High-fidelity simulation in anesthesiology training: a survey of Canadian anesthesiology residents' simulator experience La simulation haute-fidelitedans la formation en anesthesiologie: un sondage concernant l'experience des residents en anesthesiologie au Canada avec les simulateurs
Bibliographic record
Abstract
Purpose The objective of this survey was to explore Canadian anesthesiology residents’ educational experience with high-fidelity simulation and to improve understanding of the factors perceived to have either a positive or a negative effect on residents’ learning. Methods In 2008, all Canadian anesthesiology residents (n = 599) were invited to complete a ten-minute anonymous online survey. Survey questions were derived from two sources, a literature search of MEDLINE (1966 to present), EMBASE (1980 to present), and the Cochrane and Campbell collaboration libraries and the experience of 25 pilot residents and the lead author. Results The survey response rate was 27.9% (n = 167). Junior residents (PGY1–3) responded that it would be helpful to have an introductory simulation course dealing with common intraoperative emergencies. The introduction of multidisciplinary scenarios (where nurses and colleagues from different specialties were involved in scenarios) was strongly supported. With respect to gender, male anesthesia residents indicated their comfort in making mistakes and asking for help in the simulator more frequently than female residents. In accordance with the ten Best Evidence Medical Education (BEME) principles of successful simulator education, Canadian centres could improve residents’ opportunities for repetitive practice (with feedback), individualization of scenarios, and defined learning outcomes for scenarios. Discussion Anesthesiology residents indicate that simulation-based education is an anxiety provoking experience, but value its role in promoting safe practice and enhancing one’s ability to deal with emergency situations. Suggestions to improve simulation training include increasing residents’ access, adopting a more student-centred approach to learning, and creating a safer learning environment.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".