Critical care ultrasound training: a survey exploring the “education gap” between potential and reality in Canada
Bibliographic record
Abstract
BACKGROUND: Critical care ultrasound (CCUS) is now a core competency for Canadian critical care medicine (CCM) physicians, but little is known about what education is delivered, how competence is assessed, and what challenges exist. We evaluated the Canadian CCUS education landscape and compared it against published recommendations. METHODS: A 23-item survey was developed and incorporated a literature review, national recommendations, and expert input. It was sent in the spring of 2019 to all 13 Canadian Adult CCM training programs via their respective program directors. Three months were allowed for data collection and descriptive statistics were compiled. RESULTS: Eleven of 13 (85%) programs responded, of which only 7/11 (64%) followed national recommendations. Curricula differed, as did how education was delivered: 8/11 (72%) used hands-on training; 7/11 (64%) used educational rounds; 5/11 (45%) used image interpretation sessions, and 5/11 (45%) used scan-based feedback. All 11 employed academic half-days, but only 7/11 (64%) used experience gained during clinical service. Only 2/11 (18%) delivered multiday courses, and 2/11 (18%) had mandatory ultrasound rotations. Most programs had only 1 or 2 local CCUS expert-champions, and only 4/11 (36%) assessed learner competency. Common barriers included educators receiving insufficient time and/or support. CONCLUSIONS: Our national survey is the first in Canada to explore CCUS education in critical care. It suggests that while CCUS education is rapidly developing, gaps persist. These include variation in curriculum and delivery, insufficient access to experts, and support for educators.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".