State of emergency medicine in Rwanda 2015: an innovative trainee and trainer model
Bibliographic record
Abstract
The 1994 Rwandan war and genocide left more than 1 million people dead; millions displaced; and the country's economic, social, and health infrastructure destroyed. Despite remaining one of the poorest countries in the world, Rwanda has made remarkable gains in health, social, and economic development over the last 20 years, but modern emergency care has been slow to progress. Rwanda has recently established the Human Resources for Health program to rapidly build capacity in multiple sectors of its healthcare delivery system, including emergency medicine. This project involves multiple medical and surgical residencies, nursing programs, allied health professional trainings, and hospital administrative support. A real strength of the program is that trainers work with international faculty at Rwanda's referral hospital, but also as emergency medicine specialty trainers when returning to their respective district hospitals. Rwanda's first emergency medicine trainees are playing a unique and important role in the implementation of emergency care systems and education in the country's district hospitals. While there has been early vital progress in building emergency medicine's foundations in Rwanda, there remains much work to be done. This will be accomplished with careful planning and strong commitment from the country's healthcare and emergency medicine leaders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".