The Development of a National Emergency Medical Services Curriculum Framework for Physicians in Canada
Bibliographic record
Abstract
BACKGROUND: As the role of emergency medical services (EMS) continues to expand, EMS physicians and medical directors require special skills and training to keep pace with the rapidly evolving subspecialty of EMS. In Canada, subspecialty training in EMS is still relatively new, and a standard national curriculum for physician EMS training does not exist. OBJECTIVE: To develop a national EMS curriculum for emergency medicine (EM) residents and fellows and an abbreviated curriculum for non-EM trainees and community physicians. METHODS: The authors obtained EMS curricula and opportunities from Canadian EM and EMS training programs and a sample of U.S. programs to determine existing curricula, and developed a framework for a national EMS curriculum using an expert working group of EMS medical directors and EMS leaders in Canada. RESULTS: Canadian EM residency training programs included an EMS rotation, but their content and depth of training were not uniform. The expert working group proposed a comprehensive set of training objectives, grouped into 16 categories, stratified by level of training. CONCLUSION: The proposed framework and objectives are suitable for training medical students, family medicine trainees, community physicians, EM residents, and EMS fellows in Canada. The authors hope this article will serve as a guideline for residency and fellowship directors to develop their EMS training programs in a consistent manner, promote formal training for physicians involved in EMS, and help define the specific knowledge and expertise required of physicians who provide EMS medical direction in Canada.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".