetics. Cesarean delivery patients have even more compelling reasons to achieve optimal postoperative pain relief than other surgical patients, but they also present unique challenges. Post cesarean delivery patients are at a higher risk for thromboembolic events, which may also be precipitated by immobility from inadequate pain control or excessive sedation from opioids. Objectives: This research aimed to describe pain intensity among women with post-cesarean Section. Methods: This research conducted at an obstetric ward in Hasan Sadikin Hospital, Bandung, West Java, Indonesia with 60 women with postcesarean section. Instrument used Visual Rating Scale (VAS) for pain measurement. A descriptive Study with Mean±SD for univariate analysis Result: Pain intensity among women with post-caesarian section were mild pain level with mean of pain level was 2.8. Women with mild pain level as much as 81,6%. Conclusions: As a nurse, can be considered as a nonpharmacological intervention to reduce the pain of cesarean section effectively and to decrease the number of medications and their side effects.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".