Development of an EMS Curriculum
Bibliographic record
Abstract
Emergency medical services (EMS) became an American Board of Medical Specialties (ABMS) approved subspecialty of emergency medicine in September 2010. Achieving specialty or subspecialty recognition in an area of medical practice requires a unique body of knowledge, a scientific basis for the practice, a significant number of physicians who dedicate a portion of their practice to the area, and a sufficient number of fellowship programs. To prepare EMS fellows for successful completion of fellowship training, a lifetime of subspecialty practice, and certification examination, a formalized structured fellowship curriculum is necessary. A functional curriculum is one that takes the entire body of knowledge necessary to appropriately practice in the identified area and codifies it into a training blueprint to ensure that all of the items are covered over the prescribed training period. A curriculum can be as detailed as desired but typically all major headings and subheadings of the core content are identified and addressed. Common curricular components, specific to each area of the core content, include goals and objectives, implementation methods, evaluation, and outcomes assessment methods. Implementation methods can include simulation, observations, didactics, and experiential elements. Evaluation and outcomes assessment methods can include direct observation of patient assessment and treatment skills, structured patient simulations, 360° feedback, written and oral testing, and retrospective chart reviews. This paper describes a curriculum that is congruent with the current EMS core content, as well as providing a 12-month format to deploy the curriculum in an EMS fellowship program. Key words: curriculum; education; emergency medical services; fellowships and scholarships.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".