International Emergency Medical Teams Training Workshop Special Report
Bibliographic record
Abstract
The World Health Organization's (WHO; Geneva, Switzerland) Emergency Medical Team (EMT) Initiative created guidelines which define the basic procedures to be followed by personnel and teams, as well as the critical points to discuss before deploying a field hospital. However, to date, there is no formal standardized training program established for EMTs before deployment. Recognizing that the World Association of Disaster and Emergency Medicine (WADEM; Madison, Wisconsin USA) Congress brings together a diverse group of key stakeholders, a pre-Congress workshop was organized to seek out collective expertise and to identify key EMT training competencies for the future development of training programs and protocols. The future of EMT training should include standardization of curriculum and the recognition or accreditation of selected training programs. The outputs of this pre-WADEM Congress workshop provide an initial contribution to the EMT Training Working Group, as this group works on mapping training, competencies, and curriculum. Common EMT training themes that were identified as fundamental during the pre-Congress workshop include: the ability to adapt one's professional skills to low-resource settings; context-specific training, including the ability to serve the needs of the affected population in natural disasters; training together as a multi-disciplinary EMT prior to deployment; and the value of simulation in training. AlbinaA, ArcherL, BoivinM, CranmerH, JohnsonK, KrishnarajG, ManeshiA, OddyL, Redwood-CampbellL, RussellR. International Emergency Medical Teams training workshop special report. Prehosp Disaster Med. 2018;33(3):335-338.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.135 | 0.056 |
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".