A collaboration to improve perioperative acute pain care at the University Teaching Hospital of Butare, Rwanda
Bibliographic record
Abstract
BACKGROUND: A perioperative acute pain care program integrating standardized assessment and treatment forms into pain care was developed and implemented at an urban hospital in Rwanda through a collaboration between Rwandan and Canadian experts. This study evaluated the perioperative acute pain care program using a quality improvement lens. METHODS: Using the Model for Improvement: Plan, Do, Study, Act (PDSA) cycle, a mixed methods evaluation was performed. Over one year, 519 randomized patient chart audits were conducted and analyzed through control charts. Through purposeful sampling, focus groups comprised ofsurgeons and nurses (N=34) involved in pain care in surgery, obstetrics, and anesthesiology were performed and analyzed via thematic coding. RESULTS: The average attempted form completion rate across all forms varied monthly between 56-93% (mean=79%; median=81%). Across all forms, both the mean and median total number of errors per form were 12.5. Enablers of form use included improved pain care for patients and feelings of professional satisfaction. Program implementation was challenged by resource constraints, form integration, and health care provider training. CONCLUSION: Future quality improvement collaborations should identify and address improved pain care while working with local experts to ensure PDSA cycles are continuous, and evidence based.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".