Establishing a Self-sustaining Emergency Medicine Point-of-Care Ultrasound Curriculum in an Academic Teaching Hospital in Ethiopia
Bibliographic record
Abstract
Background: Point-of-care ultrasound (POCUS) training has become a standard component of Canadian emergency medicine (EM) residency programs. In resource-limited contexts, including Ethiopia, there is a critical shortage of local clinicians who can perform and teach POCUS. Our aim was to establish an introductory POCUS rotation within the EM residency program at Addis Ababa University (AAU) through The Toronto Addis Ababa Academic Collaboration in Emergency Medicine (TAAAC-EM). Methods: Through stakeholder engagement, the authors completed a quality improvement initiative and conducted a survey of AAU EM faculty and residents to understand which POCUS scans should be included in a core residency POCUS curriculum, "POCUS1". Results: 17 residents completed the POCUS1 program and 16 residents completed the written survey. Focused assessment with sonography for trauma, inferior vena cava, and lung (pneumothorax, pleural effusions, and interstitial syndrome) were identified as core introductory topics. Seventeen residents completed the initial POCUS1 program. Three program graduates were supported to become "POCUS1 Master Instructors" to continue the program during the SARS-CoV-2 global pandemic. Conclusion: The authors identified the highest yield POCUS scans through a written survey, successfully introduced a sustainable core POCUS curriculum at AAU for EM residents, and graduated three master instructors for curriculum continuation. We outline the structure and materials for implementation of POCUS programs for EM trainees and staff in similar low- and middle-income countries.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".