Vital Anaesthesia Simulation Training (VAST); immersive simulation designed for diverse settings
Bibliographic record
Abstract
Simulation-based education (SBE) of health care providers is ubiquitous in well-resourced locations and has been used successfully to teach clinical and nontechnical skills. Numerous barriers prevent this important educational tool from widespread use in resource-limited and remote settings. Vital Anaesthesia Simulation Training (VAST) was founded with the aim of promoting the use of vivid, experiential simulation-based learning regardless of location. As an organization, VAST now offers a range of training opportunities. The 3-day VAST Course is a highly portable program that uses low-cost materials to teach core perioperative practices and non-technical skills for health care practitioners in diverse settings. The VAST Course is paired with the VAST Facilitator Course to build skills for SBE among local educators. The VAST Design Course equips simulation facilitators with tools for developing their own simulation scenarios. The VAST Foundation Year is a 48-week curriculum of active learning sessions for early anesthesia trainees, made available to VAST facilitators. This manuscript describes many of the challenges faced when delivering SBE in varied environments and indicates VAST’s strategies to help overcome potential barriers. An overview of the VAST scenario template and stepwise approach to scenario design is included (Supplemental Digital Content 1, http://links.lww.com/IJSGH/A14). While challenges facing SBE in resource-limited settings are significant, its potential value in this context is immense. VAST offers a viable platform for expanding SBE beyond the traditional simulation center.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.027 | 0.004 |
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".