Development and implementation of an emergency medicine graduate training program at Addis Ababa University School of Medicine: challenges and successes.
Bibliographic record
Abstract
BACKGROUND: Ethiopians experience high rates of acute illness and injury that have been sub-optimally addressed by the existing health care system. High rates of patient morbidity and mortality prompted the Federal Ministry of Health (FMOH) and the Addis Ababa University School of Medicine (AAU-SM) to prioritize the establishment of emergency medicine (EM) as a medical specialty in Ethiopia to meet this acute health system need. OBJECTIVES: To review the EM residency training program developed and implemented at AAU-SM in partnership with the University of Wisconsin (UW), the University of Toronto (UT) and University of Cape Town (UCT) and to evaluate the progress and challenges to date. METHODS: An EM Task Force (EMTF) at AAU-SM developed a context-specific three-year graduate EM curriculum with UW input. This curriculum has been co-implemented by faculty teachers from AAU-SM, UT and UW. The curriculum together with all documents (written, audio, video) are reviewed and used as a resource for this article. RESULTS: Seventeen residents are currently in full-time training. Five residents research projects are finalized and 100% of residents passed their year-end exams. CONCLUSION: A novel graduate EM training program has been successfully developed and implemented at AAU-SM. Interim results suggest that this curriculum and tri-institutional collaboration has been successful in addressing the emergency health needs of Ethiopians and bolstering the expertise of Ethiopian physicians. This program, at the forefront of EM education in Africa, may serve as an effective model for future EM training development throughout Africa.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".