Postoperative pain assessment and management among nurses in selected hospitals in Benin City, Edo State, Nigeria
Bibliographic record
Abstract
ABSTRACT Objective: This study was designed to determine the nurse assessment of postoperative pain and its management in selected hospitals, Benin City, Edo State, Nigeria. Materials and Methods: A descriptive cross-sectional survey was adopted. The target population consist of 222 purposely nurses who are in the cadre of nursing officer II to chief nursing officer who works in the various surgical wards/units of the selected health facilities. The data were collected from the participants using the pretested structured questionnaire developed by the researcher. Results: Results showed that 66.2% of nurses had a poor level of knowledge on postoperative pain assessment. The McGill Pain Questionnaire was the most used pain assessment tool with a mean score of 2.84 whereas the Dallas Pain Questionnaire was the least used with a mean score of 1.90. “Providing clean, calm, and well-ventilated ward environment” (3.69 ± 0.61) was the most used nonpharmacological method for postoperative pain management, followed by “distraction, relaxation, and guided imagery” (3.52 ± 0.50), “dressing, bandage, splint, and reinforce wound sites postoperatively” (3.39 ± 0.54), and “early ambulation/exercise” (3.20 ± 0.62). The most used pharmacological interventions were “acetaminophen” (3.63 ± 0.55), “topical anesthetic” (2.92 ± 0.62), “nonselective nonsteroidal anti-inflammatory drugs” (2.87 ± 0.43), and “mixed opioid agonist–antagonist” (2.56 ± 0.56). Conclusion: There is a poor level of knowledge on postoperative pain assessment among nurses in this study setting. It is, therefore, pertinent for hospitals to organize continuous in-service training for postoperative pain assessment and management, especially on nonpharmacological approaches among nurses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".