Pain Management and Its Possible Implementation Research in North Ethiopia: A before and after Study
Bibliographic record
Abstract
Background. Though there is an effective intervention, pain after surgical intervention is undermanaged worldwide. A systematic implementation is required to increase the utilization of available evidence-based intervention to manage the inevitable pain after surgery. The aim of this research project is to develop a scalable model for managing pain after cesarean section by implementing the World Health Organization’s (WHO) pain management guidelines through a combination of implementation research and quality improvement methods. Methods. We implemented the World Health Organization (WHO) pain management guidelines using effective implementation strategies. First, we conducted a formative qualitative exploration to identify enablers and obstacles. In addition, we took base-line assessment on pain management implementation process and outcome using a checklist prepared from the guideline and an adapted American Pain Outcome assessment tool version 2010, respectively. Then, we integrated the guidelines into the existing practice by using collaborative iterative learning strategy. We analyzed the data by Statistical Packages for Social Sciences (SPSS) version 21. We compared the before and after data using chi-squared and Fischer’s exact test. A change in any measurement was considered as significant at p value 0.05. Result. We collected data from 106 mothers before and 110 mothers after intervention implementation. We successfully integrated pain as a fifth vital sign in more than 87% ( p value <0.001) of patient, and fidelity was approximately 59% ( p value <0.001). In addition, we significantly improved pain outcome measures after the implementation of the intervention. Conclusion and Recommendations. A systematic approach to implement pain management guidelines was successful. We recommend the ward sustain these gains and that hospital, the region, and the nation to replicate the success.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".