Pain experience and nursing approaches to pain control among patients undergone abdominal surgery at tertiary hospital
Bibliographic record
Abstract
Background: Pain is the most common problem found in postoperative patients. Even with improved and advanced surgical techniques, people still feel some pain and discomfort after surgery. Methods: The descriptive study was carried out to assess pain experience and nursing approaches to pain control among 111 post- operative patients at surgical ward of Manipal Teaching Hospital, Pokhara. The data was collected in the month of May to July, 2019. Wong Baker Pain rating scale and structured questionnaire were used to collect data. The collected data was analysed by using descriptive and inferential statistic. Results: Almost all the patients experienced pain after surgery, the most common site of pain was at surgical incision. Majority (90.1%) of them felt more pain while changing position. Conversely, 88.3% of patients reported least pain during rest. While 55% of them had reported moderate level of pain. Mean severity of pain experienced by patients was 4.8±2.4. Regarding nursing approaches, 82% of patients told that nurse had frequently asked whether they had pain, 80.2% of them reported that the nurse administered pain killer drugs when they required. However, none of them told non-pharmacological methods such as massage, listening music, imagination to distraction of pain were performed. The significant association was seen in level of pain with post-operative day. Conclusions: The findings of study indicate that the majority of patients experienced pain in the first day of surgery, and it is clear that effective pain management is essential in early day of operation.
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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".