Abstract: Building a National Simulation Program in Rwanda Through the Use of Partnerships
Bibliographic record
Abstract
BackgroundBuilding a simulation program in a low-resource environment can be challenging. With the assistance of international partners, Rwanda has successfully built a sustainable national simulation program. We believe this may be a successful model for other low-resource countries.DescriptionIn 2003, a partnership was formed between Kigali Health Institute (KHI) and VVOB (the Flemish International Corporation in Belgium). Through this partnership former KHI, currently College of Medicine and Health Sciences of the University of Rwanda – ( UR-CMHS) was able to build an initial simulation program including an education plan, outfitting rooms, training standardized patients, purchasing and the setting up of equipment, and training simulation staff and faculty. This partnership was extended until 2010. From 2010 to 2012, a partnership was formed between the Kigali University Teaching Hospital (CHUK) and the Canadian Anesthesiologists Society International Education Foundation. Through this partnership a skills lab was set up at CHUK, for use by medical students, residents, and physicians at CHUK. From 2012-2015, a third simulation partnership was set up with the Human Resources for Health Program whereby an experienced simulation advisor was sent to CMHS for one year. A number of improvements have been accomplished through this partnership.Lessons LearnedThrough the use of deliberate, structured international partnerships, a successful and sustainable simulation program has been set up in Rwanda. We have learned that the partnerships should be designed in a way that ensures sustainable growth and improvement in the simulation program long after partnerships terminate.ConclusionInternational simulation program partnerships, if set up correctly, can assist in building sustainable simulation facilities in low-resource settings.Key words: simulation, clinical education, low resource countries, Rwanda, partnerships
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".