The Effect Of Implementing A Bundle Of Evidence-Based Pain Management On Patient's Perception Of Pain And Clinical Outcomes
Bibliographic record
Abstract
Background: Evidence-based practice in pain management has been shown to provide improvements in patient outcomes. Aim: this study aimed to determine the effect of implementing a bundle of evidence-based pain management to improve patients' perception of pain and clinical outcomes. Research design: A quasi-experimental research design was used. Setting: the study was conducted on 3 surgical units (general, orthopedics and oncology surgery in Sayed Galal Hospital Sampling: a purposive sample of 70 adult patients was included in the study. Tools of data collection: four tools were used, first tool: a structured interviewing questionnaire. Second tool: Physiological parameters data. Third tool: Pain assessment by using The McGill Pain Questionnaire. Fourth tool: Groningen Sleep quality Scale. Result: the main results revealed that there was a statistically significant improvement in BP, HR, RR and Pso2 in the study group after intervention. There was statistically significant reducing total pain severity in the study group as well compared to control group with p-value = (0.01*). There was a highly statistically significant improvement of sleep quality at 3rd day postoperatively, with a p-value of 0.001* among study group. Conclusion: the implementation of a bundle of evidence-based pain management had a positive effect on reducing pain intensity, improvement of physiological parameters and sleep quality among the study group compared to control group. Recommendations: this study recommended that, reduplication this study in a large probability sample, using a bundle of evidence-based pain management in a different-surgical-wards. Set pain assessment scale as a part of a routine measurement like a vital-signs in surgical units
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".