Emergency Ultrasound Training Program in Guyana: Systematic Credentialing Process in a Resource‐limited Setting
Bibliographic record
Abstract
Ultrasound has become an important skill for emergency physicians. Ultrasound is more crucial in resource-limited settings where diagnostic testing may not be as timely or available at all. In 2015, an emergency medicine ultrasound curriculum was implemented at Georgetown Public Hospital Corporation in Georgetown, Guyana. Implementing an ultrasound-training curriculum in Guyana had four main challenges: limited ultrasound equipment, lack of informational technology infrastructure to record and review ultrasound examinations, availability of local emergency ultrasound expertise, and competing educational needs within the curriculum. These challenges were met with creative solutions and the formation of a formalized curriculum and credentialing process. The experience of creating the program is described along with the curriculum, credentialing process, and plan for sustainability. Since implementation, every graduating resident has displayed competency on final assessment.
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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 | 0.041 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".